Large Scale Distributed Deep Learning on Kubernetes Clusters
Explore distributed deep learning deployment on Kubernetes, comparing tf-operator and mpi-operator for TensorFlow training jobs, and optimizing resource utilization for better economics in machine learning tasks.
GPU Accelerated Deep Learning for Scala 3 with Storch
Discover Storch, a new PyTorch-based library for GPU-accelerated deep learning in Scala 3. Learn about tensor operations, automatic differentiation, and neural network APIs for building enterprise-grade machine learning applications.
Coding a ChatGPT-Like Transformer From Scratch in PyTorch
Step-by-step tutorial on building a ChatGPT-like Transformer from scratch in PyTorch, covering data preparation, position encoding, attention mechanisms, and model training for natural language processing.
Executorch: Enabling and Accelerating PyTorch Models on Edge Devices
Learn about ExecuTorch, a new stack for accelerating PyTorch models on edge devices, addressing on-device ML challenges and enabling efficient, portable execution without compromising user experience.
PyTorch 2.1 - New Features and Accelerating Generative AI Models
Explore PyTorch 2.1's new features for AI model acceleration, including compile, distributed, inference, and edge technologies, with insights on optimizing generative AI models for enhanced performance.
GPT-Fast - Blazingly Fast Inference with PyTorch
Explore techniques for accelerating AI inference using PyTorch, including compile, quantization, speculative decoding, and tensor parallelism, with expert insights from Horace He.
Efficient Integration of Kubeflow Tools - ISS Data Case Study on Anomaly Detection
Explore Kubeflow's integrated tools for ML workflows through an ISS anomaly detection case study, showcasing Dask, Katib, PyTorch Operator, and KServe in a unified KFP pipeline.
FractalNet Deep Neural Network Explained
Explore FractalNet, an alternative to residual networks, through paper analysis and PyTorch implementation. Learn about fractal expansion, drop path, and performance comparisons with ResNet.
Stochastic Depth for Neural Networks - Implementation and Analysis
Explore stochastic depth in neural networks: a regularization method for residual networks that enhances training speed and test performance. Includes methodology explanation and PyTorch implementation.
GPT-4 Coding Skills: Real-time Voice Changer and Small Neural Network
Explore GPT-4o's coding capabilities through practical tests, including a real-time voice changer and small neural network implementation. Gain insights into AI's potential for software development.
Reproducing GPT-2 (124M) from Scratch - Implementation and Optimization
Comprehensive tutorial on reproducing GPT-2 (124M) from scratch, covering implementation, optimization, training setup, and results analysis. Includes practical coding and performance tuning tips.
Object Detection Using Detection Transformer (DETR) for Bone Fracture Dataset
Learn to build an advanced object detection system using DETR, covering data preparation, model implementation, training, and inference with PyTorch, Transformers, and PyTorch Lightning.
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.
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.
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.
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.
DenseNet Deep Neural Network Architecture Explained
Comprehensive explanation of DenseNet architecture, including its benefits and implementation in PyTorch. Covers theory, results, and practical coding walkthrough for deep learning enthusiasts.