Neural Networks Courses and Certifications

MIT HAN Lab

EfficientML.ai - Basics of Neural Networks - Lecture 2

Explore neural network fundamentals, including architecture, training, and optimization techniques. Gain insights into efficient machine learning concepts and applications.

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

MIT HAN Lab

Basics of Neural Networks - Lecture 2

Explore neural network fundamentals, including architecture, training, and optimization techniques for efficient machine learning model development.

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

Wolfram

Understanding ChatGPT and Neural Networks for Human-Like Tasks

Explore the inner workings of ChatGPT and large language models, delving into neural networks, model fitting, and machine learning techniques for human-like tasks.

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

Valence Labs

Clifford Group Equivariant Neural Networks

Explore Clifford Group Equivariant Neural Networks for E(n)-equivariant network construction. Discover theoretical foundations, methodology, and applications in various dimensions and tasks.

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

Valence Labs

Geometric Clifford Algebra Networks and Clifford Neural Layers for PDE Modeling

Explore Clifford algebras in deep learning for PDE modeling, focusing on geometric transformations and fluid dynamics. Learn about Clifford neural layers, convolutions, and Fourier transforms to enhance neural PDE surrogates.

• YouTube
• 1 hour 32 minutes
• On-Demand
• Free Video

Simons Institute

In Search of New Algorithms Part I - Neural Networks

Explore neural networks and their role in developing new algorithms, focusing on computational complexity and statistical inference challenges.

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

Simons Institute

The Interpolation Phase Transition in Neural Networks - Memorization and Generalization Lazy Training

Explores overparametrization in neural networks, focusing on two-layer models in the neural tangent regime. Analyzes interpolation, optimization landscape, and generalization error under specific data assumptions.

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

Simons Institute

Towards Verification of General Neural Networks: A Dual Approach

Explore a dual approach for verifying general neural networks, focusing on mathematical and computational challenges in real-time decision making.

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

Simons Institute

Learning One-Hidden-Layer Neural Networks with Landscape Design

Explore optimization techniques for one-hidden-layer neural networks, focusing on landscape design and new objective functions to overcome traditional training challenges.

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

Simons Institute

Composing Graphical Models with Neural Networks for Structured Representations and Fast Inference

Explore structured representations and fast inference by combining graphical models with neural networks, enhancing machine learning capabilities.

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

Applied Algebraic Topology Network

On the Topological Expressiveness of Neural Networks

Explore the topological constraints of neural network architectures and their impact on function representation, with insights from recent research and ongoing work in the field.

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

DigitalSreeni

The Topology of Deep Neural Networks - Designing Your Model

Explore deep neural network topology and model design techniques for effective machine learning implementations.

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

Aleksa Gordić - The AI Epiphany

Tensor Programs - Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Explore μTransfer: a technique for tuning large neural networks via zero-shot hyperparameter transfer, making optimal hyperparameters stable with respect to width scaling.

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

Aleksa Gordić - The AI Epiphany

The Sensory Neuron as a Transformer - Permutation-Invariant Neural Networks for Reinforcement Learning - Paper Explained

Explore Google Brain's "Attention Neuron" model for reinforcement learning, featuring permutation-invariant neural networks and their application in various experiments and visualizations.

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

Simons Institute

Neural Networks and Spurious Correlations

Explore neural networks' vulnerability to spurious correlations and strategies to enhance robustness, including reweighting, subsampling, and overcoming simplicity bias in machine learning models.

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