#Deep Learning Ai Tutorials

Here are the best FREE AI Courses, from Beginner to Advanced... #ai #learning #motivation #success

6 YouTube Channels to learn everything around AI ✅👇 1.⁠ ⁠Matt Wolfe AI Channel - https://www.youtube.com/@mreflow 2.⁠ ⁠AI Explained - https://www.youtube.com/@aiexplained-official 3.⁠ ⁠Two Minute Papers - https://www.youtube.com/@TwoMinutePapers 4.⁠ ⁠Deep Learning.AI - https://www.youtube.com/@Deeplearningai 5.⁠ ⁠The AI Advantage - https://www.youtube.com/@aiadvantage 6.⁠ ⁠MattVidPro AI - https://www.youtube.com/@MattVidPro Follow @thedigital.indian for more such Personal & Career growth content. #ai #artificialintelligence #youtube #youtubechannel #deeplearning #personaldevelopment #growth #selfimprovement

Deep learning 😋 some ideas if you want to learn more on your journey for any stage in your career. Lots of people think that these are just for college students looking for a job but that is not the case. I even like to do projects to learn something new, a new field, topic or experiment with libraries or models I haven’t dealt with. #coding #codingprojects #swe #developer #chatgpt #deeplearning #artificialintelligence #ml #productmanager

Read our Weekly AI Newsletter—educational, easy to understand, mathematically explained, and completely free (link in bio 🔗). Backpropagation, a fundamental deep learning algorithm central to training neural networks, was popularized in the 1980s but has roots tracing back earlier. Paul Werbos is credited with formalizing backpropagation in his 1974 PhD thesis, providing a systematic way to compute gradients efficiently in multi-layer networks. Despite its potential, pioneers in the AI space such as Marvin Minsky were initially skeptical, hesitating to use backpropagation since it seemed computationally expensive and unreliable compared to simpler models. However, as computational power grew (and so did FLOPs), practical implementations showed better success—leading to the wide use of backpropagation that we know of today. It revolutionized machine learning by enabling deep networks to learn complex patterns, fueling advances in all fields of machine learning, such as computer vision, natural language processing, and more. As backpropagation is key to training machine learning models, it remains useful for any modern machine learning application—seen in LLMs like ChatGPT, DeepSeek r1, and more. C: welch labs Join our AI community for more posts like this @aibutsimple 🤖 #computervision #deeplearning #statistics #machinelearning #computerscience #coding #mathematics #math #physics #science #education

AI vs ML vs Deep Learning, explained in 30 seconds🤖🧠✨ Heard these terms thrown around but still not sure how they’re actually different? This quick breakdown will finally make it click. 🎯 Let me know in the comments what other tech buzzwords you want me to break down next! #AI #MachineLearning #DeepLearning #Tech #Career #Google #StudentLife #TechExplained #BuildInPublic #CSBasics #womenintech #softwareengineer

Roadmap to money in 2025. #coding #programming #javascript #agentai #ai #roadmap #earn

Want to start an AI automation agency? Here's how you can use ChatGPT to make it easier

Comment "free" to get them all! 1 Microsoft has launched a copilot studio agent academy teaching you how to setup agents at companies 2 Anthropic has multiple courses on ai literacy, claude code, claude api and model context protocol 3 Deep learning ai has dozens of courses teaching you everything from chatgpt, replit, hugging face, prompt engineering and much more 4 Openai academy has a ton of courses teaching the basics of chatgpt, api access, agent mode and more 5 Google has courses on ai essentials, prompting essentials and even advance courses on using vertex ai, image gen and others Follow @ishansharma7390 for more. #ai #freecourses #machinelearning #artificialintelligence #upskilling #microsoft #google #openai #chatgpt #meta

Follow and comment “YT” to get the links for all the top AI channels! 🚀 Want to learn everything about AI? Here are 5 YouTube channels that will make you an AI pro: 1️⃣ AI Explained 2️⃣ Matt Wolfe 3️⃣ Deep Learning AI 4️⃣ The AI Advantage 5️⃣ Liam Ottley 👨‍🏫 These channels are packed with valuable AI insights! Don’t miss out. 🎥 Baki, this was Day 345 of the 365 days Freedom With AI challenge. Keep following for the latest AI tools, news, and updates! #AIChannels #AITutorials #AIlearning #AICommunity #FreedomWithAI

Comment "link"-Create Automation or create API #aiautomation #aicoding

6 YouTube Channels to learn everything around AI ✅👇 1.⁠ ⁠Matt Wolfe AI Channel - https://www.youtube.com/@mreflow 2.⁠ ⁠AI Explained - https://www.youtube.com/@aiexplained-official 3.⁠ ⁠Two Minute Papers - https://www.youtube.com/@TwoMinutePapers 4.⁠ ⁠Deep Learning.AI - https://www.youtube.com/@Deeplearningai 5.⁠ ⁠The AI Advantage - https://www.youtube.com/@aiadvantage 6.⁠ ⁠MattVidPro AI - https://www.youtube.com/@MattVidPro Follow @thedigital.indian for more such Personal & Career growth content. #ai #artificialintelligence #youtube #youtubechannel #deeplearning #personaldevelopment #growth #selfimprovement

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]