Alfredo Canziani Courses

Convolutional Neural Nets - CNNs

Explore locality, stationarity, and compositionality in natural input data using convolutional neural networks. Learn CNN components, parameter sharing, and hands-on implementation.

• YouTube
• 1 hour 27 minutes
• On-Demand
• Free Video

Recurrent Neural Networks - Training Example

Learn to train Recurrent Neural Networks through a hands-on example, exploring the learning problem, coding implementation, and prototyping process.

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

CNN Loss

Explore convolutional neural network loss functions, including mean square error, cross-entropy, and negative log likelihood, with practical examples and implementation insights.

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

CNN Training

Learn to train Convolutional Neural Networks using the optim package, covering objective functions, datasets, training scripts, and gradient optimization techniques.

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

An Overview of Lua

Explore Lua programming language basics, including syntax, data structures, and control flow, in this concise tutorial for beginners and intermediate coders.

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

Inference with Neural Nets

Explore neural network inference, covering mathematical foundations, data science concepts, and practical tools like PyTorch. Gain insights into advanced topics and real-world applications.

• YouTube
• 1 hour 7 minutes
• On-Demand
• Free Video

PCA, AE, K-Means, Gaussian Mixture Model, Sparse Coding, and Intuitive VAE

Explore advanced machine learning techniques including PCA, autoencoders, K-means, Gaussian mixture models, sparse coding, and VAEs. Gain insights into their applications and intuitive interpretations.

• YouTube
• 1 hour 54 minutes
• On-Demand
• Free Video

Differentiable Associative Memories, Attention, and Transformers

Explore differentiable associative memories, attention mechanisms, and transformers. Learn about energy minimization, planning, and various transformer applications in machine learning and AI.

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

Self-Supervised Learning and Variational Inference

Explore self-supervised learning, variational inference, and advanced deep learning concepts with Yann LeCun. Gain insights into GANs, sparse modeling, and Variational Autoencoders (VAEs) in this comprehensive lecture.

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

Prediction and Planning Under Uncertainty

Explore prediction and planning in uncertain environments, covering model predictive control, stochastic scenarios, and practical applications in AI decision-making.

• YouTube
• 1 hour 15 minutes
• On-Demand
• Free Video

Latent Variable EBMs for Structured Prediction

Explore advanced machine learning concepts including energy-based models, contrastive methods, latent variable models, and structured prediction. Gain insights into cutting-edge techniques and algorithms in deep learning.

• YouTube
• 1 hour 49 minutes
• On-Demand
• Free Video

Joint Embedding Method and Latent Variable Energy Based Models

Explore joint embedding methods and latent variable energy-based models with Yann LeCun. Learn about predictive systems, inference, probabilistic models, and various training techniques for advanced machine learning applications.

• YouTube
• 1 hour 52 minutes
• On-Demand
• Free Video

The Truck Backer-Upper

Explore neural network applications in vehicle control, focusing on the Truck Backer-Upper problem. Learn about state transitions, training strategies, and Bayesian neural networks.

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

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.

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

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.

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