The Mathematical Foundations of Deep Learning: From Rating Impossibility to Practical Existence Theorems
Explorez les fondements mathématiques de l'apprentissage profond, de l'impossibilité de notation aux théorèmes d'existence pratique. Découvrez les défis et les avancées dans ce domaine en pleine expansion.
Polynomial Time Cryptanalytic Extraction of Neural Network Models
Explore polynomial-time techniques for extracting neural network parameters, improving upon previous methods and demonstrating practical efficiency on a CIFAR10 classifier.
Geometric Deep Graph Learning: Exploring Opportunities in Different Geometric Spaces
Explore geometric deep graph learning in non-Euclidean spaces. Discover properties of hyperbolic and hyperspherical spaces for enhanced graph representation and analysis across various applications.
Prediction of Multi-Class Peptides by T-Cell Receptor Sequences with Deep Learning
Explore deep learning techniques for predicting multi-class peptides using T-cell receptor sequences, enhancing immune system understanding and potential disease treatments.
Learning Manifold-Structured Data Using Deep Neural Networks - Theory and Applications
Explore deep neural networks' ability to learn geometric data structures using multi-chart latent spaces and Chart Auto-Encoders, with theoretical foundations and practical applications.
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Explore a novel fine-tuning method for Large Language Models that improves performance without additional human-annotated data, using self-play mechanisms and theoretical guarantees.
Automating Knowledge Work with Generative AI
Explore generative AI's impact on knowledge work, learn to apply models for business solutions, and gain hands-on experience in automation techniques.
10X Faster Machine Learning: From R&D to Production
Supercharge ML experimentation with PyTorch Lightning and DVC, and streamline production using Kubeflow. Learn to overcome challenges in complex ML model development and deployment.
Deep AutoViML for TensorFlow Models and MLOps Workflows
Explore Deep AutoViML for building powerful TensorFlow models and MLOps workflows. Learn to fast prototype data pipelines and models using TF 2.4+ and Keras preprocessing layers.
Efficient Video Understanding and Generative Models - Lecture 19
Explore efficient video understanding and generative models, focusing on techniques for deploying neural networks on resource-constrained devices and optimizing deep learning applications.
Efficient Video Understanding and Generative Models - Lecture 19
Explore efficient video understanding and generative models, focusing on techniques for deploying neural networks on resource-constrained devices and optimizing deep learning applications.
EfficientML.ai: Quantization Part II - Lecture 6
Explore advanced quantization techniques for efficient machine learning, including Rooney quantization, scaling factors, and fine-tuning strategies.
Basics of Neural Networks - Lecture 2
Explore neural network fundamentals, including architecture, training, and optimization techniques for efficient machine learning model development.
Neural Architecture Search - Part I
Explore neural architecture search techniques, focusing on automated design of efficient deep learning models for various applications.
Neural Architecture Search - Part I (EfficientML.ai) - Lecture 7
Explore neural architecture search techniques for optimizing deep learning models, covering fundamentals, methods, and applications in AI efficiency.
Neural Architecture Search - Part II - Lecture 8
Explore advanced neural architecture search techniques, including gradient-based methods and hardware-aware NAS, to optimize deep learning models for efficiency and performance.
Neural Architecture Search (Part II) - Lecture 8
Explore advanced neural architecture search techniques, including evolutionary algorithms and reinforcement learning, to optimize deep learning models for efficiency and performance.
Transformer and Large Language Models - Part I - Lecture 12
Explore Transformer architecture and Large Language Models in this comprehensive MIT lecture, covering key concepts and recent advancements in AI.
Transformer and Large Language Models - Part I - Lecture 12
Explore Transformer architecture and Large Language Models in this comprehensive MIT lecture, covering key concepts and recent advancements in AI.
Transformer and Large Language Models - Part II - Lecture 13
Explore advanced concepts of Transformers and Large Language Models in this in-depth lecture from MIT's EfficientML course.
Transformer and Large Language Models - Part II - Lecture 13
Explore advanced concepts of Transformers and Large Language Models, focusing on efficiency and optimization techniques in AI and machine learning.