{"id":7467,"date":"2023-09-12T16:26:48","date_gmt":"2023-09-13T00:26:48","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=7467"},"modified":"2025-04-24T17:14:07","modified_gmt":"2025-04-24T17:14:07","slug":"10-open-source-machine-learning-libraries","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/10-open-source-machine-learning-libraries\/","title":{"rendered":"10 Open Source Machine Learning Libraries"},"content":{"rendered":"\n<div class=\"fh fi fj fk fl\">\n<div class=\"ab ca\">\n<div class=\"ch bg et eu ev ew\">\n<figure class=\"lx ly lz ma mb mc lu lv paragraph-image\">\n<div class=\"md me eb mf bg mg\" tabindex=\"0\" role=\"button\">\n<figure><img loading=\"lazy\" decoding=\"async\" class=\"bg mh mi c\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:700\/1*w-ZZqQxj1RT0LCEKgrb1yQ.png\" alt=\"Machine Learning Open Source\" width=\"700\" height=\"368\"><\/figure><div class=\"lu lv lw\"><picture><\/picture><\/div>\n<\/div>\n<\/figure>\n<p id=\"5c62\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">The open-source movement is responsible for most of the technological innovation we see today, and machine learning is no exception. This movement has birthed many new libraries, fueled projects, enabled rapid growth, and increased the <a class=\"af ng\" href=\"https:\/\/medium.com\/p\/2bd64277ed5a\" rel=\"noopener\">reproducibility of experimental results<\/a> and innovative applications. In addition, these libraries have made it more feasible to design large-scale real-world systems and adopt models.<\/p>\n<p id=\"6796\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Let\u2019s get started and see what we\u2019ve got on our hands! Because there are a lot of open source machine learning libraries out there, we are only going to look at a few. Below is a list of various machine learning libraries and how they\u2019ve changed the machine learning landscape.<\/p>\n<ul class=\"\">\n<li id=\"05e6\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Numpy<\/li>\n<li id=\"e00e\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Pandas<\/li>\n<li id=\"c8ca\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">SciPy<\/li>\n<li id=\"5d02\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Scikit-learn<\/li>\n<li id=\"711a\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">TensorFlow<\/li>\n<li id=\"1181\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Keras<\/li>\n<li id=\"4039\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">PyTorch<\/li>\n<li id=\"ad5d\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Natural Language Toolkit (NLTK)<\/li>\n<li id=\"b0f5\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">DanfoJS<\/li>\n<li id=\"51f5\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Microsoft Cognitive Toolkit (CNTK)<\/li>\n<\/ul>\n<h2 id=\"2d2d\" class=\"ns nt fo be nu nv nw nx ny nz oa ob oc mt od oe of mx og oh oi nb oj ok ol om bj\" data-selectable-paragraph=\"\">1. Numpy<\/h2>\n<figure class=\"oo op oq or os mc lu lv paragraph-image\">\n<figure><img loading=\"lazy\" decoding=\"async\" class=\"bg mh mi c\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:335\/1*QPp7NZVG4RJoCWFHz8GJHQ.png\" alt=\"\" width=\"335\" height=\"150\"><\/figure><div class=\"lu lv on\"><picture><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/format:webp\/1*QPp7NZVG4RJoCWFHz8GJHQ.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/format:webp\/1*QPp7NZVG4RJoCWFHz8GJHQ.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/format:webp\/1*QPp7NZVG4RJoCWFHz8GJHQ.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/format:webp\/1*QPp7NZVG4RJoCWFHz8GJHQ.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/format:webp\/1*QPp7NZVG4RJoCWFHz8GJHQ.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/format:webp\/1*QPp7NZVG4RJoCWFHz8GJHQ.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:670\/format:webp\/1*QPp7NZVG4RJoCWFHz8GJHQ.png 670w\" type=\"image\/webp\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 335px\"><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/1*QPp7NZVG4RJoCWFHz8GJHQ.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/1*QPp7NZVG4RJoCWFHz8GJHQ.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/1*QPp7NZVG4RJoCWFHz8GJHQ.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/1*QPp7NZVG4RJoCWFHz8GJHQ.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/1*QPp7NZVG4RJoCWFHz8GJHQ.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/1*QPp7NZVG4RJoCWFHz8GJHQ.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:670\/1*QPp7NZVG4RJoCWFHz8GJHQ.png 670w\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 335px\" data-testid=\"og\"><\/picture><\/div>\n<\/figure>\n<p id=\"1375\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Numpy is one of the first libraries you\u2019ll encounter when getting started with machine learning. Numpy, also known as numerical Python, was first developed by <a class=\"af ng\" href=\"https:\/\/en.wikipedia.org\/wiki\/Travis_Oliphant\" target=\"_blank\" rel=\"noopener ugc nofollow\">Travis Oliphant<\/a>. This library is the go-to Python library for scientific computation, handling multi-dimensional array and matrix operations.<\/p>\n<p id=\"d5b7\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">NumPy uses a special array type and brings the computational power of languages like C and Fortran to Python. Thus, it can perform various calculations like linear algebra, Fourier transform, and random number capabilities in milliseconds.<\/p>\n<p id=\"a7a1\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">It also lies at the core and forms the basis of many data science libraries. So, for example, we have visualization libraries like Matplotlib, Seaborn, Plotly, Altair, and Bokeh; machine learning libraries like Scikit-learn and SciPy; and array libraries like Dash, PyTorch, TensorFlow, MXNet, and even Pandas.<\/p>\n<p id=\"06ee\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Strengths:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"0de6\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Numpy arrays take less compact storage and memory space, so they have better runtime speeds when compared with traditional Python arrays.<\/li>\n<li id=\"8b10\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Its integration with C, C++, and Fortran code.<\/li>\n<li id=\"0e8f\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It supports vectorized operations.<\/li>\n<li id=\"bdba\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Its ability to handle Fourier transforms like linear algebra and random numbers.<\/li>\n<li id=\"107b\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Community support, especially since it is the foundation of multiple libraries.<\/li>\n<\/ul>\n<p id=\"5b22\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Weaknesses:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"2bdb\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It requires a contiguous allocation of memory.<\/li>\n<\/ul>\n<figure class=\"oo op oq or os mc\"><\/figure>\n<pre>import numpy as np\n\n#creating an array wiith np.array\nnew_matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n\nprint(new_matrix)<\/pre>\n<p id=\"2240\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Visit <a class=\"af ng\" href=\"https:\/\/numpy.org\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Numpy.org<\/a> to learn more about this library and submit an <a class=\"af ng\" href=\"https:\/\/numpy.org\/doc\/stable\/dev\/index.html\" target=\"_blank\" rel=\"noopener ugc nofollow\">open-source contribution<\/a> today! I contribute to this project so you can reach out to me if you need help.<\/p>\n<h2 id=\"e14d\" class=\"ns nt fo be nu nv nw nx ny nz oa ob oc mt od oe of mx og oh oi nb oj ok ol om bj\" data-selectable-paragraph=\"\">2. Pandas<\/h2>\n<figure class=\"oo op oq or os mc lu lv paragraph-image\">\n<figure><img loading=\"lazy\" decoding=\"async\" class=\"bg mh mi c\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:353\/1*u57hdEsvpdVBkBMyUr3xfg.png\" alt=\"\" width=\"353\" height=\"143\"><\/figure><div class=\"lu lv ox\"><picture><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/format:webp\/1*u57hdEsvpdVBkBMyUr3xfg.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/format:webp\/1*u57hdEsvpdVBkBMyUr3xfg.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/format:webp\/1*u57hdEsvpdVBkBMyUr3xfg.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/format:webp\/1*u57hdEsvpdVBkBMyUr3xfg.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/format:webp\/1*u57hdEsvpdVBkBMyUr3xfg.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/format:webp\/1*u57hdEsvpdVBkBMyUr3xfg.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:706\/format:webp\/1*u57hdEsvpdVBkBMyUr3xfg.png 706w\" type=\"image\/webp\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 353px\"><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/1*u57hdEsvpdVBkBMyUr3xfg.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/1*u57hdEsvpdVBkBMyUr3xfg.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/1*u57hdEsvpdVBkBMyUr3xfg.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/1*u57hdEsvpdVBkBMyUr3xfg.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/1*u57hdEsvpdVBkBMyUr3xfg.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/1*u57hdEsvpdVBkBMyUr3xfg.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:706\/1*u57hdEsvpdVBkBMyUr3xfg.png 706w\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 353px\" data-testid=\"og\"><\/picture><\/div>\n<\/figure>\n<p id=\"efe6\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">You can\u2019t say you haven\u2019t heard of Pandas as a data scientist. Pandas is a fast and flexible data analysis and manipulation library developed by <a class=\"af ng\" href=\"https:\/\/wesmckinney.com\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Wes McKinney<\/a>. Pandas isn\u2019t technically a machine learning library, unlike the other libraries on this list. However, it is needed for handling tabular data, data transformation, and performing <a class=\"af ng\" href=\"https:\/\/heartbeat.comet.ml\/exploratory-data-analysis-eda-for-categorical-data-870b37a79b65\" target=\"_blank\" rel=\"noopener ugc nofollow\">EDA (Exploratory Data Analysis)<\/a>.<\/p>\n<p id=\"eb6a\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">This library leverages high-level data structures (Series and DataFrame) and has multiple in-built data cleaning and analysis methods. Panda methods cut down complex Python calculations into a few lines of code. You can pretty much call it the Microsoft Excel of Python. Besides data manipulation, Pandas is very handy for data visualization.<\/p>\n<p id=\"23b6\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">To get a feel for Pandas in action, check out <a class=\"af ng\" href=\"https:\/\/heartbeat.comet.ml\/feature-engineering-for-categorical-data-897e98caea35\" target=\"_blank\" rel=\"noopener ugc nofollow\">feature engineering for categorical data<\/a>, <a class=\"af ng\" href=\"https:\/\/heartbeat.comet.ml\/building-an-article-recommendation-system-using-python-fd26aba85b9c\" target=\"_blank\" rel=\"noopener ugc nofollow\">build an article recommendation system using Python<\/a>, and <a class=\"af ng\" href=\"https:\/\/deepnote.com\/@bennykillua\/Data-Analytics-with-Pandas-a566cac0-5259-4928-a6de-15d7e4b956fe\" target=\"_blank\" rel=\"noopener ugc nofollow\">data analytics with Pandas \ud83d\udc3c<\/a>.<\/p>\n<p id=\"c966\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Strengths:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"e1dd\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Efficient handling of huge data sets for EDA.<\/li>\n<li id=\"2643\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Its code and data structures are simple, fast, and flexible.<\/li>\n<li id=\"88a1\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It integrates easily with other libraries in the machine learning ecosystem.<\/li>\n<li id=\"a618\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It has an extensive set of inbuilt commands.<\/li>\n<\/ul>\n<p id=\"cc7c\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Weaknesses:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"09af\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Poor 3D matrix compatibility.<\/li>\n<\/ul>\n<figure class=\"oo op oq or os mc\"><\/figure>\n<pre>import pandas as pd\n\ndf = pd.DataFrame({\"A\":[1, 10, 100, 1000, 10000],\n                   \"B\":[2, 20, 200, 2000, 20000],\n                   \"C\":[3, 30, 300, 3000, 30000],\n                   \"D\":[4, 40, 400, 4000, 40000]})\n\n#return the mean absolute deviation of the values for the requested axis\ndf.mad(axis = 0)<\/pre>\n<p id=\"2779\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Visit <a class=\"af ng\" href=\"https:\/\/pandas.pydata.org\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Pandas.pydata.org<\/a> to learn more about this library and submit an <a class=\"af ng\" href=\"https:\/\/pandas.pydata.org\/docs\/development\/contributing.html\" target=\"_blank\" rel=\"noopener ugc nofollow\">open-source contribution<\/a> today!<\/p>\n<h2 id=\"4cfa\" class=\"ns nt fo be nu nv nw nx ny nz oa ob oc mt od oe of mx og oh oi nb oj ok ol om bj\" data-selectable-paragraph=\"\">3. SciPy<\/h2>\n<figure class=\"oo op oq or os mc lu lv paragraph-image\">\n<figure><img loading=\"lazy\" decoding=\"async\" class=\"bg mh mi c\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:356\/1*FHNaBnCZBRuNemskefq6ZQ.png\" alt=\"\" width=\"356\" height=\"141\"><\/figure><div class=\"lu lv oy\"><picture><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/format:webp\/1*FHNaBnCZBRuNemskefq6ZQ.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/format:webp\/1*FHNaBnCZBRuNemskefq6ZQ.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/format:webp\/1*FHNaBnCZBRuNemskefq6ZQ.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/format:webp\/1*FHNaBnCZBRuNemskefq6ZQ.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/format:webp\/1*FHNaBnCZBRuNemskefq6ZQ.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/format:webp\/1*FHNaBnCZBRuNemskefq6ZQ.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:712\/format:webp\/1*FHNaBnCZBRuNemskefq6ZQ.png 712w\" type=\"image\/webp\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 356px\"><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/1*FHNaBnCZBRuNemskefq6ZQ.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/1*FHNaBnCZBRuNemskefq6ZQ.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/1*FHNaBnCZBRuNemskefq6ZQ.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/1*FHNaBnCZBRuNemskefq6ZQ.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/1*FHNaBnCZBRuNemskefq6ZQ.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/1*FHNaBnCZBRuNemskefq6ZQ.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:712\/1*FHNaBnCZBRuNemskefq6ZQ.png 712w\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 356px\" data-testid=\"og\"><\/picture><\/div>\n<\/figure>\n<p id=\"1419\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Scientific Python, also known as SciPy, is used to perform scientific analysis and technical computing on large data sets. SciPy was developed by <a class=\"af ng\" href=\"https:\/\/en.wikipedia.org\/wiki\/Travis_Oliphant\" target=\"_blank\" rel=\"noopener ugc nofollow\">Travis Oliphant<\/a>, <a class=\"af ng\" href=\"https:\/\/github.com\/pearu\" target=\"_blank\" rel=\"noopener ugc nofollow\">Pearu Peterson<\/a>, and <a class=\"af ng\" href=\"https:\/\/www.enthought.com\/team\/eric-jones\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Eric Jones<\/a>. Array optimization, linear algebra, integration, interpolation, special functions, ODE solvers, FFT, and signal and image processing are among the modules in this Numpy-based library.<\/p>\n<p id=\"fe45\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Strengths:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"79e4\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It\u2019s easy to use and fast to execute.<\/li>\n<li id=\"b456\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Complex numerical operations<\/li>\n<li id=\"cd55\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It has high-level commands with extensive functionality.<\/li>\n<\/ul>\n<p id=\"9405\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Weaknesses:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"89f2\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Despite being built on NumPy, Scipy has a slower computational speed.<\/li>\n<\/ul>\n<figure class=\"oo op oq or os mc\"><\/figure>\n<pre>from scipy import special\n\n#exponential Function\nx = special.exp10(1)\n\nprint(x)<\/pre>\n<p id=\"1d7c\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Visit <a class=\"af ng\" href=\"https:\/\/scipy.org\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Scipy.org<\/a> to learn more about this library and submit an <a class=\"af ng\" href=\"https:\/\/scipy.org\/contribute\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">open-source contribution<\/a> today!<\/p>\n<h2 id=\"d2fc\" class=\"ns nt fo be nu nv nw nx ny nz oa ob oc mt od oe of mx og oh oi nb oj ok ol om bj\" data-selectable-paragraph=\"\">4. Scikit-learn<\/h2>\n<figure class=\"oo op oq or os mc lu lv paragraph-image\">\n<figure><img loading=\"lazy\" decoding=\"async\" class=\"bg mh mi c\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:306\/1*xr_Cx3vJJBiu2otS3z7Olg.png\" alt=\"\" width=\"306\" height=\"165\"><\/figure><div class=\"lu lv oz\"><picture><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/format:webp\/1*xr_Cx3vJJBiu2otS3z7Olg.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/format:webp\/1*xr_Cx3vJJBiu2otS3z7Olg.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/format:webp\/1*xr_Cx3vJJBiu2otS3z7Olg.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/format:webp\/1*xr_Cx3vJJBiu2otS3z7Olg.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/format:webp\/1*xr_Cx3vJJBiu2otS3z7Olg.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/format:webp\/1*xr_Cx3vJJBiu2otS3z7Olg.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:612\/format:webp\/1*xr_Cx3vJJBiu2otS3z7Olg.png 612w\" type=\"image\/webp\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 306px\"><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/1*xr_Cx3vJJBiu2otS3z7Olg.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/1*xr_Cx3vJJBiu2otS3z7Olg.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/1*xr_Cx3vJJBiu2otS3z7Olg.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/1*xr_Cx3vJJBiu2otS3z7Olg.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/1*xr_Cx3vJJBiu2otS3z7Olg.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/1*xr_Cx3vJJBiu2otS3z7Olg.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:612\/1*xr_Cx3vJJBiu2otS3z7Olg.png 612w\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 306px\" data-testid=\"og\"><\/picture><\/div>\n<\/figure>\n<p id=\"1f90\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Scikit-learn is home to many machine learning algorithms, model selection, and preprocessing features. Scikit-learn was written in C and Python and built on top of NumPy and SciPy. It was developed by <a class=\"af ng\" href=\"https:\/\/en.wikipedia.org\/wiki\/David_Cournapeau\" target=\"_blank\" rel=\"noopener ugc nofollow\">David Cournapeau<\/a> as a <a class=\"af ng\" href=\"https:\/\/summerofcode.withgoogle.com\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Google Summer of Code<\/a> project. It is presently one of the most widely used machine learning libraries for developing machine learning algorithms.<\/p>\n<p id=\"9436\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">The library is simple, robust, intuitive, and user-friendly. The library is straightforward, dependable, intuitive, and user-friendly. It\u2019s also a useful library for building machine learning models, evaluating models, data modeling, and statistical modeling. This library can also vectorize text using BOW and hashing vectorization, among other things.<\/p>\n<p id=\"4e51\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Check out <a class=\"af ng\" href=\"https:\/\/heartbeat.comet.ml\/fake-news-detection-with-python-d7339cf1f018\" target=\"_blank\" rel=\"noopener ugc nofollow\">fake news detection with Python<\/a>, <a class=\"af ng\" href=\"https:\/\/heartbeat.comet.ml\/the-best-way-of-splitting-data-for-machine-learning-54c2f44cf409\" target=\"_blank\" rel=\"noopener ugc nofollow\">the best ways of splitting data for machine learning<\/a>, <a class=\"af ng\" href=\"https:\/\/heartbeat.comet.ml\/how-do-i-detect-anomalies-and-why-is-it-necessary-c570eef39622\" target=\"_blank\" rel=\"noopener ugc nofollow\">how do I detect anomalies and why is it necessary?<\/a> to get a sense of how Scikit-learn works in practice.<\/p>\n<p id=\"82c1\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Strengths:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"fe95\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It\u2019s a powerful model-building tool that\u2019s relatively simple to use.<\/li>\n<li id=\"cdc3\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It is also highly adaptable and valuable for a variety of real-world situations.<\/li>\n<li id=\"72fa\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Detailed API documentation.<\/li>\n<\/ul>\n<p id=\"c0eb\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Weaknesses:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"8be0\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It doesn\u2019t support distributed computing for large-scale production environment applications very well.<\/li>\n<li id=\"a651\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It works only with numeric data and will require you to encode categorical data.<\/li>\n<\/ul>\n<figure class=\"oo op oq or os mc\"><\/figure>\n<pre>from sklearn import cluster, datasets\n\n# load data\niris = datasets.load_iris()\n\n# K-means clustering: create clusters for k=3\nk=3\nk_means = cluster.KMeans(k)\n\n# fit data\nk_means.fit(iris.data)\n\n# print results\nprint( k_means.labels_[::10])\nprint( iris.target[::10])<\/pre>\n<p id=\"d4c4\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Visit <a class=\"af ng\" href=\"https:\/\/scikit-learn.org\/stable\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Scikit-learn.org<\/a> to learn more about this library and submit an <a class=\"af ng\" href=\"https:\/\/scikit-learn.org\/stable\/developers\/contributing.html\" target=\"_blank\" rel=\"noopener ugc nofollow\">open-source contribution<\/a> today!<\/p>\n<h2 id=\"46cf\" class=\"ns nt fo be nu nv nw nx ny nz oa ob oc mt od oe of mx og oh oi nb oj ok ol om bj\" data-selectable-paragraph=\"\">5. TensorFlow<\/h2>\n<figure class=\"oo op oq or os mc lu lv paragraph-image\">\n<figure><img loading=\"lazy\" decoding=\"async\" class=\"bg mh mi c\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:388\/1*2BIwQlGCI76qo-72wuj3Yg.png\" alt=\"\" width=\"388\" height=\"130\"><\/figure><div class=\"lu lv pa\"><picture><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/format:webp\/1*2BIwQlGCI76qo-72wuj3Yg.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/format:webp\/1*2BIwQlGCI76qo-72wuj3Yg.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/format:webp\/1*2BIwQlGCI76qo-72wuj3Yg.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/format:webp\/1*2BIwQlGCI76qo-72wuj3Yg.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/format:webp\/1*2BIwQlGCI76qo-72wuj3Yg.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/format:webp\/1*2BIwQlGCI76qo-72wuj3Yg.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:776\/format:webp\/1*2BIwQlGCI76qo-72wuj3Yg.png 776w\" type=\"image\/webp\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 388px\"><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/1*2BIwQlGCI76qo-72wuj3Yg.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/1*2BIwQlGCI76qo-72wuj3Yg.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/1*2BIwQlGCI76qo-72wuj3Yg.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/1*2BIwQlGCI76qo-72wuj3Yg.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/1*2BIwQlGCI76qo-72wuj3Yg.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/1*2BIwQlGCI76qo-72wuj3Yg.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:776\/1*2BIwQlGCI76qo-72wuj3Yg.png 776w\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 388px\" data-testid=\"og\"><\/picture><\/div>\n<\/figure>\n<p id=\"4108\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Tensor is a library for building, training, and running deep learning models and neural networks. This library was developed by the <a class=\"af ng\" href=\"https:\/\/research.google\/teams\/brain\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Google Brain team<\/a>. Its architecture and framework are also flexible; hence it can run across various computational platforms such as CPU, GPU, and TPU.<\/p>\n<p id=\"07ed\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">TensorFlow also offers a web-based visualization tool called Tensorboard, as well as frameworks like TensorFlow Lite and TensorFlow that make it easy to deploy machine learning models. You can visualize model parameters, gradients, and performance with Tensorboard.<\/p>\n<p id=\"c2fd\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Read <a class=\"af ng\" href=\"https:\/\/heartbeat.comet.ml\/dropout-regularization-with-tensorflow-keras-c484b7459cd6\" target=\"_blank\" rel=\"noopener ugc nofollow\">Dropout Regularization with Tensorflow Keras<\/a> to see Tensorflow in action.<\/p>\n<p id=\"d48b\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Strengths:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"d409\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Its TPU architecture allows it to outperform GPU and CPU in terms of computation speed.<\/li>\n<li id=\"5476\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It has top-notch computational graph visualization support.<\/li>\n<li id=\"99b5\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It is compatible with Keras and various languages, such as C++, JavaScript, Python, C#, Ruby, and Swift.<\/li>\n<\/ul>\n<p id=\"dabc\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Weaknesses:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"bf58\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Its TPU architecture only allows a model to be executed, not trained.<\/li>\n<li id=\"dcd4\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It has only NVIDIA and Python support for GPU.<\/li>\n<\/ul>\n<figure class=\"oo op oq or os mc\"><\/figure>\n<pre>import tensorflow as tf\n\n# Create a Tensor.\nhello = tf.constant(\"hello world\")\nprint(hello)<\/pre>\n<p id=\"5e08\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Visit <a class=\"af ng\" href=\"https:\/\/www.tensorflow.org\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Tensorflow.org<\/a> to learn more about this library and submit an <a class=\"af ng\" href=\"https:\/\/www.tensorflow.org\/community\/contribute\" target=\"_blank\" rel=\"noopener ugc nofollow\">open-source contribution<\/a> today!<\/p>\n<h2 id=\"ab91\" class=\"ns nt fo be nu nv nw nx ny nz oa ob oc mt od oe of mx og oh oi nb oj ok ol om bj\" data-selectable-paragraph=\"\">6. Keras<\/h2>\n<figure class=\"oo op oq or os mc lu lv paragraph-image\">\n<figure><img loading=\"lazy\" decoding=\"async\" class=\"bg mh mi c\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:417\/1*IXIjViV_5K0pa1374e9FKw.png\" alt=\"\" width=\"417\" height=\"121\"><\/figure><div class=\"lu lv pb\"><picture><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/format:webp\/1*IXIjViV_5K0pa1374e9FKw.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/format:webp\/1*IXIjViV_5K0pa1374e9FKw.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/format:webp\/1*IXIjViV_5K0pa1374e9FKw.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/format:webp\/1*IXIjViV_5K0pa1374e9FKw.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/format:webp\/1*IXIjViV_5K0pa1374e9FKw.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/format:webp\/1*IXIjViV_5K0pa1374e9FKw.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:834\/format:webp\/1*IXIjViV_5K0pa1374e9FKw.png 834w\" type=\"image\/webp\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 417px\"><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/1*IXIjViV_5K0pa1374e9FKw.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/1*IXIjViV_5K0pa1374e9FKw.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/1*IXIjViV_5K0pa1374e9FKw.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/1*IXIjViV_5K0pa1374e9FKw.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/1*IXIjViV_5K0pa1374e9FKw.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/1*IXIjViV_5K0pa1374e9FKw.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:834\/1*IXIjViV_5K0pa1374e9FKw.png 834w\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 417px\" data-testid=\"og\"><\/picture><\/div>\n<\/figure>\n<p id=\"3f78\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Keras, often called Python deep learning library, is used for developing and evaluating neural networks within deep learning and machine learning models. Keras was built by <a class=\"af ng\" href=\"https:\/\/fchollet.com\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Fran\u00e7ois Chollet<\/a>. It supports multiple backend support: Tensorflow, Theano, and CNTK. Because of this, training neural networks is easier and can be done with fewer codes and configurations.<\/p>\n<p id=\"fc87\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Keras has inbuilt features like cov2d, max-pooling layers, and data processing libraries. It also comes with a variety of pre-trained models and image classification models.<\/p>\n<p id=\"627a\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Interested in how Keras works? Check out <a class=\"af ng\" href=\"https:\/\/deepnote.com\/workspace\/bennykillua-60d8eb7f-17cc-49cb-8ade-2fadf13254f0\/project\/Trumps-Twitter-insults-4b4f29cc-5c46-4e6d-9e38-61104ff4d0b5\/%2Fexploring-trump-s-tweets-over-the-years.ipynb\" target=\"_blank\" rel=\"noopener ugc nofollow\">Trump\u2019s Twitter insults<\/a> and <a class=\"af ng\" href=\"https:\/\/heartbeat.comet.ml\/build-your-first-convolutional-neural-network-to-classify-cats-and-dogs-a90f46cf3737\" target=\"_blank\" rel=\"noopener ugc nofollow\">build your first convolutional neural network to classify cats and dogs<\/a>.<\/p>\n<p id=\"2de3\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Strengths:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"a269\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Keras simplifies the development of standard deep learning models.<\/li>\n<li id=\"58eb\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It works seamlessly with various deep learning frameworks and supports different backends like Tensorflow, Theano, and CNTK.<\/li>\n<\/ul>\n<p id=\"3d49\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Weaknesses:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"d51a\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">High resource requirements.<\/li>\n<li id=\"fe8f\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Because it cannot perform low-level computations, it is frequently used with TensorFlow, Theano, and Microsoft CNTK.<\/li>\n<li id=\"945e\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Keras operates at a high level of abstraction; users cannot fully implement their own core algorithm.<\/li>\n<\/ul>\n<figure class=\"oo op oq or os mc\"><\/figure>\n<pre>import tensorflow as tf\nfrom tensorflow import keras\n\n#load dataset\nmnist = tf.keras.datasets.mnist\n\n#Build a machine learning model\nmodel = tf.keras.models.Sequential([\n  tf.keras.layers.Flatten(input_shape=(28, 28)),\n  tf.keras.layers.Dense(100, activation='relu'),\n  tf.keras.layers.Dropout(0.5),\n  tf.keras.layers.Dense(10)\n])<\/pre>\n<p id=\"4683\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Visit <a class=\"af ng\" href=\"https:\/\/keras.io\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Keras.io<\/a> to learn more about this library and submit an <a class=\"af ng\" href=\"https:\/\/keras.io\/contributing\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">open-source contribution<\/a> today!<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"fh fi fj fk fl\">\n<div class=\"ab ca\">\n<div class=\"ch bg et eu ev ew\">\n<blockquote class=\"pk\"><p id=\"b1a9\" class=\"pl pm fo be pn po pp pq pr ps pt nf dv\" data-selectable-paragraph=\"\">Big teams rely on big ideas. <a class=\"af ng\" href=\"https:\/\/info.comet.ml\/roundtable-developing-ml-at-enterprise-scale\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Learn how experts at Uber, WorkFusion, and The RealReal use Comet<\/a> to scale out their ML models and ensure visibility and collaboration company-wide.<\/p><\/blockquote>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"fh fi fj fk fl\">\n<div class=\"ab ca\">\n<div class=\"ch bg et eu ev ew\">\n<h2 id=\"3034\" class=\"ns nt fo be nu nv nw nx ny nz oa ob oc mt od oe of mx og oh oi nb oj ok ol om bj\" data-selectable-paragraph=\"\">7. PyTorch<\/h2>\n<figure class=\"oo op oq or os mc lu lv paragraph-image\">\n<figure><img loading=\"lazy\" decoding=\"async\" class=\"bg mh mi c\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:225\/1*CJ_HYhtX_6L0MgjmqWN54Q.png\" alt=\"\" width=\"225\" height=\"225\"><\/figure><div class=\"lu lv pu\"><picture><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/format:webp\/1*CJ_HYhtX_6L0MgjmqWN54Q.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/format:webp\/1*CJ_HYhtX_6L0MgjmqWN54Q.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/format:webp\/1*CJ_HYhtX_6L0MgjmqWN54Q.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/format:webp\/1*CJ_HYhtX_6L0MgjmqWN54Q.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/format:webp\/1*CJ_HYhtX_6L0MgjmqWN54Q.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/format:webp\/1*CJ_HYhtX_6L0MgjmqWN54Q.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:450\/format:webp\/1*CJ_HYhtX_6L0MgjmqWN54Q.png 450w\" type=\"image\/webp\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 225px\"><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/1*CJ_HYhtX_6L0MgjmqWN54Q.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/1*CJ_HYhtX_6L0MgjmqWN54Q.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/1*CJ_HYhtX_6L0MgjmqWN54Q.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/1*CJ_HYhtX_6L0MgjmqWN54Q.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/1*CJ_HYhtX_6L0MgjmqWN54Q.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/1*CJ_HYhtX_6L0MgjmqWN54Q.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:450\/1*CJ_HYhtX_6L0MgjmqWN54Q.png 450w\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 225px\" data-testid=\"og\"><\/picture><\/div>\n<\/figure>\n<p id=\"be43\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">PyTorch is a library that is used for deep learning. The library was developed by <a class=\"af ng\" href=\"https:\/\/www.linkedin.com\/in\/apaszke\/?locale=en_US\" target=\"_blank\" rel=\"noopener ugc nofollow\">Adam Paszke<\/a>, <a class=\"af ng\" href=\"https:\/\/www.linkedin.com\/in\/samgross\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Sam Gross<\/a>, <a class=\"af ng\" href=\"https:\/\/www.linkedin.com\/in\/soumith\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Soumith Chintala<\/a>, and <a class=\"af ng\" href=\"https:\/\/www.linkedin.com\/in\/gregory-chanan-49530836\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Gregory Chanan<\/a>. It offers various tools for machine learning, computer vision, and natural language processing (NLP). PyTorch can also generate computational graphs and execute tensor computations using GPU acceleration.<\/p>\n<p id=\"0335\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Currently, PyTorch is managed by <a class=\"af ng\" href=\"https:\/\/ai.facebook.com\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Meta AI<\/a>, formerly called FAIR (Facebook AI Research lab).<\/p>\n<p id=\"420e\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Strengths:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"c71a\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It provides a solid framework for creating computational graphs backed up by fast execution times.<\/li>\n<li id=\"9887\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It offers a data parallelism capability that allows you to split computing jobs over numerous CPUs or GPUs.<\/li>\n<li id=\"dd07\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It is flexible, faster, and provides optimizations.<\/li>\n<\/ul>\n<p id=\"f1bb\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Weaknesses:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"976f\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It lacks an interface for monitoring and visualization.<\/li>\n<\/ul>\n<figure class=\"oo op oq or os mc\"><\/figure>\n<pre>import torch\n\np_Tensor = torch.ones((2, 2))\n\n#size of a Tensor\nprint(p_Tensor.size())\n\n#resizing 2x2 Tensor to 4x1\np_Tensor = p_Tensor.view(4)\n\nprint(p_Tensor)<\/pre>\n<p id=\"90b2\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Visit <a class=\"af ng\" href=\"https:\/\/pytorch.org\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">PyTorch.org<\/a> to learn more about this library and submit an <a class=\"af ng\" href=\"https:\/\/pytorch.org\/docs\/stable\/community\/contribution_guide.html\" target=\"_blank\" rel=\"noopener ugc nofollow\">open-source contribution<\/a> today!<\/p>\n<h2 id=\"13cf\" class=\"ns nt fo be nu nv nw nx ny nz oa ob oc mt od oe of mx og oh oi nb oj ok ol om bj\" data-selectable-paragraph=\"\">8. Natural Language Toolkit (NLTK)<\/h2>\n<figure class=\"oo op oq or os mc lu lv paragraph-image\">\n<div class=\"md me eb mf bg mg\" tabindex=\"0\" role=\"button\">\n<figure><img loading=\"lazy\" decoding=\"async\" class=\"bg mh mi c\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:215\/1*DJkmMXxWbkot-7HUD8oH5w.png\" alt=\"\" width=\"215\" height=\"235\"><\/figure><div class=\"lu lv pv\"><picture><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/format:webp\/1*DJkmMXxWbkot-7HUD8oH5w.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/format:webp\/1*DJkmMXxWbkot-7HUD8oH5w.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/format:webp\/1*DJkmMXxWbkot-7HUD8oH5w.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/format:webp\/1*DJkmMXxWbkot-7HUD8oH5w.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/format:webp\/1*DJkmMXxWbkot-7HUD8oH5w.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/format:webp\/1*DJkmMXxWbkot-7HUD8oH5w.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:430\/format:webp\/1*DJkmMXxWbkot-7HUD8oH5w.png 430w\" type=\"image\/webp\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 215px\"><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/1*DJkmMXxWbkot-7HUD8oH5w.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/1*DJkmMXxWbkot-7HUD8oH5w.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/1*DJkmMXxWbkot-7HUD8oH5w.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/1*DJkmMXxWbkot-7HUD8oH5w.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/1*DJkmMXxWbkot-7HUD8oH5w.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/1*DJkmMXxWbkot-7HUD8oH5w.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:430\/1*DJkmMXxWbkot-7HUD8oH5w.png 430w\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 215px\" data-testid=\"og\"><\/picture><\/div>\n<\/div>\n<\/figure>\n<p id=\"630e\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">NLTK is a Python library for performing natural language processing (NLP) tasks. The library was developed by <a class=\"af ng\" href=\"http:\/\/www.stevenbird.net\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Steven Bird<\/a>, <a class=\"af ng\" href=\"http:\/\/edward.loper.org\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Edward Loper<\/a>, and <a class=\"af ng\" href=\"https:\/\/homepages.inf.ed.ac.uk\/ewan\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Ewan Klein<\/a>. One thing to keep in mind is that NLTK is a collection of sub-packages and modules rather than a single ML library. These modules enable you to perform a range of tasks. For example, sentence segmentation, stopword removal, word tokenization, entity recognition (NER), dependency parsing, sentiment analysis, and text classification.<\/p>\n<p id=\"0650\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Check out <a class=\"af ng\" href=\"https:\/\/heartbeat.comet.ml\/keyword-extraction-with-python-498bc18aadc\" target=\"_blank\" rel=\"noopener ugc nofollow\">keyword extraction with Python<\/a> to get started with NLTK.<\/p>\n<p id=\"fcd1\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Strengths:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"62ab\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It supports many languages compared to other natural language processing (NLP) libraries.<\/li>\n<li id=\"9171\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">The architecture of NLTK is modular, with multiple sub-packages that can be applied to various NLP tasks.<\/li>\n<\/ul>\n<p id=\"ca84\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Weaknesses:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"423f\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It has no neural network models.<\/li>\n<li id=\"1047\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">NLTK does not use semantic analysis for sentence tokenization.<\/li>\n<\/ul>\n<figure class=\"oo op oq or os mc\"><\/figure>\n<pre>from nltk.stem import PorterStemmer\n#from nltk.tokenize import sent_tokenize, word_tokenize\n\nps = PorterStemmer()\n\ntext_words = [\"tech\",\"technology\",\"technologized\",\"techy\", \"Technologization\"]\n\nfor x in text_words:\n    print(ps.stem(x))<\/pre>\n<p id=\"3a15\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Visit <a class=\"af ng\" href=\"https:\/\/www.nltk.org\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">NLTK.org<\/a> to learn more about this library and submit an <a class=\"af ng\" href=\"https:\/\/www.nltk.org\/contribute.html\" target=\"_blank\" rel=\"noopener ugc nofollow\">open-source contribution<\/a> today!<\/p>\n<h2 id=\"b529\" class=\"ns nt fo be nu nv nw nx ny nz oa ob oc mt od oe of mx og oh oi nb oj ok ol om bj\" data-selectable-paragraph=\"\">9. Danfo.JS<\/h2>\n<figure class=\"oo op oq or os mc lu lv paragraph-image\">\n<figure><img loading=\"lazy\" decoding=\"async\" class=\"bg mh mi c\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:225\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg\" alt=\"\" width=\"225\" height=\"225\"><\/figure><div class=\"lu lv pu\"><picture><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/format:webp\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/format:webp\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/format:webp\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/format:webp\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/format:webp\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/format:webp\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:450\/format:webp\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg 450w\" type=\"image\/webp\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 225px\"><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:450\/1*OqLRc1-6emQ_DbDX2D-wAA.jpeg 450w\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 225px\" data-testid=\"og\"><\/picture><\/div>\n<\/figure>\n<p id=\"691d\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Danfo.js is a JavaScript library for manipulating and processing structured data. It provides high-performance, intuitive, and simple-to-use data structures. The library is based on TensorFlow.js and is greatly inspired by Pandas. This library was developed by <a class=\"af ng\" href=\"https:\/\/risenw.github.io\/risingodegua\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Rising Odegua<\/a> and <a class=\"af ng\" href=\"https:\/\/steveoni.github.io\/v4\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Stephen Oni<\/a>.<\/p>\n<p id=\"0431\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">This library is handy especially because it enables developers to create JavaScript applications for machine learning and deep learning.<\/p>\n<p id=\"a61d\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Strengths:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"ca4f\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It has high-performance and user-friendly data structures.<\/li>\n<li id=\"b632\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It supports tensors.<\/li>\n<li id=\"f0f3\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It makes it simple for web-based apps to support machine learning features.<\/li>\n<li id=\"9a4a\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It provides JavaScript developers with data processing, machine learning, and AI tools.<\/li>\n<\/ul>\n<p id=\"1dcb\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Weaknesses:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"53b3\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It possesses low-level arithmetic operations.<\/li>\n<\/ul>\n<figure class=\"oo op oq or os mc\"><\/figure>\n<pre>npm install danfojs-node\n\nimport * as dfd from \"danfojs-node\"\n\n#creating a DataFrame\/Series\ns = new dfd.Series([1, 3, 5, undefined, 6, 8])\ns.print()<\/pre>\n<p id=\"2053\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Visit <a class=\"af ng\" href=\"https:\/\/danfo.jsdata.org\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Danfo.jsdata.org<\/a> to learn more about this library and submit an <a class=\"af ng\" href=\"https:\/\/danfo.jsdata.org\/contributing-guide\" target=\"_blank\" rel=\"noopener ugc nofollow\">open-source contribution<\/a> today!<\/p>\n<h2 id=\"9a00\" class=\"ns nt fo be nu nv nw nx ny nz oa ob oc mt od oe of mx og oh oi nb oj ok ol om bj\" data-selectable-paragraph=\"\">10. Microsoft Cognitive Toolkit (CNTK)<\/h2>\n<figure class=\"oo op oq or os mc lu lv paragraph-image\">\n<figure><img loading=\"lazy\" decoding=\"async\" class=\"bg mh mi c\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:287\/1*XL2olNIitOWABSTyV_XaYA.png\" alt=\"\" width=\"287\" height=\"175\"><\/figure><div class=\"lu lv pw\"><picture><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/format:webp\/1*XL2olNIitOWABSTyV_XaYA.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/format:webp\/1*XL2olNIitOWABSTyV_XaYA.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/format:webp\/1*XL2olNIitOWABSTyV_XaYA.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/format:webp\/1*XL2olNIitOWABSTyV_XaYA.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/format:webp\/1*XL2olNIitOWABSTyV_XaYA.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/format:webp\/1*XL2olNIitOWABSTyV_XaYA.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:574\/format:webp\/1*XL2olNIitOWABSTyV_XaYA.png 574w\" type=\"image\/webp\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 287px\"><source srcset=\"https:\/\/miro.medium.com\/v2\/resize:fit:640\/1*XL2olNIitOWABSTyV_XaYA.png 640w, https:\/\/miro.medium.com\/v2\/resize:fit:720\/1*XL2olNIitOWABSTyV_XaYA.png 720w, https:\/\/miro.medium.com\/v2\/resize:fit:750\/1*XL2olNIitOWABSTyV_XaYA.png 750w, https:\/\/miro.medium.com\/v2\/resize:fit:786\/1*XL2olNIitOWABSTyV_XaYA.png 786w, https:\/\/miro.medium.com\/v2\/resize:fit:828\/1*XL2olNIitOWABSTyV_XaYA.png 828w, https:\/\/miro.medium.com\/v2\/resize:fit:1100\/1*XL2olNIitOWABSTyV_XaYA.png 1100w, https:\/\/miro.medium.com\/v2\/resize:fit:574\/1*XL2olNIitOWABSTyV_XaYA.png 574w\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 287px\" data-testid=\"og\"><\/picture><\/div>\n<\/figure>\n<p id=\"622b\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Microsoft CNTK is a deep learning model and algorithm training toolkit. It can be used as a standalone machine-learning tool as Brainscript or in Python and C++ projects. CNTK makes it simple to employ standard models, including feed-forward DNNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs\/LSTMs) for voice training, handwriting recognition, and image recognition projects.<\/p>\n<p id=\"2bd2\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Strengths:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"17d1\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It has built-in components that are highly optimized for multi-dimensional dense or sparse data.<\/li>\n<li id=\"579d\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">Its architecture supports GAN, RNN, and CNN.<\/li>\n<li id=\"7051\" class=\"mj mk fo be b ml nn mn mo mp no mr ms mt np mv mw mx nq mz na nb nr nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It provides automatic hyperparameter tuning.<\/li>\n<\/ul>\n<p id=\"ba5c\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\"><strong class=\"be ot\">Weaknesses:<\/strong><\/p>\n<ul class=\"\">\n<li id=\"430e\" class=\"mj mk fo be b ml mm mn mo mp mq mr ms mt nh mv mw mx ni mz na nb nj nd ne nf nk nl nm bj\" data-selectable-paragraph=\"\">It lacks a visualization board.<\/li>\n<\/ul>\n<p id=\"7f8b\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Visit <a class=\"af ng\" href=\"https:\/\/docs.microsoft.com\/en-us\/cognitive-toolkit\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">docs.microsoft.com\/cognitive-toolkit<\/a> to learn more about this library and submit an <a class=\"af ng\" href=\"https:\/\/docs.microsoft.com\/en-us\/cognitive-toolkit\/contributing-to-cntk\" target=\"_blank\" rel=\"noopener ugc nofollow\">open-source contribution<\/a> today!<\/p>\n<h1 id=\"e440\" class=\"px nt fo be nu py pz qa ny qb qc qd oc qe qf qg qh qi qj qk ql qm qn qo qp qq bj\" data-selectable-paragraph=\"\">Final thoughts<\/h1>\n<p id=\"9489\" class=\"pw-post-body-paragraph mj mk fo be b ml qr mn mo mp qs mr ms mt qt mv mw mx qu mz na nb qv nd ne nf fh bj\" data-selectable-paragraph=\"\">Open-source libraries are essential, and they have revolutionized machine learning research. They\u2019re used in various machine learning stacks, helped solve real-world problems, and have simplified the development of real-world projects.<\/p>\n<p id=\"85a4\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">If you enjoyed this post, you should try your hand at applying any of the libraries mentioned while using <a class=\"af ng\" href=\"https:\/\/www.comet.com\/site\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">Comet\u2019s machine learning platform<\/a> to track, compare, and reproduce your machine learning experiments.<\/p>\n<p id=\"3920\" class=\"pw-post-body-paragraph mj mk fo be b ml mm mn mo mp mq mr ms mt mu mv mw mx my mz na nb nc nd ne nf fh bj\" data-selectable-paragraph=\"\">Thanks for reading!<\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>The open-source movement is responsible for most of the technological innovation we see today, and machine learning is no exception. This movement has birthed many new libraries, fueled projects, enabled rapid growth, and increased the reproducibility of experimental results and innovative applications. In addition, these libraries have made it more feasible to design large-scale real-world [&hellip;]<\/p>\n","protected":false},"author":90,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"categories":[6],"tags":[],"coauthors":[187],"class_list":["post-7467","post","type-post","status-publish","format-standard","hentry","category-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>10 Open Source Machine Learning Libraries - Comet<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.comet.com\/site\/blog\/10-open-source-machine-learning-libraries\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"10 Open Source Machine Learning Libraries\" \/>\n<meta property=\"og:description\" content=\"The open-source movement is responsible for most of the technological innovation we see today, and machine learning is no exception. 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In addition, these libraries have made it more feasible to design large-scale real-world [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.comet.com\/site\/blog\/10-open-source-machine-learning-libraries\" \/>\n<meta property=\"og:site_name\" content=\"Comet\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/cometdotml\" \/>\n<meta property=\"article:published_time\" content=\"2023-09-13T00:26:48+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-04-24T17:14:07+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/miro.medium.com\/v2\/resize:fit:700\/1*w-ZZqQxj1RT0LCEKgrb1yQ.png\" \/>\n<meta name=\"author\" content=\"Benny Ifeanyi Iheagwara\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@Cometml\" \/>\n<meta name=\"twitter:site\" content=\"@Cometml\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benny Ifeanyi Iheagwara\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"12 minutes\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"10 Open Source Machine Learning Libraries - Comet","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.comet.com\/site\/blog\/10-open-source-machine-learning-libraries","og_locale":"en_US","og_type":"article","og_title":"10 Open Source Machine Learning Libraries","og_description":"The open-source movement is responsible for most of the technological innovation we see today, and machine learning is no exception. 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