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Explore Transformers, Large Language Models, prompt engineering, and innovative applications like CLIP and image generation in this comprehensive overview of foundation models.
Explore strategies for building and maintaining continual learning systems, including monitoring metrics, data curation, training triggers, and testing methods for machine learning models.
Comprehensive guide to deploying ML models: from prototyping to production, covering architectures, optimization techniques, scaling strategies, and edge deployment considerations.
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.
Comprehensive guide to testing and troubleshooting ML systems, covering software testing tools, automation, ML-specific testing strategies, and performance optimization techniques.
Comprehensive overview of deep learning development infrastructure, covering software engineering, frameworks, distributed training, GPUs, resource management, and experiment tracking.
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.
Explore ML team structures, roles, and management strategies. Gain insights on building effective organizations and navigating the ML job market.
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.
Learn strategies for deploying machine learning models, from batch prediction to edge deployment, with focus on REST APIs, performance optimization, and scaling techniques.
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.
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.
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.
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.
Learn a systematic approach to troubleshoot deep neural networks, from starting simple to tuning hyperparameters, with practical strategies for implementation, debugging, evaluation, and improvement.
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