Data Science Courses Courses

PAC Learnability in Deep Learning - Lecture 20

Delve into statistical machine learning fundamentals, exploring PAC learnability concepts and their application in deep learning, with focus on generalization theories and practical challenges.

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

Graph Neural Networks: Graph Attention Networks and Convolutional Networks - Lecture 19

Dive into Graph Convolutional Networks and Graph Attention Networks, exploring how attention mechanisms enhance node feature processing for advanced network learning applications.

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

Graph Neural Networks and Convolutional Networks - Part 1 - Lecture 18

Dive into Graph Convolutional Networks, exploring convolution operations on graphs, ChebNet architecture, and practical applications of spectral-based graph neural networks.

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

Denoising Diffusion Probabilistic Models and Generative Moment Matching Networks - Lecture 17

Delve into diffusion models and DDPMs, exploring progressive data refinement techniques and generative moment matching networks using Maximum Mean Discrepancy for distribution analysis.

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

Generative Adversarial Networks and Adversarial Autoencoders - Lecture 16

Dive into advanced concepts of GANs and AAEs, exploring conditional implementations, mode collapse solutions, and practical applications in supervised and unsupervised learning scenarios.

• YouTube
• 1 hour 20 minutes
• On-Demand
• Free Video

Variational Autoencoders and Performers in Deep Learning - Lecture 15

Delve into advanced deep learning concepts including Variational Autoencoders (VAE), variational inference, ELBO computation, and Performer-based Transformer architectures.

• YouTube
• 1 hour 13 minutes
• On-Demand
• Free Video

Deep Learning: RLHF, ChatGPT, and Alignment in LLMs - Lecture 14

Dive into advanced concepts of RLHF, ChatGPT training, and LLM alignment, exploring efficient transformers and the technical foundations behind modern language models.

• YouTube
• 1 hour 14 minutes
• On-Demand
• Free Video

Deep Learning: Deep Reinforcement Learning and Policy Gradients - Lecture 13

Dive into advanced deep reinforcement learning concepts including policy gradients, REINFORCE algorithm, and their applications in Atari games and Go, taught by Ali Ghodsi.

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

Deep Learning: Introduction to Reinforcement Learning and Policy Iteration - Lecture 12

Dive into reinforcement learning fundamentals, exploring policy iteration, value iteration, Markov decision processes, and Monte Carlo estimation techniques for advanced AI applications.

• YouTube
• 1 hour 40 minutes
• On-Demand
• Free Video

Deep Learning: Transformers and Encoder-Decoder Architecture - Lecture 10

Dive into transformer architectures, exploring encoder-decoder mechanisms and positional embeddings for advanced deep learning applications.

• YouTube
• 1 hour 10 minutes
• On-Demand
• Free Video

Deep Learning: BERT and GPT Models - Lecture 11

Dive into the evolution and architecture of transformer models, exploring BERT, GPT series, and T5 to understand their impact on modern natural language processing.

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

Attention Mechanism and Self-Attention in Deep Learning - Lecture 9

Dive into attention mechanisms and self-attention concepts in deep learning, exploring their revolutionary impact on NLP and discovering practical applications in sequence models.

• YouTube
• 1 hour 18 minutes
• On-Demand
• Free Video

Deep Learning: Dropout and Batch Normalization - Lecture 5

Delve into dropout and batch normalization techniques, understanding their mechanisms and effectiveness in deep learning, with insights on loss function smoothness and Lipschitzness.

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

Recurrent Neural Networks and Backpropagation Through Time - Lecture 8

Dive into recurrent neural networks, exploring LSTM, GRU architectures, and advanced concepts like backpropagation through time and gradient challenges in deep learning implementations.

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

Deep Learning: Convolutional Neural Networks and CNN Architecture - Lecture 6

Dive into the fundamentals of Convolutional Neural Networks (CNNs), exploring convolution operations, cross-correlation techniques, and pooling layers for advanced deep learning applications.

• YouTube
• 1 hour 18 minutes
• On-Demand
• Free Video