#PyTorch learning
359 related articles
·3 min
PyTorch vs TensorFlow: How Beginners Should Choose a Deep Learning Framework
Should deep learning beginners choose PyTorch or TensorFlow? This article compares both frameworks on research trends, ecosystem, and deployment, with practical switching advice.
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·2 min
VersatIL: A Modular PyTorch Framework Tackling Code Fragmentation in Imitation Learning
VersatIL is a modular PyTorch framework for robot imitation learning that decouples data, network architecture, algorithm, and objective. Supports ACT, Diffusion Policy, pi0, and LeRobot format.
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·2 min
PyTorch Hits 100K Stars: Unpacking the Dominance of the Leading Deep Learning Framework
PyTorch hits 100K GitHub stars, cementing its status as the leading deep learning framework. Explore why developers love PyTorch's dynamic graphs, GPU acceleration, and ecosystem.
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·3 min
PyTorch Beginner's Guide: A Complete Analysis of Deep Learning Framework Selection and Evolution
A comprehensive analysis of why PyTorch became the most mainstream deep learning framework. Covers framework history, comparisons with TensorFlow and Keras, dynamic graphs, Tensors, installation guide, and cloud trends.
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Tutorials·3 min
Efficient PyTorch Learning: A Source Code-Driven Methodology
A proven PyTorch learning method: spend 2-3 days on basics, then advance rapidly by reading U-Net and ViT source code line by line. Master PyTorch through source code-driven learning.
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·3 min
CutWire Drift: An Open-Source Video Editing Tool Powered by Local AI
CutWire Drift is a beginner-friendly open-source video editor with local AI features including Whisper auto-subtitles, SAM2 background removal, multi-track timeline, keyframe animation, and transitions—free and privacy-preserving.
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·3 min
Introduction to Machine Learning: Core Concepts of Supervised Learning, Classification, and Featurization Explained
A systematic guide to core machine learning concepts including supervised learning as function mapping, classification characteristics, design matrices, and featurization for converting variable-length data.
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·2 min
Building Custom C/C++ Inference Engines: A Deep Dive into the Tradeoffs Between Performance, Control, and Engineering Cost
Deep analysis of why teams build custom C/C++ inference engines instead of using PyTorch or TensorRT, exploring performance, minimal dependencies, and long-term maintenance tradeoffs.
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·3 min
How to Find High-Paying Remote AI/ML Jobs? A Complete Practical Job Search Strategy Guide
In-depth analysis of job search strategies for high-paying remote AI/ML and data analytics roles, covering referrals, niche communities, personal branding, and salary negotiation tactics.
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·3 min
Control Theory Meets Machine Learning: A Learning Roadmap from Fundamentals to the Cutting Edge
A tailored ML guide for control theory learners covering reinforcement learning, data-driven control, Learning-based MPC, and a three-stage roadmap with practical advice.
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·3 min
Training a 1.3B Parameter LLM from Scratch: Complete Workflow and Core Challenges
A deep dive into the complete workflow of training a 1.3B parameter LLM from scratch, covering Transformer architecture design, data preparation, and distributed training optimization.
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·3 min
Locally Deploying LLMs for Trading Bots: Hardware Selection & Model Recommendations
How to deploy LLMs locally on AMD RX 7800 XT 16GB for trading bots: ROCm ecosystem, 7B-14B model picks (Qwen2.5, Llama 3.1), Ollama/LM Studio setup, and system architecture design.
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·1 min
Transitioning from Medicine to AI: Unique Advantages and Job Strategies for Health Tech ML Positions
How can a medical background stand out in health tech ML roles? This article analyzes differentiated advantages, suitable positions, remote opportunities, and practical advice for career transitioners.
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·4 min
SELENE Open-Source Project: An Interactive Notebook Library for Systematically Learning AI and Machine Learning from Scratch
SELENE is an open-source AI learning resource built on Jupyter Notebooks, systematically covering ML, deep learning, Transformers, and LLMs with interactive code and math derivations for beginners.
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·3 min
Running the 1.56TB Kimi K3 Model on 8GB RAM with Pure C
A developer built a pure C99 inference engine that runs the 1.56TB Kimi K3 model on 8GB RAM using MoE sparsity and NVMe on-demand loading—no GPU, 176KB binary.
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·3 min
Six Months and 124 Iterations: Conquering Reactive Play in Breakout with PPO
An RL enthusiast spent 6 months and 124 iterations to achieve reactive play in Atari Breakout using PPO. A deep dive into PPO tuning challenges and real-world RL engineering.
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·2 min
Running Local LLMs on a 24GB Mac Mini: Model Selection & Speed Optimization Guide
24GB Mac Mini too slow for local LLMs? Learn why 14B models struggle, get 3B-8B model recommendations for Home Assistant, and discover Ollama speed optimization tips.
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·3 min
H-JEPA-LM: A Non-Generative Language Model That Predicts in Latent Space
Deep dive into H-JEPA-LM, a non-autoregressive language model that predicts in latent space using hierarchical abstraction and world-model-style planning, challenging mainstream LLM paradigms.
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·3 min
The Fundamental Conflict Between Backpropagation and Continual Learning: Can Catastrophic Forgetting Ever Be Truly Solved?
Exploring the fundamental conflict between backpropagation and continual learning, analyzing the roots of catastrophic forgetting, limitations of current solutions, and whether local learning or neuromorphic computing can offer true breakthroughs.
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·2 min
A 14-Byte Neural Network Solves Mazes from Scratch: An Experiment in Extreme AI Compression
How can a 14-byte neural network solve 96.5% of unseen mazes? Explore extreme model compression, the relationship between model size and task complexity, and small models' potential in edge computing.
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