Alfredo Canziani Courses

Self-Supervised Learning in Computer Vision

Explore advanced self-supervised learning techniques in computer vision, including PIRL, SwAV, AVID+CMA, and Barlow Twins, to enhance your understanding of representation learning and model training.

• YouTube
• 1 hour 36 minutes
• On-Demand
• Free Video

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.

• YouTube
• 1 hour 55 minutes
• On-Demand
• Free Video

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.

• YouTube
• 1 hour 12 minutes
• On-Demand
• Free Video

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.

• YouTube
• 1 hour 8 minutes
• On-Demand
• Free Video

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.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

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.

• YouTube
• 57 minutes
• On-Demand
• Free Video

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.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

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.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

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.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

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.

• YouTube
• 1 hour 9 minutes
• On-Demand
• Free Video

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.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

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.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

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.

• YouTube
• 1 hour 11 minutes
• On-Demand
• Free Video

Matrix Multiplication, Signals, and Convolutions

Explore matrix multiplication, natural signals, and convolutions in deep learning. Includes practical demonstrations using IPython and PyTorch.

• YouTube
• 47 minutes
• On-Demand
• Free Video

Structured Prediction with Energy Based Models

Explore energy-based models for structured prediction, covering factor graphs, efficient inference, Graph Transformer Networks, loss functions, and variational inference in deep learning applications.

• YouTube
• 2 hours 8 minutes
• On-Demand
• Free Video