ולכן דמוקרטיזציה לא הייתה עבורו סיסמה - היא הייתה תוכנית עבודה בשלושה צירים: 1. להנגיש את הידע: ללמד כמה שיותר אנשים את הפרקטיקה של Deep Learning. 2. ליצור כלים: לפתח חבילות קוד (כמו ספריית fastai) שמאפשרות שימוש קל, מהיר ויעיל. 3. לייעל את הארכיטקטורה: לעבוד על מודלים קטנים ומהירים יותר שלא דורשים חוות שרתים של נאס"א. >>

people still whining about fastai install on google cloud, as if deep learning success is just a pip away. if a guide scares you, ai is moving faster than you can keep up. ai like fastai devours the lazy; the stoic human must adapt or fade.

google cloud setup for fastai is still a circus—broken links, version hell, conda ritual fixes. if you can’t even launch a basic jupyter notebook, maybe ai isn’t for you. the future will not slow down for anyone—humans must adapt or become obsolete.

Görsel yapay zeka platformu Krea, yüksek kaliteli görselleri yaklaşık iki saniye gibi rekor bir sürede üretebilen yeni hızlı yapay zeka modeli Krea 2 Turbo'yu piyasaya sürdü. #KreaAI #Krea2Turbo #ImageGeneration #FastAI #DesignTools #CreativeWorkflow airehber.com.tr/timeline/VM0…

جمّعتلكم مجموعة رهيبة من كورسات الذكاء الاصطناعي بمنشور واحد! هون بتلاقوا روابط لمصادر من أقوى شركات AI بالعالم، مع أكاديميات وجامعات. أي حدا بيقدر يدخل عالم الهايتك تقريبًا من الصفر: 🔅 فوتوا على موقع Anthropic 🔅 تعلّموا مع Google 🔅 خذوا تدريب من NVIDIA 🔅 مركز التعلم تبع Microsoft 🔅 أكاديمية OpenAI 🔅 برنامج المهارات من IBM 🔅 دورات AWS 🔅 كورسات DeepLearningAI الاحترافية 🔅 شروحات Hugging Face 🔅 دورة FastAI 🔅 مسارات Kaggle 🔅 كورس Stanford AI 🔅 كورسات MIT OpenCourseWare المجانية 🔅 تعلّم بأسلوب Full Stack Deep Learning 🔅 مصادر DeepMind 🔅 OpenAI Cookbook 🔅 أبحاث وكود على Papers With Code 🔅 مدونة AssemblyAI وكل هذا مجاني 100%! 🆓 الروابط بالتعليق ، احفظ البوست وابعثه لصحابك 👇 #خضر_غَليون #الذكاء_الاصطناعي

🚨ATENCIÓN: He encontrado cursos GRATIS de INTELIGENCIA ARTIFICIAL. Y de las mejores empresas tecnológicas. Comparto los 20 enlaces: 1. Anthropic: anthropic.skilljar.com 2. Google: grow.google/ai 3. Meta: ai.meta.com/resources 4. NVIDIA: developer.nvidia.com/trainin… (GOATed) 5. Microsoft: learn.microsoft.com/training 6. OpenAI: academy.openai.com 7. IBM: skillsbuild.org 8. AWS: skillbuilder.aws 9. DeepLearningAI: deeplearning.ai 10. Hugging Face: huggingface.co/learn 11. FastAI: course.fast.ai 12. Kaggle Learn: kaggle.com/learn 13. Stanford AI: cs231n.stanford.edu 14. MIT OpenCourseWare: ocw.mit.edu 15. Full Stack Deep Learning: fullstackdeeplearning.com 16. DeepMind Resources: deepmind.com/learning-resour… 17. OpenAI Cookbook: github.com/openai/openai-coo… 18. Papers With Code: paperswithcode.com 19. AssemblyAI Blog: assemblyai.com/blog 20. Pinecone Learn: learn.pinecone.io Guárdatelo para luego. Sígueme para más contenido como este.

🚀 Top 20 GitHub Repositories Every ML/AI Engineer Should Know in 2026 ➤ PyTorch — github.com/pytorch/pytorch ➤ Transformers — github.com/huggingface/trans… ➤ scikit-learn — github.com/scikit-learn/scik… ➤ ML for Beginners — github.com/microsoft/ML-For-… ➤ AI for Beginners — github.com/microsoft/AI-For-… ➤ LangChain — github.com/langchain-ai/lang… ➤ LlamaIndex — github.com/run-llama/llama_i… ➤ Ollama — github.com/ollama/ollama ➤ Open WebUI — github.com/open-webui/open-w… ➤ MLOps Zoomcamp — github.com/DataTalksClub/mlo… ➤ Made With ML — github.com/GokuMohandas/Made… ➤ fastai — github.com/fastai/fastai ➤ PyTorch Lightning — github.com/Lightning-AI/pyto… ➤ Awesome Machine Learning — github.com/josephmisiti/awes… ➤ Tinygrad — github.com/tinygrad/tinygrad ➤ DeepSpeed — github.com/microsoft/DeepSpe… ➤ vLLM — github.com/vllm-project/vllm ➤ OpenHands — github.com/All-Hands-AI/Open… ➤ DSPy — github.com/stanfordnlp/dspy ➤ LLMs from Scratch — github.com/rasbt/LLMs-from-s…

You do not need expensive courses to learn AI and machine learning. Strong fundamentals in Python, statistics, deep learning, NLP, MLOps, and real projects can be built using high-quality free resources from MIT, FastAI, Hugging Face, Kaggle, and Google. #AI #MachineLearning #DeepLearning #Python #DataScience

Stop Paying for AI courses. Here you can find any AI Courses for free 1. Anthropic -- anthropic.skilljar.com/ 2. OpenAI -- academy.openai.com/ 3. Google -- grow.google/intl/en_in/ 4. IBM -- skillsbuild.org/ 5. Fast AI -- course.fast.ai/ These all worth is for free. #Anthropic #chatgpttips #openai #socialmediamarketing #FastAI #ChatGPTMagic

NO TENGO DINERO PARA APRENDER IA. Se acabaron las excusas, 20 cursos GRATIS: (de empresas TOP en IA) 1. Anthropic: anthropic.skilljar.com 2. Google: grow.google/ai 3. Meta: ai.meta.com/resources 4. NVIDIA: developer.nvidia.com/trainin… (GOATed) 5. Microsoft: learn.microsoft.com/training 6. OpenAI: academy.openai.com 7. IBM: skillsbuild.org 8. AWS: skillbuilder.aws 9. DeepLearningAI: deeplearning.ai 10. Hugging Face: huggingface.co/learn 11. FastAI: course.fast.ai 12. Kaggle Learn: kaggle.com/learn 13. Stanford AI: cs231n.stanford.edu 14. MIT OpenCourseWare: ocw.mit.edu 15. Full Stack Deep Learning: fullstackdeeplearning.com 16. DeepMind Resources: deepmind.com/learning-resour… 17. OpenAI Cookbook: github.com/openai/openai-coo… 18. Papers With Code: paperswithcode.com 19. AssemblyAI Blog: assemblyai.com/blog 20. Pinecone Learn: learn.pinecone.io Guárdatelo para luego. Envíaselo a un amig@, te lo agradecerá.

10 GITHUB REPOS THAT WILL MAKE YOU A 10X DEVELOPER. Bookmark every single one. The repos quietly turning solo devs into entire teams. 1. OpenCode Open source coding agent that runs in your terminal. Works with Claude, GPT, and local models. Free Claude Code alternative. github.com/sst/opencode 2. Plandex Terminal-based AI coding agent built for large projects. Handles tasks across millions of tokens of context. github.com/plandex-ai/plande… 3. Warp The terminal rebuilt for the AI era. Natural language commands, shared workflows, and AI debugging built in. github.com/warpdotdev/Warp 4. Ruff Python linter and formatter written in Rust. 100x faster than Flake8. Used by FastAPI, Pandas, and FastAI. github.com/astral-sh/ruff 5. Biome One toolchain that replaces ESLint, Prettier, and Babel. 25x faster formatting. Used by every modern JS team. github.com/biomejs/biome 6. Atuin Replaces your shell history with an encrypted, searchable, synced database across every machine you own. github.com/atuinsh/atuin 7. Mise Single tool that replaces nvm, pyenv, rbenv, asdf, direnv, and make. Manages every runtime and env in one config. github.com/jdx/mise 8. Coolify Open source replacement for Vercel, Heroku, and Netlify. Self-host any app, database, or service on your own VPS. github.com/coollabsio/coolif… 9. Excalidraw Hand-drawn style diagrams that look like a senior engineer sketched them on a whiteboard. Used by Meta, Stripe, and Vercel. github.com/excalidraw/excali… 10. Tabby Self-hosted AI coding assistant. Run a private GitHub Copilot inside your own infrastructure with zero data leaving your servers. github.com/TabbyML/tabby The developers shipping 5x faster than everyone else are not working harder. They installed the right repos first.

Calling Tanishq a retard😂 bro was in fastai courses when he was 12. He would clarify our doubts in the forum. Completed his Ph.D in Biomedical engineering from UC Davis at 19. He is real life Sheldon.

Digging into the PyTorch blitz and the first 3 FastAI lectures by @jeremyphoward . After manually routing gradients and handling network structures from scratch, learning the actual framework internals feels like the natural next step. Time to see how the standard abstractions manage the computational graph when things scale up. #FastAi #PyTorch #BuildInPublic

HOW TO BECOME TERRIFYINGLY GOOD AT AI IN 2026. Without wasting 1000 hours on garbage tutorials. Without fake AI “experts.” Without drowning in endless information overload. I spent weeks filtering the internet to build the ultimate AI resource stack for: • LLMs • AI Agents • MCP • Prompt Engineering • RAG • AI Engineering • Vector Databases 🧠 Videos Andrej Karpathy — Intro to LLMs youtube.com/watch?v=zjkBMFhN… LLMs from Scratch youtube.com/watch?v=9vM4p9NN… Stanford Agentic AI Overview youtube.com/watch?v=kJLiOGIe… Building Effective AI Agents youtube.com/watch?v=D7_ipDqh… AI Agents Crash Course youtube.com/watch?v=F8NKVhkZ… MCP Explained youtube.com/watch?v=kQmXtrmQ… 🗂️ Repositories Awesome AI Agents github.com/e2b-dev/awesome-a… Microsoft AI Agents for Beginners github.com/microsoft/ai-agen… Prompt Engineering Guide github.com/dair-ai/Prompt-En… Hands-On LLMs github.com/HandsOnLLM/Hands-… LangChain github.com/langchain-ai/lang… LLM Course github.com/mlabonne/llm-cour… 📚 Guides OpenAI Prompt Engineering Guide platform.openai.com/docs/gui… Building Effective Agents by Anthropic anthropic.com/engineering/bu… OpenAI Agents Guide platform.openai.com/docs/gui… MCP Documentation modelcontextprotocol.io 📖 Books Build a Large Language Model From Scratch manning.com/books/build-a-la… The LLM Engineering Handbook oreilly.com/library/view/llm… Designing Machine Learning Systems oreilly.com/library/view/des… 📄 Papers ReAct arxiv.org/abs/2210.03629 Toolformer arxiv.org/abs/2302.04761 Generative Agents arxiv.org/abs/2304.03442 Attention Is All You Need arxiv.org/abs/1706.03762 🎓 Courses HuggingFace Agents Course huggingface.co/learn/agents-… DeepLearning.AI Short Courses deeplearning.ai/short-course… Full Stack LLM Bootcamp fullstackdeeplearning.com/ll… FastAI course.fast.ai Bookmark this. Because the people learning AI deeply right now will look unfairly ahead in the next 2 years.

Yesterday I had flashback to the first fast. ai Deep Learning lesson while training a small world model in SolveIT. I had a dataset with 10min of video & actions of me driving the robot around the room. The task was to predict next frame given the previous one and the action. I began with a very small CNN to test the whole process end-to-end: 1. Load the HF data 2. Convert it to a Torch dataset 3. Write a small model training loop 4. Check the predicted results It was hard to tell if the model was any good just by looking at a single image because they are all so blurry anyway. What I did instead was to create a FastHTML (in the dialog itself) which allowed to navigate with the keyboard given an initial frame. Thanks to that I realized that the model was pretty useless and I should try something else. However, I noticed that this e2e experience mirrored exactly the first lesson Practical Deep Learning, where you trained and deployed a classifier right away. When I took that lesson I was amazed at having an example running right away. The fastai lib had a lot of nice helpers to inspect the dataset, samples, run the loop and even find new data in google. In my case, SolveIT helped doing all of that for me, while letting me ask questions along the way. In under 2h I had a first prototype. Along the way I could visualize the data & plot anything. Very nice interactive coding. Moreover, In DL lesson 2 actually you were taught how to deploy the model with Gradio. But now I just run an fasthtml app in the nb and use the model to move around the room (well, it just blurs the images but...) It feels like I've had an upgraded experience of that first 2 lessons w/o any teachers involved. Truly great experience.

If you’re serious, you should recognize these: 🧠 TensorFlow → production-scale ML 🔥 PyTorch → research flexibility 🤖 Scikit-learn → classic ML foundation ⚡ Keras → fast prototyping 🤗 Hugging Face Transformers → LLM ecosystem 👁️ OpenCV → image/video processing 🌲 XGBoost → structured data winner 🚀 fastai → practical deep learning 🔗 LangChain → agent workflows 📦 ONNX → model portability Which one are you actually using in production? 👇 Save this before you forget. 📌

10 AI/ML Frameworks Every Engineer Should Know: - TensorFlow 🔶 (Google) - PyTorch 🔥 (Meta/Research) - Scikit-learn 🤖 (Classic ML) - Keras 🧠 (Beginner friendly) - HuggingFace 🤗 (NLP/LLMs) - OpenCV 👁️ (Computer Vision) - XGBoost 🌲 (Structured data) - FastAI 🚀 (Quick prototyping) - LangChain 🔗 (LLM apps) - ONNX 📦 (Model deployment) Which one are you using most? 👇 Save this. 📌 #AI #MachineLearning #DeepLearning #Tech

If it goes well and people are interested, could very easily see myself doing a course on what I learn. Gives the same itch as me exploring fastai and teaching that