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Debugging Image Classifiers With Confusion Matrices
Introduction We often rely on scalar metrics and static plots to describe and evaluate machine learning models, but these methods…
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Compare Object Detection Models From TorchVision
Introduction Object detection is one of the most popular applications of machine learning for computer vision. A detection model predicts…
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Credit Card Fraud Detection With Autoencoders
In this article, we’ll leverage the power of autoencoders to address a key issue for banks and their customers: credit…
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“Text-to-Color” from Scratch with CLIP, PyTorch, and Hugging Face Spaces
Example input and output from the Gradio app built using the Text to Color model. Moving from left to right,…
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Using CLIP and Gradio to assess similarity between text prompts and ranges of colors
Link to Colab notebook Hugging Face Space Intro OpenAI’s CLIP model and related techniques have taken the field of machine…
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4 Techniques To Tackle Overfitting In Deep Neural Networks
Image Created By Author Using Canva A neural network is a combination of different neurons, layers, weights, and biases. The…
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How To Train Your Deep Learning Models Faster
Photo by Marc-Olivier Jodoin on Unsplash Deep learning is a subset of machine learning that utilizes neural networks in “deep” architectures, or…
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How to 10x Throughput When Serving Hugging Face Models Without a GPU
By optimising how a model is served, we serve over 100 predictions per second with a simply Python API using…
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Debugging Classifiers with Confusion Matrices
A confusion matrix can provide us with a more representative view of our classifier’s performance, including which specific instances it…
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How to apply machine learning and deep learning methods to audio analysis
To view the code, training visualizations, and more information about the python example at the end of this post, visit…
Category: Tutorials
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