Accelerating the LLM Lifecycle on the Cloud
Explore PyTorch Lightning and Lightning Studios for reproducible AI development on the cloud, enabling seamless accelerated computing and solving challenges of reproducibility and collaboration.
Powering Generative AI Workloads with AMD and Open-Source ROCm
Explore cost-effective generative AI using AMD hardware and open-source ROCm. Learn to run language and image models, compare performance with NVIDIA, and discover ROCm's latest features and support.
ML Data Version Control and Reproducibility at Scale
Explore data version control techniques for efficient ML model development, addressing reproducibility challenges and enabling scalable cloud-based training using open-source tools like Langchain and TensorFlow.
Fireside Chat on PyTorch and Machine Learning
Insightful discussion on PyTorch's development, AI trends, and the future of machine learning with industry experts Soumith Chintala and Merve Noyan.
THC - Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression
Accelerates distributed deep learning with Tensor Homomorphic Compression, enabling direct aggregation of compressed values and compatibility with in-network aggregation for faster training of vision and language models.
The Fundamentals of Modern Deep Learning with PyTorch
Learn PyTorch for deep learning: train neural networks for image and text classification, explore tensor operations, automatic differentiation, and modern libraries like PyTorch Lightning for efficient GPU training.
Real-World PyTorch: From Zero to Hero in Deep Learning and LLMs - Tensors, Operations, Model Training
Comprehensive PyTorch tutorial covering tensors, operations, GPU usage, datasets, neural networks, and model training. Hands-on examples for mastering deep learning and LLMs.
Using RunPod Storage Network Volume with Automatic1111 and ControlNet - Advanced Tutorial
Comprehensive guide on using RunPod's Storage Network Volume, deploying Automatic1111 with ControlNet, and leveraging InstantID for advanced image generation and face transfer techniques.
Navigating the GENAI Frontier: Empowering Data Scientists as Ethical Innovators
Explore ethical innovation in GenAI with Alison Cossette. Learn about algorithmic literacy, transparency, and bias mitigation to become a responsible data scientist shaping AI's future for societal benefit.
Geometric Deep Learning - Graph Neural Networks and Differential Equations
Explore Graph Neural Networks, symmetry principles, and connections to differential equations, geometry, and topology in this comprehensive introduction to Geometric Deep Learning.
Visual Introduction to Transformers and GPTs - Deep Learning Chapter 5
Visual introduction to transformers and GPTs, covering key concepts like word embeddings, deep learning premise, and softmax. Explores the inner workings of these models with clear animations and explanations.
Beefy ML - Ultra-Low Power Algorithms on Cattle
Explore ultra-low power machine learning algorithms for monitoring cattle feed intake using accelerometer data, focusing on system design, implementation, and validation.
LoRAX - Serving Thousands of Fine-Tuned LLMs on a Single GPU
Explore LoRAX, an innovative LLM inference system enabling efficient serving of multiple fine-tuned models on a single GPU. Learn about dynamic adapter loading, heterogeneous batching, and scheduling techniques.
Get to Know PyTorch - A Cutting Edge Open Source AI Framework for Deep Learning
Explore PyTorch, a leading open-source AI framework. Learn deep learning fundamentals, PyTorch basics, and recent developments. Discover how to contribute and leverage PyTorch for creating neural network models.
Medical Melanoma Detection - TensorFlow U-Net Tutorial
Step-by-step guide to implementing a U-Net model for melanoma detection using TensorFlow/Keras, covering data preparation, augmentation, model building, training, testing, and result visualization.
Introduction to Machine Learning - Professor Zoubin Ghahramani, Cambridge University
Comprehensive overview of machine learning fundamentals by renowned expert, covering statistical approaches, Bayesian methods, deep learning, and automated data analysis techniques.
Data-Driven Regularisation for Solving Inverse Problems - Carola-Bibiane Schönlieb, Turing/Cambridge
Explores data-driven regularizers for inverse imaging problems, combining model-based and data-driven approaches. Covers bilevel optimization and deep neural networks for image processing.
Turing Lecture - Regulating Unreality - Deepfakes, Revenge-Pornography & Fake News
Explore the societal implications of deepfakes, including revenge porn and fake news, and discuss potential regulatory approaches to combat the spread of AI-generated misinformation.
Towards Ambient Intelligence in AI-Assisted Healthcare Spaces - Dr. Fei-Fei Li, Stanford University
Exploring AI-powered ambient intelligence in healthcare environments to enhance patient care through computer vision and human activity understanding, with real-world applications and future directions.
Towards Safe, Reliable and Trustworthy AI
Exploring challenges and frameworks for developing safe, reliable AI systems. Introduces spec-consistent machine learning to formalize desired AI behavior for real-world applications.
Interpreting Deep Neural Networks Towards Trustworthiness
Explore methods for interpreting deep neural networks, including ACD and AWD, to enhance trustworthiness in AI. Learn about feature importance, scientific interpretability, and quality control in data science.