PyTorch | BestHub

php中文网 Courses

May 15, 2025 · Artificial Intelligence

Why Python Dominates Data Analysis and Machine Learning: Core Tools, Full‑Stack Solutions, and Learning Path

This article explains why Python has become the leading language for data analysis and machine learning, outlines the essential libraries and frameworks, provides practical code examples, describes typical application scenarios, suggests a staged learning roadmap, and forecasts future trends such as AutoML and federated learning.

PyTorchPythonTensorFlow

0 likes · 6 min read

Why Python Dominates Data Analysis and Machine Learning: Core Tools, Full‑Stack Solutions, and Learning Path

Tencent Technical Engineering

Apr 16, 2025 · Artificial Intelligence

Understanding Transformer Architecture for Chinese‑English Translation: A Practical Guide

This practical guide walks through the full Transformer architecture for Chinese‑to‑English translation, detailing encoder‑decoder structure, tokenization and embeddings, batch handling with padding and masks, positional encodings, parallel teacher‑forcing, self‑ and multi‑head attention, and the complete forward and back‑propagation training steps.

EmbeddingPositional EncodingPyTorch

0 likes · 26 min read

Understanding Transformer Architecture for Chinese‑English Translation: A Practical Guide

Python Programming Learning Circle

Apr 3, 2025 · Artificial Intelligence

Accelerating PyTorch Model Training: Techniques, Benchmarks, and Code

This article explains how to dramatically speed up PyTorch model training using code optimizations, mixed‑precision, torch.compile, distributed data parallelism, and DeepSpeed, presenting benchmark results that show up to 11.5× acceleration on multiple GPUs while maintaining high accuracy.

Deep LearningDeepSpeedDistributed Training

0 likes · 6 min read

Accelerating PyTorch Model Training: Techniques, Benchmarks, and Code

Tencent Technical Engineering

Mar 31, 2025 · Artificial Intelligence

Step-by-Step Guide to Local Training of DeepSeek R1 on Multi‑GPU A100 Systems

This step‑by‑step tutorial shows how to set up CUDA 12.4, install required packages, prepare a JSON dataset and custom reward, troubleshoot out‑of‑memory errors, and launch DeepSeek R1 training on an 8‑GPU A100 cluster using Accelerate, Deepspeed zero‑3 and vLLM configurations.

A100CUDADeepSeek

0 likes · 9 min read

Step-by-Step Guide to Local Training of DeepSeek R1 on Multi‑GPU A100 Systems

DataFunTalk

Mar 2, 2025 · Artificial Intelligence

Implementing GRPO from Scratch with Distributed Reinforcement Learning on Qwen2.5-1.5B-Instruct

This tutorial explains how to build a distributed reinforcement‑learning pipeline using the GRPO algorithm, covering data preparation, evaluation and reward functions, multi‑GPU DataParallel implementation, and full fine‑tuning of the Qwen2.5‑1.5B‑Instruct model with PyTorch, FlashAttention2 and Weights & Biases.

AIDistributed TrainingGRPO

0 likes · 10 min read

Implementing GRPO from Scratch with Distributed Reinforcement Learning on Qwen2.5-1.5B-Instruct

Cognitive Technology Team

Feb 24, 2025 · Artificial Intelligence

Fine-Tuning Large Language Models with LoRA: A Step-by-Step Guide and Code Example

This article demonstrates the before-and-after effects of fine‑tuning a large language model, explains the concept with analogies, details hardware setup, dataset preparation, LoRA configuration, training arguments, and provides complete Python code for a pure‑framework fine‑tuning workflow.

HuggingFaceLLM fine-tuningLoRA

0 likes · 24 min read

Fine-Tuning Large Language Models with LoRA: A Step-by-Step Guide and Code Example

Python Programming Learning Circle

Feb 18, 2025 · Artificial Intelligence

Getting Started with PyTorch: Installation, Core Operations, and Practical Deep Learning Projects

This article introduces PyTorch, covering installation on CPU/GPU, basic tensor operations, automatic differentiation, building and training neural networks, data loading with DataLoader, image classification on MNIST, model deployment, and useful tips for accelerating deep‑learning workflows.

Deep LearningGPUPyTorch

0 likes · 9 min read

Getting Started with PyTorch: Installation, Core Operations, and Practical Deep Learning Projects

Python Programming Learning Circle

Jan 9, 2025 · Artificial Intelligence

Choosing Between Keras and PyTorch: A Guide for Deep Learning Beginners

This article compares Keras and PyTorch for beginners, explaining their differences, showing simple digit‑recognition code examples, and offering practical advice on how to select and transition between the two deep‑learning frameworks.

Deep LearningKerasPyTorch

0 likes · 6 min read

Choosing Between Keras and PyTorch: A Guide for Deep Learning Beginners

Python Programming Learning Circle

Jan 3, 2025 · Artificial Intelligence

Visualizing Convolutional Neural Network Features with 40 Lines of Python Code

This article demonstrates how to visualize convolutional features of a VGG‑16 network using only about 40 lines of Python code, explains the underlying concepts, walks through generating patterns by maximizing filter activations, and provides a complete implementation with hooks, loss functions, and multi‑scale optimization.

CNNDeep LearningFeature Visualization

0 likes · 15 min read

Visualizing Convolutional Neural Network Features with 40 Lines of Python Code

Python Programming Learning Circle

Dec 19, 2024 · Artificial Intelligence

Overview of Microsoft’s Open‑Source Computer Vision Recipes Library

The article introduces Microsoft’s open‑source Computer Vision Recipes library, describing its purpose, target audience, repository links, supported vision scenarios such as image classification, similarity, detection, key‑point, segmentation, action recognition, multi‑object tracking and crowd counting, and provides guidance on using PyTorch, Azure and GPU resources.

AzurePyTorchcomputer vision

0 likes · 7 min read

Overview of Microsoft’s Open‑Source Computer Vision Recipes Library

Python Programming Learning Circle

Dec 19, 2024 · Artificial Intelligence

DeepPurpose: An AI Toolkit for Accelerating COVID‑19 Drug Discovery

DeepPurpose, a PyTorch‑based AI toolkit developed by Harvard researchers, provides COVID‑19 bioassay data and 56 cutting‑edge models that enable rapid drug‑target affinity prediction, virtual screening, and drug repurposing with just a few lines of code, dramatically shortening new‑drug development cycles.

AICOVID-19DeepPurpose

0 likes · 7 min read

DeepPurpose: An AI Toolkit for Accelerating COVID‑19 Drug Discovery

Cognitive Technology Team

Nov 20, 2024 · Artificial Intelligence

Fundamentals and Implementation of Neural Networks and Transformers with PyTorch Examples

This article provides a comprehensive overview of neural network fundamentals, loss functions, activation functions, embedding techniques, attention mechanisms, multi‑head attention, residual networks, and the full Transformer encoder‑decoder architecture, illustrated with detailed PyTorch code and a practical MiniRBT fine‑tuning case for Chinese text classification.

AIDeep LearningPyTorch

0 likes · 49 min read

Fundamentals and Implementation of Neural Networks and Transformers with PyTorch Examples

DaTaobao Tech

Nov 13, 2024 · Artificial Intelligence

Understanding Neural Networks and Transformers: Principles, Implementation, and Applications

The article surveys neural networks from basic neuron operations and loss functions through deep architectures to the Transformer model, detailing embeddings, positional encoding, self‑attention, multi‑head attention, residual links, and encoder‑decoder design, and includes PyTorch code examples for linear regression, translation, and fine‑tuning Hugging Face’s MiniRBT for text classification.

AIDeep LearningNLP

0 likes · 44 min read

Understanding Neural Networks and Transformers: Principles, Implementation, and Applications

Zhuanzhuan Tech

Oct 16, 2024 · Artificial Intelligence

Optimizing TorchServe Inference Service Architecture for High‑Performance AI Deployment

This article details the engineering practice of optimizing TorchServe‑based AI inference services, covering background challenges, framework selection, GPU‑accelerated Torch‑TRT integration, CPU‑side preprocessing improvements, and deployment on Kubernetes to achieve higher throughput and lower resource consumption.

GPUOptimizationKubernetesModelServing

0 likes · 17 min read

Optimizing TorchServe Inference Service Architecture for High‑Performance AI Deployment

DataFunSummit

Oct 5, 2024 · Artificial Intelligence

Optimizing TorchRec for Large‑Scale Recommendation Systems on PyTorch

This article details the performance‑focused optimizations applied to TorchRec, PyTorch's large‑scale recommendation system library, including CUDA graph capture, multithreaded kernel launches, pinned memory copies, and input‑distribution refinements that together achieve a 2.25× speedup on MLPerf DLRM‑DCNv2 across 16 DGX H100 nodes.

CUDA GraphDistributed TrainingGPU optimization

0 likes · 11 min read

Optimizing TorchRec for Large‑Scale Recommendation Systems on PyTorch

Rare Earth Juejin Tech Community

Aug 22, 2024 · Artificial Intelligence

Understanding Faster R-CNN: Architecture, Training, and Experimental Results

This article provides an in‑depth overview of the Faster R‑CNN object detection framework, covering its background, key innovations such as the Region Proposal Network, detailed algorithmic principles, training procedures, experimental results on PASCAL VOC and MS COCO, and a reproducible PyTorch implementation.

Deep LearningFaster R-CNNPyTorch

0 likes · 14 min read

Understanding Faster R-CNN: Architecture, Training, and Experimental Results

Rare Earth Juejin Tech Community

Jun 30, 2024 · Artificial Intelligence

Spatial Attention Mechanism and Its PyTorch Implementation

This article explains the principle of spatial attention in convolutional neural networks, details the underlying algorithmic steps, and provides a complete PyTorch implementation including the attention module, full network architecture, and practical considerations for integrating spatial attention into deep learning models.

CNNDeep LearningPyTorch

0 likes · 10 min read

Spatial Attention Mechanism and Its PyTorch Implementation

Rare Earth Juejin Tech Community

Jun 16, 2024 · Artificial Intelligence

HRNet Source Code Walkthrough: Keypoint Dataset Construction, Online Data Augmentation, and Training Pipeline

This article provides a detailed, English-language walkthrough of the HRNet source code, covering how the COCO keypoint dataset is built, the online data‑augmentation techniques applied during training, and the end‑to‑end training and inference procedures for human pose estimation.

Deep LearningHRNetPyTorch

0 likes · 36 min read

HRNet Source Code Walkthrough: Keypoint Dataset Construction, Online Data Augmentation, and Training Pipeline

Practical DevOps Architecture

May 30, 2024 · Artificial Intelligence

Eight‑Week LLM and Large Model Training Course Outline

This article outlines an eight‑week curriculum covering LLM evolution, PyTorch fundamentals, CUDA training, large‑model fine‑tuning, LangChain application development, cloud‑based quantization, industry case studies, and a recruitment session, providing video resources for each topic.

AIFine-tuningLLM

0 likes · 5 min read

Eight‑Week LLM and Large Model Training Course Outline

Python Programming Learning Circle

May 11, 2024 · Artificial Intelligence

A Comprehensive Overview of Popular Python Libraries for Artificial Intelligence and Data Science

This article introduces and demonstrates more than twenty widely used Python libraries for artificial intelligence, computer vision, natural language processing, and data analysis, providing concise explanations and runnable code snippets that illustrate each library's core functionality and typical use cases.

Artificial IntelligenceNumPyPyTorch

0 likes · 29 min read

A Comprehensive Overview of Popular Python Libraries for Artificial Intelligence and Data Science