The Five Pillars of MLSecOps - Episode 134
Explore the five pillars of ML SecOps with Ian Swanson: supply chain vulnerabilities, model provenance, GRC, trusted AI, and adversarial machine learning. Learn to secure AI systems and comply with emerging regulations.
Understanding ChatGPT: From Machine Learning to Language Models
Explore machine learning, neural networks, and GPT technology. Gain insights into the evolution and inner workings of ChatGPT, from basic concepts to advanced applications.
Choosing the Right AI: Copilot, Copilot Studio, Azure AI Studio, and More
Explore AI technologies like Copilot, Copilot Studio, and Azure AI Studio. Learn foundations of generative AI and get guidance on selecting the right AI for different use cases.
Quantum and Post-Quantum Cryptography - Opportunities, Controversies, and Path to Quantum Resistance
Explore quantum-resistant cryptography, including post-quantum algorithms and physical-layer approaches. Examine NIST's recent announcements and the potential of hybrid solutions for secure communication in the quantum era.
Biosystems and Human Performance Computing in the AI Revolution
Explore AI's impact on workforce and warfare, focusing on biosystems for optimizing human performance in complex environments through computational biology and XR technologies.
Quantization for Efficient Machine Learning - Part I - Lecture 5
Explore quantization techniques for efficient machine learning, covering fundamental concepts and practical applications in neural network optimization.
EfficientML.ai - Quantization Part I - Lecture 5
Explore quantization techniques for efficient machine learning, focusing on part I of the topic in this MIT lecture by Prof. Song Han.
EfficientML.ai Lecture - Introduction to Efficient Machine Learning
Explore cutting-edge techniques for optimizing machine learning models, focusing on efficiency and performance in AI applications.
Pruning and Sparsity in Machine Learning - Part II (EfficientML.ai Lecture 4)
Explore advanced pruning techniques and sparsity concepts for efficient machine learning, focusing on practical applications and cutting-edge research.
Basics of Neural Networks - EfficientML.ai Lecture 2
Explore neural network fundamentals, including architecture, training, and optimization techniques, in this comprehensive MIT lecture on efficient machine learning.
EfficientML.ai: Introduction to Efficient Machine Learning - Lecture 1
Explore efficient machine learning techniques and their applications in this comprehensive introduction to EfficientML.ai from MIT's Prof. Song Han.
EfficientML.ai: Introduction to Efficient Machine Learning - Lecture 1
Explore efficient machine learning techniques and their applications in this introductory lecture from MIT's EfficientML course, led by Prof. Song Han.
Efficient 3D Perception for Autonomous Vehicles
Explore cutting-edge techniques for efficient 3D perception in autonomous vehicles, including multi-sensor fusion, accelerated view transformation, and real-time performance on edge GPUs.
Efficient Transformers - Lecture 20
Explore efficient transformer architectures for optimized deep learning on resource-constrained devices. Learn techniques to enhance performance and reduce computational demands.
Efficient Transformers - Lecture 20
Explore efficient transformer architectures for optimized deep learning on resource-constrained devices. Learn techniques to enhance performance and reduce computational demands.
TinyEngine - Efficient Training and Inference on Microcontrollers - Lecture 17
Explore TinyEngine library for efficient neural network training and inference on microcontrollers. Learn techniques to optimize deep learning for resource-constrained devices.
Programming Language as Language
Explore programming languages as human languages, examining their structural elements, cognitive impacts, and role in shaping developer communities and mental models.
Scaling Up Machine Learning Without Tears - Programming Languages' Role
Explore techniques for scaling machine learning workloads across accelerators, focusing on partitioning, parallelism, and programming language concepts to optimize performance and hardware utilization.
Bridging Informal and Formal Mathematical Reasoning with Neural Language Models
Explore neural language models for mathematical reasoning, bridging informal and formal approaches through NaturalProver and Draft, Sketch, and Prove techniques.
Probabilistic Programming Tutorial - Part 2
Explore advanced concepts in probabilistic programming, focusing on practical applications and techniques for scientific computing and data analysis.
情報学と戯れる - 京都大学最終講義 2018
情報学の進化と応用について、京都大学の美濃導彦教授による最終講義で深い洞察と経験を共有する90分の旅へ出発。