Archive

A chronological archive of everything published on this site.

Published elsewhere

1. An Introduction to HuggingFace’s Accelerate Library: An introduction to the Accelerate Library by HuggingFace.
2. Train, Optimize, Analyze, Visualize and Deploy Models for Automatic Speech Recognition with NVIDIA’s NeMo: A guide on training, optimizing, analyzing, visualizing, and deploying models for Automatic Speech Recognition with NVIDIA’s NeMo.
3. Interpret any PyTorch Model Using W&B Embedding Projector: A guide on interpreting any PyTorch model using the W&B Embedding Projector.
4. How Weights and Biases Can Help with Audits & Regulatory Guidelines: A discussion on how Weights and Biases can assist with audits and regulatory guidelines.
5. How Weights & Biases and MS Fairlearn can help deal with Model and Dataset Bias: A guide on how Weights & Biases and MS Fairlearn can help deal with model and dataset bias.
6. ResNet Strikes Back: A Training Procedure in TIMM: A report on the training procedure of ResNet in TIMM.
7. Is MLP-Mixer a CNN in Disguise?: A discussion on whether MLP-Mixer is a CNN in disguise.
8. Are fully connected and convolution layers equivalent? If so, how?: A report on the equivalence of fully connected and convolution layers.
9. ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases: A report on improving Vision Transformers with Soft Convolutional Inductive Biases.
10. A faster way to get working and up-to-date conda environments using “fastchan”: A guide on using “fastchan” for faster and up-to-date conda environments.
11. Explained: Characterizing Signal Propagation to Close the Performance Gap in Unnormalized ResNets: An explanation of characterizing signal propagation to close the performance gap in unnormalized ResNets.
12. Revisiting ResNets: Improved Training and Scaling Strategies: A report on improved training and scaling strategies for ResNets.
13. EfficientNetV2: A report on EfficientNetV2.
14. I trained on ImageNet for the “first time” - here’s what I learnt: A report on the author’s experience and learnings from training on ImageNet for the first time.
15. Understanding Logits, Sigmoid, Softmax, and Cross-Entropy Loss in Deep Learning: A deep dive into understanding logits, sigmoid, softmax, and cross-entropy loss in deep learning.
16. How to Build a Robust Medical Model Using Weights & Biases: A guide on building a robust medical model using Weights & Biases.
17. Tracking CO2 Emissions of Your Deep Learning Models with CodeCarbon and Weights & Biases: A guide on tracking CO2 emissions of deep learning models with CodeCarbon and Weights & Biases.
18. How to track all your experiments using Microsoft Excel?: A guide on tracking all your experiments using Microsoft Excel.
19. How to save all your trained model weights locally after every epoch: A guide on saving all your trained model weights locally after every epoch.
20. How to prepare the dataset for the Melanoma Classification?: A guide on preparing the dataset for the Melanoma Classification.
21. How to use Weights & Biases for your Kaggle Competitions?: A guide on using Weights & Biases for Kaggle competitions.
22. How to use Weights & Biases for your next Machine Learning Project?: A guide on using Weights & Biases for your next Machine Learning project.

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