The Full Stack Courses

Introduction to Deep Learning - Full Stack Deep Learning - March 2019

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Comprehensive introduction to AI and deep learning, covering key concepts, applications, and historical context. Explores data processing, computational requirements, and course structure.

• YouTube
• 26 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

ML Projects - Full Stack Deep Learning - Spring 2021

Learn to set up ML projects professionally, covering lifecycle, feasibility, impact, archetypes, metrics, and baselines. Gain insights into project management and optimization for successful machine learning implementations.

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

Transfer Learning and Transformers - Full Stack Deep Learning - Spring 2021

Explore transfer learning's evolution from computer vision to NLP, diving into embeddings, language models, and the revolutionary Transformer architecture. Gain insights into various models and their applications.

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

Recurrent Neural Networks - Full Stack Deep Learning - Spring 2021

Explore Recurrent Neural Networks: architecture, challenges, solutions, and applications. Dive into LSTMs, bidirectionality, attention mechanisms, CTC loss, and non-recurrent sequence models like WaveNet.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

Computer Vision Applications - Full Stack Deep Learning - Spring 2021

Comprehensive overview of deep learning applications in computer vision, covering ConvNet architectures, detection methods, and advanced tasks like 3D shape inference and style transfer.

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