Implementing DeepMind's DQN from Scratch - Project Update
Explore DeepMind's DQN implementation process, including project organization, code walkthrough, and visualization techniques for reinforcement learning algorithms.
EfficientNetV2 - Smaller Models and Faster Training - Paper Explained
Explore EfficientNetV2's improved image classification performance, including progressive training, Fused-MBConv layer, and novel reward function for Neural Architecture Search.
AlphaGo - Mastering the Game of Go with Deep Neural Networks and Tree Search - RL Paper Explained
Comprehensive exploration of AlphaGo's groundbreaking AI system, detailing its neural networks, search algorithms, and innovative techniques that led to defeating professional Go players.
How to Get Started With Graph ML - Blog Walkthrough
Comprehensive introduction to Graph Machine Learning, covering applications, methods, and resources. Explores research challenges, GNN expressivity, and related subfields, providing a solid foundation for beginners.
Graph Attention Network Project Walkthrough
Comprehensive walkthrough of Graph Attention Network implementation, covering dataset analysis, key implementation details, and related deep learning projects for enthusiasts and practitioners.
Temporal Graph Networks - GNN Paper Explained
Explore temporal graph networks and dynamic graphs, learning advanced techniques for graph machine learning, including time-based sampling, memory management, and attention mechanisms.
Graph SAGE - Inductive Representation Learning on Large Graphs - GNN Paper Explained
Deep dive into Graph SAGE, exploring its innovative approach to large-scale graph learning. Covers key concepts, training methods, aggregator functions, and comparisons with other graph neural networks.
Graph Convolutional Networks - GNN Paper Explained
Comprehensive exploration of Graph Convolutional Networks, covering theory, implementation, and applications. Delves into spectral methods, Weisfeiler-Lehman perspective, and GNN depth, offering insights for both beginners and experts.
Graph Attention Networks - GNN Paper Explained
Explore Graph Attention Networks (GAT) in-depth, covering graph theory basics, GAT methodology, multi-head versions, visualizations, and applications in transductive/inductive learning scenarios.
Learning Forever, Backprop Is Insufficient
Explores continual learning in machine learning, discussing plasticity decay and proposing Continual Backpropagation as a solution to overcome limitations of traditional backpropagation in non-stationary environments.
Building Makemore - Building a WaveNet
Dive deep into building a WaveNet-like convolutional neural network, exploring torch.nn, and understanding the typical deep learning development process through hands-on implementation.
Building Makemore - Becoming a Backprop Ninja
Hands-on tutorial on manual backpropagation through a 2-layer MLP, enhancing understanding of gradient flow in neural networks for confident innovation and debugging.
Building Makemore - Activations & Gradients, BatchNorm
Explore MLP internals, activations, gradients, and BatchNorm. Learn to diagnose deep networks, understand training challenges, and implement modern techniques for improved performance.
Building Makemore - MLP
Implement a multilayer perceptron character-level language model, covering machine learning basics like model training, hyperparameter tuning, evaluation, and addressing under/overfitting.
The Spelled-Out Intro to Language Modeling - Building Makemore
Comprehensive tutorial on building a bigram character-level language model, covering tensor operations, neural networks, and PyTorch fundamentals for natural language processing tasks.
Intro to Neural Networks and Backpropagation - Building Micrograd
Step-by-step guide to neural networks and backpropagation, covering core concepts, implementation in Python, and practical exercises. Ideal for beginners with basic Python knowledge.
VQ-GAN - Taming Transformers for High-Resolution Image Synthesis - Paper Explained
Explore VQ-GAN's innovative approach to high-resolution image synthesis, combining modified VQ-VAEs with GPT-2 for impressive results. Learn about perceptual loss, adversarial techniques, and comparisons with other models.
VQ-VAEs - Neural Discrete Representation Learning - Paper + PyTorch Code Explained
Explore VQ-VAEs for neural discrete representation learning, covering key concepts, implementation details, and applications in generative modeling. Gain insights into this important AI technique.
Non-Parametric Transformers - Paper Explained
Explore non-parametric transformers: innovative AI model for tabular data, relational reasoning, and causal mechanisms. Learn architecture, loss function, and connections to other neural networks.
Sign Language Detection Using Action Recognition with Python - LSTM Deep Learning Model
Learn to build a sign language detection system using action recognition, LSTM, and Python. Extract keypoints, create datasets, train deep learning models, and make real-time predictions for sign language interpretation.
Reinforcement Learning - Full Course Using Python
Comprehensive introduction to reinforcement learning using Python, covering fundamentals, custom environments, and practical projects like Atari games and autonomous driving.