High Performance Machine Learning, Deep Learning, and Data Science - Principles and Practice

Overview

Master high-performance computing principles for machine learning and deep learning, focusing on practical implementation strategies and advanced techniques for optimizing data science workflows.

Syllabus

• Introduction to High-Performance Computing (HPC) in AI
Overview of HPC concepts and its importance in AI
Key differences between traditional computing and HPC
• Machine Learning Foundations
Supervised vs. unsupervised learning
Key algorithms: Linear regression, decision trees, clustering
• Deep Learning Fundamentals
Neural networks: Architecture and training
Key deep learning models: CNNs, RNNs, Transformers
• High-Performance Computing Architecture
Parallel computing and its role in machine learning
Overview of CPUs, GPUs, TPUs, and FPGA
• Optimizing Machine Learning Workflows
Data preprocessing and feature engineering at scale
Efficient hyperparameter tuning techniques
• Advanced Deep Learning Techniques
Transfer learning and fine-tuning models
Deployment of models in production environments
• Distributed Machine Learning
Frameworks for distributed training: TensorFlow, PyTorch, Horovod
Managing data distribution and synchronization
• Performance Profiling and Optimization
Profiling tools and performance metrics
Techniques for memory optimization and reducing computation time
• Advanced Data Science Practices
Handling large datasets efficiently
Data pipeline automation and orchestration
• Case Studies and Practical Implementation
Industry case studies showcasing HPC in AI applications
Hands-on projects: Implementing HPC solutions in real-world scenarios
• Future Trends in AI and HPC
Emerging technologies in AI acceleration
Ethical considerations and challenges in high-performance AI systems
• Final Project
Design and implement a high-performance solution for a complex AI task
Presentation and peer review of project outcomes

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Subjects

Data Science