Convolutional Neural Networks - Full Stack Deep Learning - Spring 2021
Explore convolutional neural networks, from basic operations to advanced concepts like dilated convolutions and pooling. Learn about the classic LeNet architecture and gain insights into ConvNet implementation.
Deep Learning Fundamentals - Full Stack Deep Learning
Comprehensive overview of deep learning basics, covering neural networks, learning problems, optimization techniques, and architectural considerations for AI practitioners.
Coding a Neural Network - Full Stack Deep Learning - Spring 2021
Code a neural network from scratch using Google Colab, NumPy, PyTorch, and Keras. Learn linear regression and multi-layer perception models through hands-on implementation and visualization.
Andrej Karpathy on AI at Tesla - Full Stack Deep Learning - August 2018
Explore Tesla's AI innovations with Andrej Karpathy, delving into cutting-edge deep learning applications in autonomous driving and computer vision.
Yangqing Jia on Deep Learning Frameworks - August 2018
Explore deep learning frameworks with Alibaba's VP AI, covering Caffe, Caffe2, and TensorFlow development insights from a pioneering expert in the field.
Data Science at Uber - Full Stack Deep Learning - August 2018
Explore Uber's data science approach with Jai Ranganathan, covering model lifecycle, customer-centric problem-solving, and practical challenges in AI implementation at scale.
Orgs - ML Teams - Full Stack Deep Learning
Explore ML team structures in organizations, from nascent to ML-first, covering key aspects like software engineering, data ownership, and model deployment responsibilities.
Computing and GPUs - Infrastructure & Tooling - Full Stack Deep Learning
Explore hardware options for deep learning, from NVIDIA GPUs to cloud providers. Learn to choose the right setup for solo developers, startups, and large companies.
Xavier Amatriain on Practical Deep Learning Systems - November 2019
Practical insights on deep learning systems, covering data handling, model selection, architecture engineering, and real-world implementation challenges in various domains.
Richard Socher on NLP at Salesforce - Full Stack Deep Learning - March 2019
Explore cutting-edge NLP applications at Salesforce with Richard Socher, discussing deep learning advancements and their impact on natural language processing in enterprise settings.
Jeremy Howard on Platform.ai and Fast.ai - Full Stack Deep Learning - March 2019
Explore machine learning innovations with Jeremy Howard, covering data labeling, deep learning successes, and insights from Fast.ai and Platform.ai for effective AI development.
Research Directions - Full Stack Deep Learning - March 2019
Explore cutting-edge deep learning research directions and strategies to stay informed with Professor Pieter Abbeel in this comprehensive lecture from the Full Stack Deep Learning bootcamp.
Testing and Deployment - Full Stack Deep Learning - March 2019
Learn about testing and deploying ML projects, covering project structure, evaluation, continuous integration, Docker, REST APIs, and various deployment options for efficient and scalable systems.
Troubleshooting Deep Neural Networks - Full Stack Deep Learning - March 2019
Learn effective strategies for diagnosing and fixing issues in deep neural networks, including a decision tree for systematic troubleshooting and performance optimization.
Data Management - Full Stack Deep Learning - March 2019
Explore data management essentials: labeling, storage, versioning, and processing. Learn about data flywheels, annotation techniques, and efficient workflows for deep learning projects.
Machine Learning Teams - Full Stack Deep Learning - March 2019
Explore AI talent challenges, ML roles, team structures, and hiring strategies for building effective machine learning teams in today's competitive landscape.
Lukas Biewald on Founding Weights & Biases and FigureEight - Full Stack Deep Learning - March 2019
Insights from AI industry pioneer on founding data labeling and ML tools companies, exploring challenges, breakthroughs, and the future of machine learning applications.
Tracking Experiments - Full Stack Deep Learning - March 2019
Learn to track deep learning experiments using Weights & Biases for a text recognizer project, enhancing your model development workflow and results analysis.
Infrastructure and Tooling - Full Stack Deep Learning - March 2019
Explore infrastructure and tooling for deep learning projects, covering GPU machines, experiment tracking, and all-in-one solutions like AWS SageMaker.
Introduction to the Text Recognizer Project - Full Stack Deep Learning - March 2019
Hands-on labs introducing text recognition project, covering setup, data handling, model creation, training, and deployment in a full-stack deep learning context.
Efficient AutoML with Ludwig, Ray, and Nodeless Kubernetes
Explore efficient AutoML using Ludwig and Ray, leveraging heuristics for model production. Learn how Nodeless Kubernetes optimizes GPU resource management, reducing costs in cloud environments.