#Deep Learning Framework Tutorial

Is school oppressive? George Hotz, (geohot)—hacker, entrepreneur, and engineer. Known for iOS jailbreaks, reverse engineering the PS3. He founded comma.ai and has been working on tinygrad, a deep learning framework. Watch more Hack Club AMAs at hack.af/AMA #hackclub #GeorgeHotz #geohot #innovation #AI #TechEntrepreneur #ViralVideo #interview

here’s a full roadmap for anyone who wants to get into machine learning but doesn’t know where to start. covers the math, tools, courses, and projects that actually matter— no fluff, just what’ll get you from zero to real-world skills. if you want the actual roadmap doc itself written up, either comment below or shoot me a DM, i’ll send it ASAP. hope that helps. 🤝 #study #viral #education #math #advice #university #studyhelp #cs #exam #leetcode #research #machinelearning #deeplearning

The PyTorch for Deep Learning Professional Certificate is live 🔥 This certificate, led by Laurence Moroney, teaches you how to build, optimize, and deploy deep learning systems using PyTorch, the most widely adopted deep learning framework of today. Through hands-on projects, you’ll create image classifiers, fine-tune pretrained models, and prepare optimized systems for deployment. You’ll work directly with tensors and training loops, apply computer vision and NLP using TorchVision and Hugging Face, design architectures like ResNets, Transformers, and Diffusion models, and prepare models for deployment with ONNX, MLflow, pruning, and quantization. The program consists of 3 courses: 📘 PyTorch: Fundamentals 📗 PyTorch: Techniques and Ecosystem Tools 📙 PyTorch: Advanced Architectures and Deployment Build the foundation that makes advanced AI possible. Start today. Go to the link in bio or comment "DL" to receive the link in your inbox.

If I was a beginner learning to code, I would use this Python roadmap step by step for beginners 💪 #coding #codingforbeginners #learntocode #codingtips #cs #python #computerscience #usemassive

Make PPT in 10 mins . . Go to perplexity.ai Enter prompt: I want to create a PPT on {topic name}, give me {no of slides} slides of content with images Go to gamma.app website Select - Create presentation using text Enter the data from perplexity Edit images if you want Export PPT . . #ppt #presentation #tips #students #college #life

no way it made a video 💀 #studyhacks #study #schoolhacks #school #college #backtoschool #collegehacks #ai

Deep learning looks complex from the outside… but at its core, it’s just layers of math transforming your input step by step. Every deep learning model... whether it’s recognizing faces, generating images, or powering ChatGPT is built from massive matrices filled with learnable parameters. Your input enters the first layer, gets multiplied, activated, reshaped… and as it flows through deeper layers, the model gradually extracts structure, patterns, and meaning. During training, AI adjusts millions (or billions) of these numbers by improving how each matrix transforms the data. This is how deep learning turns raw text, pixels, audio, or sensor data into real intelligence. If you’re learning AI, understanding this single idea changes everything: Neural networks don’t think... they transform. And with enough layers, those transformations become powerful. 🎥 Credit: 3Blue1Brown 📌 Follow @deeprag.ai for daily AI breakdowns, neural network visualizations, ML concepts, and cutting-edge tech explained simply. . . . . . . . . . #deeplearning #machinelearning #neuralnetworks #AIeducation #AIexplained #artificialintelligence #computervision #mlengineer #datascience #technews #aiinnovation #generativeai #pythonprogramming #deepragAI #ailearning #airesources #3blue1brown

Generative Pretrained Transformers (GPTs) are a type of advanced language model that utilize the transformer architecture, a deep learning framework introduced by Vaswani et al. in 2017. Transformers are designed to handle sequential data, like text, but unlike earlier models (such as RNNs or LSTMs), they use something called a self-attention mechanism that allows them to weigh the importance of each word in a sentence relative to all others, regardless of position—enabling better understanding of context and relationships. GPT models are “pretrained” on massive amounts of text data to learn patterns in language, and then they can be fine-tuned or directly used for tasks like writing essays, summarizing articles, generating code, or even composing poetry. For example, GPT-3.5 can generate a news article from a headline, complete a user’s sentence, or answer questions about a novel’s plot—all by predicting the next most likely word in a sequence based on context. The model outputs probabilities for a singular next word prediction, and it predicts words one-by-one based on their probabilities until the output is fully generated. C: @3blue1brown #datascientist #computerengineering #deeplearning #computerscience #math #mathematics #ml #logisticregression #machinelearning #datascience #education #coding #programming #learning #courses #bootcamp #course

🚨Comment "Framework" For guide | 🎯 Know this before learning full stack development. Follow @theharibalan for more valuable content #ITCareer #TechJobs #JobTrends #FullStack #AI #DigitalMarketing #FreeCourses #CareerAdvice #TechSkills #LearnTech #IBMTraining #DeepLearning #GoogleTraining #JobOpportunities #highdemanditjobs #highdemandjobs #digitalmarketer #fullstackdevelopment #fullstackdeveloper #ai #aigenerator #ailearning [ full stack development, html css js, front end framework, full stack development roadmap, college students, front end development roadmap, theharibalan ]

Machine Learning isn’t just theory — it actually works! 💻✨ 👉 Imagine what you could build if you learned ML step by step. 💾 Comment ML and I’ll send you my free PDF to start learning today! #MachineLearning #PythonProgramming #DeepLearning #AIcommunity #DataScienceProjects #MLBeginner #PythonDeveloper #CodeLife #TechDemo #100DaysOfCode

🤖 Roadmap Series – Day 6: How to Become an AI/ML Engineer in 2025! AI और Machine Learning में career banana चाहते हो? This reel gives you the full roadmap — from math basics to real-world AI projects 🚀 Covered in this roadmap: ✅ Essential Math & Stats: Linear Algebra, Probability, Calculus ✅ Learn Python + Libraries: NumPy, Pandas, Matplotlib ✅ Machine Learning Concepts: Supervised, Unsupervised, Clustering, Regression ✅ Deep Learning: Neural Networks, CNN, RNN with TensorFlow or PyTorch ✅ Domain Specialization: NLP, Computer Vision, GenAI ✅ Real Projects: Chatbots, Image classifiers, ML models ✅ Portfolio building + Kaggle competitions ✅ Smart AI Tools: ChatGPT, LangChain, Copilot, Google Colab 📅 Timeline: 3–4 months at 1.5–2 hrs/day Perfect for students, career switchers, and job seekers in 2025 💼 💬 Comment “Amazing” & I’ll DM you the complete roadmap 🚨 Make sure you followed or DM won’t reach you ❤ You can send gifts to Support me #aimlroadmap #machinelearningengineer #artificialintelligence #deeplearning #pythonforai #datascience #nlp #computerVision #kaggle #techcareers2025 #learnai #mlprojects #ranchofullstack #roadmapseries #airoadmap2025 #genai #chatgpt #harharmahadev🙏🌿🕉️ #jaishreeram🚩 #jaihanuman🙏

If I were to get started to learn GenAI — here’s exactly how I’d go about it 👇 (No degree, no prior experience needed. Just consistency and curiosity.) Step 1: Learn Python (Weeks 1–4) → Do “Python for Everybody” or freeCodeCamp → Practice daily for 30 minutes → Push everything to GitHub — even your messy code → Start building your proof of work from Day 1 Step 2: Get ML Foundations (Weeks 5–8) → Take Andrew Ng’s ML course (still gold) → Pick 1 project on Kaggle (Titanic is great for starters) → Train a basic model → deploy it with Streamlit → Now you’ve built your first ML app 👏 Step 3: Deep Learning Phase (Months 3–4) → Learn through fast.ai — super hands-on → Fine-tune a small transformer (like BERT) on Hugging Face → Don’t aim for perfection, aim to finish one project well Step 4: Understand Transformers & LLMs (Months 5–6) → Rebuild a mini GPT using Karpathy’s nanoGPT → This will help you actually understand self-attention, tokens, and training loops → Watch explainer videos + read blog posts to reinforce the concepts Step 5: Learn the Real LLM Stack (Months 7–9) → LangChain for chaining prompts & calling tools → LangGraph for multi-agent workflows and memory → Weaviate or LanceDB for retrieval (RAG setups) → QLoRA for fine-tuning open models → vLLM for efficient inference Step 6: Build and Publish (Months 10–12) → Choose one simple use case (like a research assistant or AI chatbot) → Build it end-to-end using the stack you’ve learned → Make a demo video, write a short architecture breakdown → Share on GitHub, LinkedIn, and Twitter — this is your new resume Portfolio project ideas: https://www.instagram.com/p/DG6l_MrShZ8/?igsh=NTc4MTIwNjQ2YQ== ( Link also in "Learn AI" highlights) [ai roadmap, llm learning path, genai career, how to learn ai, langchain tutorial, huggingface projects, python for ai, llm from scratch, build in public, ai agent builder, nanoGPT, deep learning 2025, vector dbs, fine tuning llms, ai portfolio project, ml roadmap, data science career guide, prompt engineering, ai learning journey]