#learn PyTorch efficiently
99 related articles
LFortran + Enzyme: A New Path to Autom…
·2 min
LFortran + Enzyme: A New Path to Automatic Differentiation for Fortran Code
LFortran + Enzyme enables automatic differentiation for decades of Fortran scientific code without rewrites. Learn the technical principles, implementation path, and impact on scientific ML.
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Dense: An Open-Source ML Workbench Bui…
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Dense: An Open-Source ML Workbench Built for Neural Network Architecture Research
Dense is an open-source ML IDE for neural network architecture research. It integrates the DeltaImportance layer and architecture visualization to help researchers iterate faster and analyze network importance during the design phase.
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Apple M7 Ultra Chip Leaked: Can 1.5TB …
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Apple M7 Ultra Chip Leaked: Can 1.5TB Unified Memory Reshape Local AI Inference?
Reddit leaks suggest Apple's M7 Ultra chip could feature up to 1.5TB unified memory. We analyze the architecture, pricing debate, bandwidth limits, and ecosystem trade-offs for local LLM inference.
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·3 min
How to Systematically Learn Machine Learning After Mastering Math and Python
Already know math and Python? Learn the complete machine learning roadmap: from data science tools and classical algorithms to deep learning frameworks and specialization.
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·2 min
Local Image-to-3D on Apple Silicon: 20 Seconds, 2GB RAM — Modelr Open-Source App Tested
A developer ports Tencent's Hunyuan3D to Apple MLX. M4 Max benchmarks: shape generation in 20s, 5.6GB peak RAM. Quantized versions run on iPhone. Modelr is the first local image-to-3D app for Apple Silicon.
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·3 min
AI-Powered OSINT in Practice: A Learning Path and Tool Guide for CS Professionals
How can OSINT practitioners with a CS background automate intelligence with AI? This guide covers computer vision, VLMs, and Agent frameworks including YOLO, SAM, and Grounding DINO.
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·2 min
Run Python in Your Browser! Why This Free Machine Learning Course Is Worth a Try
ai.coredump.digital is a completely free, no-signup, from-scratch machine learning course that runs Python directly in your browser, covering 11 ordered learning tracks with 970 quiz questions and an interview drill mode.
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·4 min
Do You Still Need to Hand-Code Machine Learning Algorithms in the AI Era? The Answer and the Method
With AI tools everywhere, is it still worth hand-coding SVM, decision trees, and other ML algorithms? This article explores the real value of hand-coding, the limits of AI tools, and smarter learning strategies for beginners in the AI era.
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·7 min
Breaking Down the Core Skills of an AI Application Engineer: From Beginner to Industrial-Grade Deployment
Knowing how to call an API doesn't make you an AI engineer. This article breaks down the complete skill structure of an AI application engineer, covering Python fundamentals, LLM fine-tuning, Agent development, and enterprise projects.
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·5 min
The Five Unsolved Challenges of Production MLOps: A Deep Dive into GPU Scheduling, Fault-Tolerant Training, and Model Observability
A deep dive into the five genuinely tough challenges of production MLOps: fault-tolerant training on Spot instances, cross-team GPU scheduling, data reproducibility, model observability, and inference cost optimization.
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·4 min
No Competitive Programming Required: How CS Students Can Pivot to AI/ML and Land Internships
How can CS students who dislike competitive programming systematically pivot to AI/ML? This guide covers skill priorities (Python/SQL/ML/deployment), portfolio strategy, Kaggle tips, and real paths to landing AI/ML internships.
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·3 min
Inside the ML Engineer Coding Assessment at Top US Tech Companies: What's Tested and How to Prepare
A firsthand account shared on Reddit reveals what a machine learning engineer online assessment (OA) at a top US tech company is really like. This article breaks down OA modules, role differences, and prep strategies for FAANG job seekers.
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·7 min
Training Machine Learning Models from Scratch: A Complete Beginner's Guide
A complete walkthrough of training machine learning models from scratch—covering problem definition, data preprocessing, algorithm selection, hyperparameter tuning, and evaluation, with tool recommendations for beginners.
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·2 min
Directly Optimizing Face Similarity: A Practical Guide to Accelerating Character LoRA Training
Ditch inefficient noise prediction and train character LoRAs by directly optimizing face similarity. Using the DRaFT differentiable reward method, training completes in 10-12 minutes on an RTX 4090, far outperforming traditional SFT. Includes open-source code.
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·4 min
INT4 ConvRot W4A4 Quantization: Running Large Image Models on Low-VRAM GPUs in ComfyUI
An in-depth look at INT4 ConvRot W4A4 quantization, covering conversions of Krea2, Qwen-Image, and other diffusion models to help ComfyUI users run large image models on 8GB GPUs.
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·5 min
Ollama Raises $65M Series B: 85% of Fortune 500 Have Already Deployed Local LLMs
Local LLM tool Ollama closes a $65M Series B, bringing total funding to $88M. With 9M developers and 85% of Fortune 500 having deployed internally, this deep dive explores why enterprises embrace local LLMs: compliance, Agent cost savings, and open-source ecosystem.
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·5 min
Essential Math for AI/ML Research: Should Undergrads Choose the Specialist Sequence or the Major Track?
Aiming for AI/ML research? How should you pick undergrad math courses? This article breaks down linear algebra, probability & statistics, and optimization, weighing the specialist sequence vs. the Major track.
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·6 min
Tencent Hunyuan HY3 Official Version Open-Sourced: The Strongest Under 300B, at 1 Yuan per Million Tokens
Tencent Hunyuan HY3 official version is open-sourced under Apache 2.0, priced as low as 1 yuan per million input tokens, with major gains in agents, reasoning, coding, and long context. On the same day, Meituan open-sourced its trillion-parameter LongCat 2.0.
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·7 min
Reverse-Engineering CUDA-checkpoint: The Technical Principles and Practice of Cracking GPU Cold Starts
An in-depth analysis of reverse-engineering Nvidia CUDA-checkpoint to accelerate GPU cold starts. Covers checkpoint/restore, Serverless GPU prospects, and VRAM snapshot challenges.
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·7 min
Complete Guide to Training Small Models on Kaggle's Free Compute: GPU Quotas and Practical Tips
In-depth guide to Kaggle's free-tier compute: P100/T4 GPU with 30 hours/week quota, 12-hour sessions, suitable models like CNN and BERT fine-tuning, plus tips like mixed precision and checkpointing to start deep learning at zero cost.
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