Graph Neural Networks (GNN) Courses and Certifications

Deep Learning

Master neural networks, CNNs, RNNs, transformers, and generative models with theory and applications in computer vision, NLP, and robotics from MIT.

• MIT OpenCourseWare
• Self-Paced
• Free Online Course

Code With Aarohi

AI and Deep Learning Explained - CNNs, Transformers, GANs, YOLO, GNNs and More

Discover comprehensive AI and deep learning through 115+ tutorials covering CNNs, Transformers, GANs, YOLO, and more with hands-on implementations in TensorFlow and PyTorch.

• YouTube
• 1 day 13 hours 59 minutes
• Self-Paced
• Free Video

Artificial Intelligence Drug Design

Explore AI-driven drug design, covering target discovery, AlphaFold, molecular generation, and ADMET prediction through practical case studies and hands-on tools from Fudan University experts.

• XuetangX
• Self-Paced
• Free Online Course

Interactive Imitation Learning: Planning Alongside Humans

Interactive imitation learning for robots: tackling feedback-driven covariate shift and predicting human intent using graph neural networks, with applications in self-driving vehicles.

• YouTube
• 1 hour 3 minutes
• Self-Paced
• Free Video

TensorFlow

Intro to Graph Neural Networks

Comprehensive introduction to graph neural networks, covering theory, applications, and hands-on practice with a Colab exercise. Ideal for those interested in advanced machine learning techniques.

• YouTube
• 51 minutes
• Self-Paced
• Free Video

Institute for Advanced Study

Theory of Deep Learning - Where Next?

Explore cutting-edge theoretical foundations of deep learning through expert talks on optimization, generalization, representation learning, and neural network interpretability.

• YouTube
• 17 hours 49 minutes
• Self-Paced
• Free Video

MedAI- Graph-Based Modeling in Computational Pathology - Siyi Tang

1 rating

Explore graph-based modeling in computational pathology, focusing on cellular interactions and spatial structures in whole slide images using graph neural networks for biomarker discovery and workflow automation.

• YouTube
• 49 minutes
• Self-Paced
• Free Video

Machine Learning with Graphs - Fall 2019

Comprehensive exploration of machine learning with graphs, covering network properties, community structures, graph neural networks, and their applications in various domains.

• YouTube
• 1 day 4 hours
• Self-Paced
• Free Video

Steve Brunton

Interpretable Deep Learning for New Physics Discovery

Explore a novel method combining deep learning and symbolic regression to uncover physical laws from data, with applications in physics and cosmology.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

Yannic Kilcher

How to Make Your CPU as Fast as a GPU - Advances in Sparsity with Nir Shavit

Explore advances in sparsity and how CPUs can match GPU performance in neural networks. Learn about pruning, efficient algorithms, and the future of sparse architectures in deep learning.

• YouTube
• 50 minutes
• Self-Paced
• Free Video

Yannic Kilcher

The Future of AI Is Self-Organizing and Self-Assembling - With Prof. Sebastian Risi

Explore self-organizing AI systems, swarm intelligence, and neural cellular automata with Prof. Sebastian Risi. Discover applications, challenges, and future directions in this emerging field of artificial intelligence.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

Yannic Kilcher

Parameter Prediction for Unseen Deep Architectures - With First Author Boris Knyazev

Explore parameter prediction for neural networks using graph hypernetworks. Learn about DeepNets-1M dataset, training techniques, and experimental results for efficient network training paradigms.

• YouTube
• 48 minutes
• Self-Paced
• Free Video

Yannic Kilcher

Discovering Symbolic Models from Deep Learning with Inductive Biases

Exploring how Graph Neural Networks and symbolic regression can derive accurate symbolic equations from observational data, combining deep learning with physics-inspired inductive biases.

• YouTube
• 46 minutes
• Self-Paced
• Free Video

Institute for Advanced Study

Representational Power of Graph Neural Networks - Stefanie Jegelka

Explore the representational power of Graph Neural Networks, focusing on message passing, aggregation operations, and their impact on network structure and reasoning tasks.

• YouTube
• 28 minutes
• Self-Paced
• Free Video

Alexander Amini;  MIT

Machine Learning for Scent

Explore machine learning applications in digitizing scent, from understanding olfactory processes to predicting odor descriptors using graph neural networks and interpreting molecular fragrance data.

• YouTube
• 39 minutes
• Self-Paced
• Free Video