Deep Learning and the Relational Algebra
Exploring the integration of machine learning inference in query processing, including tensor operations and autograd in relational algebra, with insights from early research experiences.
LLMOps: Comparison of OpenVino, ONNX, TensorRT, and PyTorch Inference
Explore model conversion to ONNX, OpenVino, and Tensor-RT formats for optimized CPU and GPU inference, comparing performance against native PyTorch.
LLMOps: Using Nvidia TensorRT SDK for GPU Inference
Optimize GPU inference using Nvidia TensorRT SDK. Convert models, compare throughput, and explore batch sizes, data precision, and runtime options for enhanced performance.
Learning Neural Network Hyperparameters for Machine Translation - 2019
Explore neural network hyperparameter optimization for machine translation, focusing on efficient methods to learn parameters during training without extensive grid searches.
An Overview of Deep Learning Frameworks and Introduction to PyTorch
Explore deep learning frameworks, focusing on PyTorch. Learn about research workflows, data processing, and model building from Facebook AI researcher Soumith Chintala.
AI from Cloud to Edge - PyTorch Conference 2024 Recap, Demos, and Learning Paths
Explore AI innovations from cloud to edge, featuring PyTorch Conference 2024 highlights, KleidiAI-accelerated demos, and new learning paths for developers in Arm's ecosystem.
High Performance Machine Learning, Deep Learning, and Data Science - Principles and Practice
Master high-performance computing principles for machine learning and deep learning, focusing on practical implementation strategies and advanced techniques for optimizing data science workflows.
NYU Deep Learning
Comprehensive deep learning course covering neural networks, convolutional and recurrent architectures, energy-based models, self-supervised learning, and advanced topics like transformers and graph networks.
¡Escuincla Babosa! A Python Deep Learning Telenovela
Explora cómo crear guiones de telenovelas usando redes neuronales y aprendizaje profundo en Python, comparando frameworks como Keras, PyTorch y TensorFlow para generar drama y giros argumentales.
Develop ML Interactive GPU-Workflows with Visual Studio Code, Docker and Dockerhub
Learn to streamline GPU-enabled ML workflows using Visual Studio Code, Docker, and Dockerhub. Overcome CUDA errors and version conflicts while developing in containers with seamless GPU integration.
PyTorch - Fast Differentiable Dynamic Graphs in Python
Explore PyTorch's dynamic deep learning framework, its high-performance design, and tensor compiler for faster AI research and development using GPUs and CPUs.
Refurbish Your Training Data - Reusing Partially Augmented Samples for Faster Deep Neural Network Training
Innovative data refurbishing technique accelerates deep neural network training while maintaining model generalization by reusing partially augmented samples and optimizing CPU-GPU overlap.
Habitat - A Runtime-Based Computational Performance Predictor for Deep Neural Network Training
Explore a runtime-based technique for predicting GPU performance in deep neural network training, aiding cost-efficient hardware selection for researchers and practitioners.
Client Side Deep Learning Optimization with PyTorch
Explore real-time computer vision on mobile devices using PyTorch. Learn model porting, optimization techniques, and performance benchmarking for client-side deep learning applications.
Using Kubernetes for Machine Learning Frameworks
Learn to leverage Kubernetes for training and deploying machine learning models using TensorFlow, Apache MXNet, and PyTorch. Explore isolation, auto-scaling, and GPU support for efficient ML workflows.
Using Kubernetes for Machine Learning Frameworks
Explore Kubernetes for machine learning frameworks, covering setup, training, and inference phases. Learn to deploy TensorFlow, Apache MXNet, and PyTorch on Kubernetes clusters for efficient ML model production.
Scaling Machine Learning with Spark
Learn to build scalable machine learning solutions with Apache Spark. Explore distributed computing, MLflow, feature engineering, and integrating Spark with deep learning frameworks like TensorFlow and PyTorch.
Graph Neural Networks for Information Extraction with PyTorch
Explore graph neural networks for information extraction using PyTorch, focusing on applications in NLP and understanding tabular documents through spatial encoding of word relationships.
Scaling Machine Learning with Spark
Learn to build scalable machine learning solutions using Apache Spark, MLlib, MLflow, TensorFlow, and PyTorch. Explore distributed computing concepts, manage ML lifecycles, and combine Spark with deep learning for end-to-end workflows.
TC-GNN - Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs
Innovative framework accelerating graph neural networks using GPU Tensor Core Units, reconciling sparse GNN computation with dense TCUs for improved performance in graph-based machine learning.
Neural Networks Interactively - Right in Your Browser
Explore neural networks interactively in your browser with TensorFlow.js. Learn about applications, demos, and privacy-focused machine learning projects through hands-on examples and visualizations.