ConvNets in Practice
Explore practical applications of ConvNets in object detection, face recognition, and semantic segmentation. Learn about network architectures, performance comparisons, and real-world implementations in robotics and image processing.
Parameter Sharing - Recurrent and Convolutional Nets
Explore parameter sharing in recurrent and convolutional neural networks, covering hypernetworks, RNNs, LSTMs, attention mechanisms, and ConvNets, with insights on architecture, training, and biological inspiration.
Modules and Architectures
Explore non-linear functions, cost functions, and advanced architectures like multiplicative interaction and mixture of experts in deep learning, with insights from expert Yann LeCun.
Gradient Descent and the Backpropagation Algorithm
Explore gradient descent and backpropagation algorithms, covering supervised learning, parametrized models, loss functions, and neural network architectures with PyTorch implementation.
Graph Convolutional Networks - GCNs
Explore Graph Convolutional Networks (GCNs), their connection to CNNs, and implementation using PyTorch and DGL. Learn about tensors on vertices and edges, residual gated GCNs, and domain sparsity.
Speech Recognition and Graph Transformer Networks
Explore modern speech recognition techniques, including connectionist temporal classification, beam search decoding, and Graph Transformer Networks, with expert insights and practical applications.
Self - Cross, Hard - Soft Attention and the Transformer
Explore attention mechanisms and Transformer architecture, covering self/cross and hard/soft attention, key-value stores, and practical implementation in PyTorch for advanced deep learning applications.
AE, DAE, and VAE with PyTorch - Generative Adversarial Networks and Code
Explore autoencoders, VAEs, and GANs with PyTorch. Learn implementation, visualization, and comparison of generative models through hands-on coding and insightful analogies.
From LV-EBM to Target Prop to Autoencoder
Explore various autoencoder types, from target propagation to variational autoencoders, understanding their mechanisms, applications, and differences in deep learning and neural network architectures.
Unsupervised Learning - Autoencoding the Targets
Explore unsupervised learning and generative models, covering topics like latent space interpolation, style transfer, super resolution, and autoencoders. Gain insights into advanced machine learning concepts and techniques.
Latent Variable Energy Based Models
Explore Latent Variable Energy Based Models (LV-EBMs) training, covering free energy concepts, loss functionals, and model manifolds. Learn practical implementation using PyTorch and gain insights into decoder learning and latent size selection.
Latent Variable Energy Based Models - Inference
Explore Latent Variable Energy Based Models and inference techniques, focusing on energy functions, manifold generation, and free energy computation in unconditional cases.
Recurrent Neural Networks, Vanilla and Gated - LSTM
Explore recurrent neural networks, including LSTM, covering architectures, training methods, and practical implementations using PyTorch for sequence-based tasks in deep learning.
Natural Signals Properties and the Convolution
Explore natural signal properties, convolutional neural networks, and their applications in image processing. Learn about stationarity, locality, sparsity, and parameter sharing in neural network architectures.
Neural Nets - Rotation and Squashing
Explore neural network fundamentals, including affine transformations, non-linearities, and PyTorch implementation. Learn about ReLU, tanh, and deep network architectures through intuitive explanations and coding examples.
Tools, Classification with Neural Nets, PyTorch Implementation
Explore neural network classification, PyTorch implementation, and advanced concepts like space-fabric stretching and regression uncertainty estimation in this comprehensive tutorial.
Supervised and Self-Supervised Transfer Learning With PyTorch Lightning
Explore supervised and self-supervised transfer learning techniques using PyTorch Lightning, comparing their implementation, training, and generalization capabilities in deep learning applications.
Deep Learning for Natural Language Processing
Explore NLP architectures, transformers, language models, and advanced techniques for text generation and embedding learning in this comprehensive deep learning lecture.
Attention and the Transformer
Explore self-attention, key-value stores, and transformer architecture in deep learning. Learn to implement attention mechanisms and understand encoder-decoder paradigms for advanced neural network design.
PyTorch Activation and Loss Functions
Explore activation and loss functions in PyTorch, including kink vs. smooth activations, margin-based losses, and designing effective loss functions for Energy-Based Models (EBMs).
Group Sparsity, World Model, and Generative Adversarial Networks
Explore advanced deep learning concepts: sparse auto-encoders, world models for autonomous control, and generative adversarial networks (GANs). Gain insights into cutting-edge AI techniques and their applications.