SkillWill | AI | ML | CS

5 Beginner-Friendly Projects to Learn LLMs #genai #llm 1️⃣ Building a Simple Q&A Chatbot using the GPT-4 API:- Tutorial by: freecodecamp 2️⃣ Summarizing a Video with LLMs:- Tutorial by: Gopenai 3️⃣ Building Retrieval Augmented Generation (RAG) from Scratch:- Tutorial by: Mahnoor Nauyan 4️⃣ Building Your Own Question Answering System Using RAG:- Tutorial by: Abhirami VS 5️⃣ Fine-Tuning Large Language Models (LLMs) with QLoRA:- Tutorial by: Sumit Das 🤖 Artificial Intelligence is Changing the World || Like to Learn How! ❤️

📘 𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬: 𝐀 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐆𝐮𝐢𝐝𝐞 Explore the world of LLMs with this book, offering both theory and practical applications: 𝐂𝐡𝐚𝐩𝐭𝐞𝐫𝐬 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰: 1️⃣ 𝐈𝐧𝐭𝐫𝐨 𝐭𝐨 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 – Basics of LLMs. 2️⃣ 𝐓𝐨𝐤𝐞𝐧𝐬 & 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬 – Fundamental concepts. 3️⃣ 𝐈𝐧𝐬𝐢𝐝𝐞 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫 𝐋𝐋𝐌𝐬 – Architecture deep dive. 4️⃣ 𝐓𝐞𝐱𝐭 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 – Practical insights. 5️⃣ 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠 & 𝐓𝐨𝐩𝐢𝐜 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 – Grouping related content. 6️⃣ 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 – Crafting effective prompts. 7️⃣ 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐓𝐞𝐱𝐭 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 – Tools & techniques. 8️⃣ 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐒𝐞𝐚𝐫𝐜𝐡 & 𝐑𝐀𝐆 – Search capabilities. 9️⃣ 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐋𝐋𝐌𝐬 – Integrating diverse data types. 🔟 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 – For representation & generation models. https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

StartUp Ideas - request from ycombinator.

Machine learning and Deep learning roadmap for beginners to intermediate http://Mldl.study

*List of ~ 400 ML startups* across Europe to help people looking for jobs/internships. It's mostly built automatically with python scripts, so if you want me to expand to more countries/cities just let me know https://github.com/gmberton/awesome-machine-learning-startups *Other good sources to find ML jobs/internships:* 1) RippleMatch - https://ripplematch.com/ 2) Bsky ML internshipts feed - https://bsky.app/profile/feed.marvinschmitt.com/feed/aaafleywzkwey

I would add to deep learning! 1) [https://www.fast.ai/](https://www.fast.ai/) 2) [https://end-to-end-machine-learning.teachable.com/](https://end-to-end-machine-learning.teachable.com/) 3) book grokking deep learning

ARTIFICIAL INTELLIGENCE 🤖 🎥 Siraj Raval - YouTube channel with tutorials about AI. 🎥 Sentdex - YouTube channel with programming tutorials. ⏱ Two Minute Papers - Learn AI with 5-min videos. ✍️ Andrej Karpathy - Old blog about AI, now posting on Medium. 📘 iamtrask - Machine Learning blog. 🧠 colah’s blog - Blog about neural networks. 🎓 Google Machine Learning Course - A crash course on machine learning taught by Google engineers. 🌐 Google AI - Learn from ML experts at Google.

Recommended learning pathways - Certifications https://docs.google.com/document/d/1_hma_Q19C5PJzDpqedSQseknPnc-j00KuJk6OJej-n8/edit?tab=t.0#heading=h.22zhnse521tf

Master Method for Linear decrease of n

Master Method

Floating Point Representation: • Number represented in form: S| E | M • Exponent is biased • Mantissa is normalized • Value (Explicit Normalization) = (−1)𝑠 ∗ 0. 𝑀 ∗ 2 𝐸−𝑏𝑖𝑎𝑠 • Value (Implicit Normalization) = (−1)𝑠 ∗ 1. 𝑀 ∗ 2 𝐸−𝑏𝑖𝑎𝑠 • More bits in exponent => Larger range • More bits in Mantissa => Greater precision or accuracy • Conventional representation can not store zero and very small numbers • IEEE-754 Single precision 32-bits: Bias = 127 S E M 1 8 23

Normal distribution Formulas

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GATE CS YOUTUBE PLAYLIST https://silencespeaks15.blogspot.com/p/gate-cs-playlist.html