The Full Stack Courses

Continual Learning - FSDL 2022

Explore strategies for building and maintaining continual learning systems, including monitoring metrics, data curation, training triggers, and testing methods for machine learning models.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

Deployment - FSDL 2022

Comprehensive guide to deploying ML models: from prototyping to production, covering architectures, optimization techniques, scaling strategies, and edge deployment considerations.

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

Data Management - FSDL 2022

Comprehensive overview of data management in ML, covering storage, processing, exploration, and versioning. Includes best practices and insights on self-supervised learning and data labeling.

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

Troubleshooting & Testing - FSDL 2022

Comprehensive guide to testing and troubleshooting ML systems, covering software testing tools, automation, ML-specific testing strategies, and performance optimization techniques.

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

Development Infrastructure & Tooling - FSDL

Comprehensive overview of deep learning development infrastructure, covering software engineering, frameworks, distributed training, GPUs, resource management, and experiment tracking.

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

When to Use ML and Course Vision - FSDL 2022

Explore when to use machine learning, how to choose ML problems, and the ML project lifecycle in this comprehensive lecture on applying deep learning effectively.

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

ML Teams - Full Stack Deep Learning - Spring 2021

Explore ML team structures, roles, and management strategies. Gain insights on building effective organizations and navigating the ML job market.

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

Research Directions - Full Stack Deep Learning - Spring 2021

Explore recent advances in deep learning, including unsupervised and reinforcement learning, meta-learning, and applications in science and engineering. Gain insights into research trends and staying updated.

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

Deploying ML Models - Full Stack Deep Learning - Spring 2021

Learn strategies for deploying machine learning models, from batch prediction to edge deployment, with focus on REST APIs, performance optimization, and scaling techniques.

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

Monitoring ML Models - Full Stack Deep Learning - Spring 2021

Learn techniques for monitoring ML models in production, including detecting data drift, measuring changes, and using tools to maintain model health and performance over time.

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

ML Testing & Explainability - Full Stack Deep Learning - Spring 2021

Explore ML testing and explainability techniques to enhance model performance, build confidence, and understand limitations. Learn software testing practices, CI/CD, and interpretable AI approaches.

• YouTube
• 1 hour 41 minutes
• On-Demand
• Free Video

Ethics - Full Stack Deep Learning - Spring 2021

Explore AI ethics, covering long-term challenges, hiring practices, fairness, representation, and best practices. Gain insights into crucial ethical considerations for AI development and implementation.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

Data Management - Full Stack Deep Learning - Spring 2021

Explore ML infrastructure for data management: ingestion, storage, processing, exploration, labeling, and versioning. Learn about tools and best practices for effective dataset handling in deep learning projects.

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

Troubleshooting Deep Neural Networks - Full Stack Deep Learning - Spring 2021

Learn a systematic approach to troubleshoot deep neural networks, from starting simple to tuning hyperparameters, with practical strategies for implementation, debugging, evaluation, and improvement.

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

Infrastructure & Tooling - Full Stack Deep Learning - Spring 2021

Comprehensive overview of ML infrastructure and tools, covering software engineering, computing, resource management, frameworks, experiment management, and hyperparameter optimization for practitioners.

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