Explore tweets tagged as #PyTorch
Python’s power lies in its libraries. From NumPy and Pandas for data analysis, Matplotlib and Plotly for visualization, to TensorFlow, PyTorch, and Scikit-learn for machine learning—these tools turn ideas into real-world solutions. #Python #MachineLearning #DataScience #AI
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🚀 LMCache has officially been out for 1.5 years now! Within its success, LMCache has become the default KV-cache library for open-source LLM inference (CPU offload, P2P sharing, multi-backend storage, vLLM/SGLang integration, and more). As a PyTorch Foundation Ecosystem
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Python libraries fuel every field: Django & Flask for web, NumPy & Pandas for data, TensorFlow & PyTorch for ML, Matplotlib & Seaborn for visualization, NLTK & SpaCy for NLP, PyCryptodome for security, Scrapy for scraping, Geopandas for GIS. #Python #DataScience
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I'm looking for intern roles in research & industry of AI -final year student -worked at Arogo AI-differential diagnosis @mythyaverse-finetuning VLMs -Strong fundamentals in ML/DL/NLP -Extensively worked in Pytorch & implemented many papers from scratch - https://t.co/EUNbBsWbec
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start with PyTorch, if are planning to start with Deep learning. It talks about the usability, design patterns and implementation ideas behind the framework & ideas that build a strong foundation.
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For the past week I've been working on a report on the state of PyTorch Hardware Acceleration in 2025. PyTorch is the most widely used Deep Learning/AI framework, and it's available on nearly all hardware. As such it can serve as a perfect object of comparison between different
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Python powers every domain: TensorFlow, PyTorch, NumPy for ML; Django, Flask, Web2py for web; PyGame, Panda3D for gaming; OpenCV, Scikit-Image for vision; BeautifulSoup, Scrapy for scraping; and PyTest for automation. One ecosystem, endless innovation. #Python #AI #DataScience
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Linear Algebra ↓ Python & C++ ↓ PyTorch & Autograd ↓ Transformer Architecture ↓ CUDA Kernels ↓ Distributed Training ↓ Quantization (AWQ/GGUF) ↓ Inference Optimization (vLLM) ↓ RAG & Vector Search ↓ Agentic Patterns (ReAct/MCP) ↓ Evals & Guardrails
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In collaboration with @PyTorch team, we added transformers modeling backend to torchtitan library ! This means training any Dense model (MoE support coming soon) with torch.compile + FSDPP/TP/PP/CP out of the box with no performance drop !
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Day 30 of learning LLMs: Today I built a full Transformer-based text classifier from scratch using PyTorch + torchtext. Here’s the distilled workflow (bookmark this): 1. Build the dataset pipeline - tokenize text → build vocabulary - map tokens → indices - use pad_sequence
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Why the Linux Foundation? The Linux Foundation has taken over some massive projects, including Kubernetes, OpenSearch, the CNCF, and Pytorch.
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TOMORROW: Inside Helion: Live Q&A with Jason Ansel, @oguz_ulgen, @weifengpy, and Jongsok Choi from @Meta’s PyTorch Compiler and Helion teams. Hear how the Python-embedded DSL compiles to Triton, accelerates kernels, and what’s ahead for Helion. 🔗 https://t.co/PqFdHODyG7
#PyTorch
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Building a neural network from scratch gives you an understanding that just importing TensorFlow/Pytorch never will. This video is the best time I've spent on YouTube in ages. This is easily one of the most visually intuitive explanations of Deep Learning ever made. (With
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> completed chapter 3 mapping threads to real 2D/3D data, images, grids, matrices. starts with indexing, then moves into blur kernels where each pixel averaging its neighbors to create that smooth blur effect and matrix multiplication reading this + working with pytorch next
> started reading Programming Massively Parallel Processors: really solid read so far, understanding how GPUs actually work under the hood instead of just using them blindly 2 chapters done
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📝 Mastering PyTorch: From Linear Regression to Computer Vision As part of my journey to grind and master ML, I wanted to share some stuff I learned in PyTorch through the exercises and projects I've done. Here are the building blocks and fundamental pieces of PyTorch I wrote
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تعتبر من اهم الدورات واكثرها فائده وتعمق في مجال التعلم العميق و هندسة الذكاء الاصطناعي باستخدام Pytorch من أقوى المكتبات. متبقي كورسين للتعمق اكثر في هذه المكتبة .. الي حاب يبدا التعلم انصح بهذه الدوره 👍
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Day 1 of Deep learning ->Read chapter 10 of homl ->Revised Deep learning Concepts like Foward Pass, Backprop, BatchNorm and CNN. ->Learned about Optuna for fine tuning Pytorch models. ->Started Recurrent Neural network.
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