Deep Learning Tutorials

Learn how to run Meta's TRIBE V2 model on Google Colab, predict cortical activity from naturalistic stimuli, and visualize the results as interactive 3D brain heatmaps.

Master the inner workings of LSTM networks, the foundation for modern LLMs. Explore gating mechanisms, gradients, and build a sentiment classifier with PyTorch.

Learn what natural language processing (NLP) is and discover its real-world application, using Google BERT to process text datasets.

Learn how to construct and implement Convolutional Neural Networks (CNNs) in Python with PyTorch.

Learn about Kolmogorov-Arnold Networks (KANs), a new type of neural network with enhanced interpretability and accuracy compared to traditional models.

Discover how the AdamW optimizer improves model performance by decoupling weight decay from gradient updates. This tutorial explains the key differences between Adam and AdamW, their use cases and provides a step-by-step guide to implementing AdamW in PyTorch.

Learn the Adagrad optimization technique, including its key benefits, limitations, implementation in PyTorch, and use cases for optimizing machine learning models.

This comprehensive, hands-on tutorial teaches you how to simplify deep learning model development with PyTorch Lightning. Perfect for beginners and experienced developers alike, it covers environment setup, model training, and practical examples.

Explore cross-entropy in machine learning in our guide on optimizing model accuracy and effectiveness in classification with TensorFlow and PyTorch examples.

A step-by-step guide to how the transformer architecture works, from self-attention to the decoder-only LLMs built on it.

A walkthrough of Autoencoders, their variations, and potential applications in the real world.

A complete guide to understanding CNNs, their impact on image analysis, and some key strategies to combat overfitting for robust CNN vs deep learning applications.