Bhoop Singh Gurjar

📚All-in-One AI Reference Guide

This webpage offers a curated, category-wise collection of resources in Artificial Intelligence—including courses, books, playlists, research papers, blogs, code snippets, and repositories .

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🚀Quick Links

Category	Category	Category
🧠 LLM Architectures	🔥 Machine Learning	✍️ GPUs
📗 RLHF	🔗 Chain of Thought	🔍 AI Math
🐭 ML	🏆 Deep Learning	🧩 NLP
🖼️ Computer Vision	🎮 Reinforcement Learning	🎨 CNNs
🔐 DPO	🔁 RNNs	🧾 Image Classification
⚡ Model Technical Paper	⛳Books	🍂LLM Reinforcement Learning
🍃Mixture of expert	🦒Fine-tuning	🪰Tensor
🧞‍♀️Supervised Learning	🐦‍🔥IISC Bangalore	🧜‍♂️AI Agent
🫀Artificial Intelligence	🧘‍♂️Prompt Engineering	🕷️Statistical
🧬Generative AI	🎡Stanford University	🦐How To Make LLM
🐍Pytorch	🐣Karpathy	🕊️How To Make LLM
🪶LLM Inference	🦭LLM-powered phone	🦟Diffusion Model
🪢Backpropagation	📀Attention	🧚‍♂️BERT
🦴Few-Shot	👩‍🚀Scaling Laws	🐿️LoRA
🦇RAG	🐺AI Youtube Channel	🫏AI Blog
😻Embedding	🐷Neural Network	🦄AGI
🐘AI beat Human	🐪Vision Transformer	🦍History
🦓 AI Learning Guide	🦖Roadmap	🐈Interview
🫎UC Berkeley University	🐲LLM alignment	🐕Reward Modeling
🐫LLM preference	🫏LLM Reasoning	🐃Positional Encoding
🐸Database	🪼Chunking	🦨Top AI Papers of the Week
🦧Topic Comparison	💫LLM from scratch	🐾N8N
🐍Agents Protocol	🤴Programming Massively Parallel Processors	🐍Princeton University
🐭Important AI Paper List	⛏️Important AI Blog

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More coming soon..

🏆Deep Learning

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📚 Deep Learning Books

#	📘 Book Name	🔗 Link
1	Deep Learning – Ian Goodfellow	Link
2	Understanding Deep Learning	Link
3	Dive into Deep Learning	Link
4	The Little Book of Deep Learning	Link
5	Grokking Deep Learning	Link
6	Practical Deep Learning for Coders – fastai	Link
7	Meta Learning – How To Learn Deep Learning And Thrive…	Link
8	David MacKay – Information Theory, Inference, and Learning Algorithms	Link

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🎓 Deep Learning Courses

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#	🎥 Course Name	🔗 Link
1	DeepLearning.AI	Link
2	NYU Deep Learning – Yann LeCun	Link
3	The Complete Mathematics of Neural Networks and Deep Learning	Link
4	Intro to Deep Learning – Sebastian Raschka	Link
5	Practical Deep Learning for Coders – fastai	Link
6	Full Stack Deep Learning – 2022	Link
7	David MacKay – Information Theory, Pattern Recognition, and Neural Networks	Link
8	UC Berkeley CS 182: Deep Learning	Link
9	MIT – Introduction to Deep Learning	Link
10	CS231n – Deep Learning for Computer Vision	Link
11	CS224d – Deep Learning for Natural Language Processing	Link
12	Machine Learning - Caltech by Yaser Abu-Mostafa (2012-2014)	Link
13	Neural networks class by Hugo Larochelle from Université de Sherbrooke (2013)	Link
14	A.I - MIT by Patrick Henry Winston (2010)	Link
15	Vision and learning - computers and brains by Shimon Ullman, Tomaso Poggio, Ethan Meyers @ MIT (2013)	Link
16	Deep Learning for Natural Language Processing - Stanford(2017)	Link
17	Machine Learning - Oxford (2014-2015)	Link
18	Deep Learning - UWaterloo by Prof. Ali Ghodsi at University of Waterloo (2015)	Link
19	Statistical Machine Learning - CMU by Prof. Larry Wasserman	Link
20	Introduction to Deep Learning by Prof. Bhiksha Raj (2017)	Link
21	Deep Learning - UC Berkeley STAT-157 by Alex Smola and Mu Li (2019)	Link

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🎨 CNNs

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#	📄 Title	🔗 Link
1	CNN from Scratch with pure Mathematical Intuition	Link
2	Convolutional Neural Network (CNN): A Complete Guide	Link
3	CNN Explainer	Link
4	ConvNetJS – Deep Learning in your browser	Link
5	Convolutional Neural Networks Explained (CNN Visualized)	Link
6	CNNs from different viewpoints	Link
7	Image Kernels	Link
8	Visualizing what ConvNets learn	Link
9	Convolutions in Image Processing	Link
10	Understanding “convolution” operations in CNN	Link
11	Convolutional Neural Networks, Explained	Link

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🔥Pytorch

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#	📄 Title	🔗 Link
1	Zero to Mastery Learn PyTorch for Deep Learning	Link
2	Learn PyTorch for deep learning in a day. Literally.	Link
3	PyTorch internals - ezyang’s blog	Link
4	MiniTorch	Link
5	PyTorch is dead. Long live JAX. - Blog	Link
6	Inside the Matrix: Visualizing Matrix Multiplication, Attention and Beyond	Link

Programming Massively Parallel Processors

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#	📄 Title	🔗 Link
1	Programming Massively Parallel Processors(2021)	Link
2	Programming Massively Parallel Processors(2019)	Link
3	Programming Massively Parallel Processors Book	Link
4	CUDA C++ Programming Guide	Link
5	How GPU Computing Works	GTC 2021	Link
6	GPU Programming: When, Why and How?	Link
7	Making Deep Learning Go Brrrr From First Principles	Link

Princeton University

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#	📄 Title	🔗 Link
1	COS 484: Natural Language Processing Spring 2025	Link

Important AI Blog

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#	📄 Title	🔗 Link
1	FlexAttention: The Flexibility of PyTorch with the Performance of FlashAttention	Link
2	Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, the World’s Largest and Most Powerful Generative Language Model	link
3	Understanding GPU Memory 1: Visualizing All Allocations over Time	link
4	Visualize and understand GPU memory in PyTorch	link
5	Data-Parallel Distributed Training of Deep Learning Models	link
6	Scaling Language Model Training to a Trillion Parameters Using Megatron	link
7	Bringing HPC Techniques to Deep Learning	link
8	Ring Attention Explained	link
9	Training your large model with DeepSpeed	link
10	Visualizing 6D Mesh Parallelism	link
11	Building Meta’s GenAI Infrastructure	link
12	100,000 H100 Clusters: Power, Network Topology, Ethernet vs InfiniBand, Reliability, Failures, Checkpointing	link
13	gpu	link
14	Mixture of Experts Explained	link
15	Go smol or go home	link
16	In the long (context) run	link
17	Introducing Async Tensor Parallelism in PyTorch	link
18	A guide to PyTorch’s CUDA Caching Allocator	link
19	Transformer Math (Part 1) - Counting Model Parameters	link
20	Activation Memory: A Deep Dive using PyTorch	link
21	Conference Talk 12: Slaying OOMs with PyTorch FSDP and torchao	link

🧠RNNs

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#	📄 Title	🔗 Link
1	Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs	Link
2	Understanding LSTM Networks	Link
3	Predict Stock Prices Using RNN: Part 1	Link
4	Recurrent Neural Networks (RNN) - Made With ML	Link
5	RNNs and LSTMs - jurafsky, stanford	Link
6	The Unreasonable Effectiveness of Recurrent Neural Networks - Karpathy	Link
7	NLP from Scratch - PyTorch	Link

📚LLM Architectures

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#	📄 Title	🔗 Link
1	Attention is all you need (Transformer) Umar Jamil	Link
2	Build a Large Language Model (From Scratch) - Sebastian Raschka	Link
3	Create a Large Language Model from Scratch with Python - Tutorial	Link
4	Intro to Transformers (slides) - giffmana	Link
5	[ML 2024] Transformers - Lucas Beyer (giffmana)	Link
6	TRANSFORMER EXPLAINER - Polo Club	Link
7	The Illustrated GPT-2 (Visualizing Transformer Language Models)	Link
8	ATTENTION IS ALL YOU NEED - Implementation	Link
9	Linear Relationships in the Transformer’s Positional Encoding	Link
10	Implement and Train ViT From Scratch for Image Recognition - PyTorch	Link
11	a smol course - huggingface	Link
12	HOW I Studied LLMs in Two Weeks: A Comprehensive Roadmap	Link
13	HOW LARGE LANGUAGE MODELS work - From zero to ChatGPT	Link
14	Building effective agents - Anthropic	Link
15	LLM VISUALIZATION	Link
16	LLM course - huggingface	Link
17	Neural Networks: Zero to Hero	Link
18	Stanford CS229 (2023)	Link
19	Building an LLM from Scratch (Sebastian Raschka, 2024)	Link
20	General Audience Large Language Models (Andrej Karpathy, 2024)	Link
21	Foundations of Large Language Models” by Tong Xiao and Jingbo Zhu	Link
22	Hands-On Large Language Models	Link
23	The Illustrated Transformer	Link

Karpathy

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#	📄 Title	🔗 Link
1	Blog	Link
2	Neural Networks: Zero to Hero	Link
3	CS231n Winter 2016	Link
4	CS231n: Convolutional Neural Networks for Visual Recognition	Link
5	EurekaLabsAI	Link
6	Github	Link

Important AI Paper List

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#	📄 Title	🔗 Link
1	FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness	Link
2	MIXED PRECISION TRAINING	Link
3	FP8-LM: Training FP8 Large Language Models	Link
4	Small-scale proxies for large-scale Transformer training instabilities	link
5	BREADTH-FIRST PIPELINE PARALLELISM	Link
6	DeepSeek-V3 Technical Report	Link
7	ZERO BUBBLE PIPELINE PARALLELISM	link
8	Mixtral of Experts	link
9	Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity	link
10	A Survey on Mixture of Experts in Large Language Models	Link
11	GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding	link
12	An Empirical Model of Large-Batch Training	link
13	Reducing Activation Recomputation in Large Transformer Models	link
14	Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism	link
15	PaLM: Scaling Language Modeling with Pathways	link
16	Gemini: A Family of Highly Capable Multimodal Models	link
17	The Llama 3 Herd of Models	Link
18	ZeRO: Memory Optimizations Toward Training Trillion Parameter Models	link
19	PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel	link
20	Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep Learning	link
21	Fast Transformer Decoding: One Write-Head is All You Need	link
22	GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints	link
23	Domino: Eliminating Communication in LLM Training via Generic Tensor Slicing and Overlapping	link
24	Ring Attention with Blockwise Transformers for Near-Infinite Context	link
25	STRIPED ATTENTION:FASTER RING ATTENTION FOR CAUSAL TRANSFORMERS	link
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How To Make LLM

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#	📄 Title	🔗 Link
1	How to Scale Your Model by google	Link
2	The Ultra-Scale Playbook: Training LLMs on GPU Clusters by Huggingface	Link
3	Tiny LLM - LLM Serving in a Week	Link

LLM from scratch

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#	📄 Title	🔗 Link
1	Llama from scratch	Link

Agents Protocol

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#	📄 Title	🔗 Link
1	Model Context Protocol (MCP) Course by HuggingFace	Link

Machine Learning

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#	📄 Title	🔗 Link
1	Machine Learning Specialization (Coursera)	Link
2	A Visual Introduction to Machine Learning	Link
3	Visual explanations of core machine learning concepts	Link
4	Papers & tech blogs by companies sharing their work on data science & machine learning in production.	Link
5	CS229: Machine Learning	Link
6	Pen and Paper Exercises in Machine Learning	Link
7	Interpretable Machine Learning	Link
8	math for data science and machine learning	Link
9	Machine Learning: Probabilistic Perspective	Link
10	XGBOOSTING	Link

Machine Learning related blog

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#	📄 Title	🔗 Link
1	What’s Really Going On in Machine Learning? Some Minimal Models	Link

Computer vision

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#	📄 Title	🔗 Link
1	Deep Learning for computer vision, by Andrej Karpathy	Link
2	Computer Vision & Deep Learning (freeCodeCamp)	Link
3	Computer Vision with Prof. Tom Yeh	Link
4	Computer vision for dummies	Link
5	Training CLIP Model from Scratch for an Fashion Image Retrieval App	Link

LLM Inference

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#	📄 Title	🔗 Link
1	How to build an LLM inference engine using C++ and CUDA from scratch without libraries	Link

N8N

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#	📄 Title	🔗 Link
1	The only video you need to Master N8N + AI agents (For complete beginners)	Link

AI Learning Guide

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#	📄 Title	🔗 Link
1	A survivor’s guide to Artificial Intelligence courses at Stanford (Updated Feb 2020)	Link

Vision Transformer

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#	📄 Title	🔗 Link
1	Vision Transformers Need Registers(2024)	Link

Roadmap

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#	📄 Title	🔗 Link
1	MLOps guide	Link

Interview

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#	📄 Title	🔗 Link
1	Introduction to Machine Learning Interviews Book	Link

AI beat Human

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#	📄 Title	🔗 Link
1	O3 beats a master-level GeoGuessr player, even with fake EXIF data	Link

LLM Reasoning

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#	📄 Title	🔗 Link
1	Understanding Reasoning LLMs by Sebastian Raschka	Link

Database

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#	📄 Title	🔗 Link
1	Relational Databases vs Vector Databases	Link

Model Technical Paper

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#	📄 Title	🔗 Link
1	DeepSeek-Prover-V2	Link
2	GPT-1 → Improving Language Understanding by Generative Pre-Training(2018)	Link
3	GPT-2 → Language Models are Unsupervised Multitask Learners (2019)	Link
4	GPT-3 → Language Models are Few-Shot Learners(2020)	Link
5	ChatGPT :Trained with RLHF – Reinforcement Learning from Human Feedback (Ouyang et al., 2022)	Link
6	GPT-4 → GPT-4 Technical Report (2023)	Link
7	Claude (Anthropic) Constitutional AI: Harmlessness from AI Feedback (2022)	Link
8	Gemini: A Family of Highly Capable Multimodal Models (2023)	Link
9	Start building with Gemini 2.5 Flash(2025)	Link
10	Gemma (Google) Gemma: Open Models for Responsible AI(2024)	Link
11	Gemma 3 Technical Report(2025)	Link
12	LLaMA Series (Meta AI) LLaMA: Open and Efficient Foundation Language Models(2023)	Liink
13	LLaMA 2: Improved training and safety (2023)	Link
14	Llama 3:The Llama 3 Herd of Models	Link
15	Llama 4:The beginning of a new era of natively multimodal AI innovation	Link
16	Mistral AI(France) Mistral 7B: Grouped-query attention (2023)	Link
17	Kimi by Moonshot AI (China) Scaling RL with LLMs: Technical Report of Kimi k1.5 (2025)	Link
18	DeepSeek(China) DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models	Link
19	DeepSeek-V3 Technical Report (2024)	Link
20	DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning	Link
21	Qwen (China) Qwen Technical Report(2023)	Link
22	Qwen2 Technical Report(2024)	Link
23	Qwen2.5 Technical Report(2024)	Link
24	Qwen2.5-Omni Technical Report Multimodel (2025)	Link
25	Qwen3: Think Deeper, Act Faster (2025)	Link
26	Phi-4-reasoning Technical Report (2025)	Link
27	Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math (2025)	Link
28	OpenAI’s GPT-3 Language Model: A Technical Overview (2020)	link

OpenAI

LLM-powered phone

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#	📄 Title	🔗 Link
1	LLM-powered phone GUI agents in phone automation	Link

Diffusion Model

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#	📄 Title	🔗 Link
1	Diffusion Models from statistical first principles	Link
2	implement Diffusion Models from scratch w/ Transformer	Link
3	Denoising Diffusion Probabilistic Models (Ho et al., 2020)	Link
4	Playlist to learn Diffusion models	Link

NLP

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#	📄 Title	🔗 Link
1	𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 Stanford University 𝗡𝗟𝗣	Link
2	NLP Demystified	Link
3	1.5 Stemming, Lemmatization, Stopwords, POS Tagging	Link
4	A curated list of resources dedicated to Natural Language Processing (NLP)	Link
5	Excited to teach Advanced NLP at CMU this semester	Link

Backpropagation

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#	📄 Title	🔗 Link
1	Learning Representations by Back-Propagating Errors (Rumelhart et al., 1986)	Link

Reward Modeling

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#	📄 Title	🔗 Link
1	Reward Modeling Part 1: Bradley-Terry Model	Link
2	An interpretable reward modeling approach	Link

LLM preference

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#	📄 Title	🔗 Link
1	Interpreting Language Model Preferences Through the Lens of Decision Trees	Link

Positional Encoding

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#	📄 Title	🔗 Link
1	Linear Relationships in the Transformer’s Positional Encoding	Link
2	Transformer Architecture: The Positional Encoding	Link

Attention

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#	📄 Title	🔗 Link
1	Attention Is All You Need (Vaswani et al., 2017)	Link
2	Implement Flash Attention Backend in SGLang - Basics and KV Cache(2025)	Link
3	Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)	Link

BERT

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#	📄 Title	🔗 Link
1	BERT: Pre-training of Deep Bidirectional Transformers (Devlin et al., 2018)	Link

Chunking

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#	📄 Title	🔗 Link
1	Chunking Strategies for LLM Applications(2023)	Link

LLM alignment

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#	📄 Title	🔗 Link
1	Alignment Guidebook	Link

Few-Shot

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#	📄 Title	🔗 Link
1	Language Models are Few-Shot Learners (Brown et al., 2020)	Link

Chain of Thought

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#	📄 Title	🔗 Link
1	Chain of Thought Prompting (Wei et al., 2022)	Link

Scaling Laws

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#	📄 Title	🔗 Link
1	Scaling Laws for Neural Language Models (Kaplan et al., 2020)	Link

AGI

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#	📄 Title	🔗 Link
1	AGI is not a milestone	Link

History

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#	📄 Title	🔗 Link
1	When ChatGPT Broke an Entire Field: An Oral History(2025)	Link
2	From Large Language Models to Reasoning Language Models - Three Eras in The Age of Computation.	Link

DPO

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#	📄 Title	🔗 Link
1	Direct Preference Optimization (Rafailov et al., 2023)	Link

LoRA

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#	📄 Title	🔗 Link
1	LoRA: Low-Rank Adaptation (Hu et al., 2021)	Link

Topic Comparison

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#	📄 Title	🔗 Link
1	Fine-Tuning vs Retrieval Augmented Generation(2023)	Link
2

RAG

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#	📄 Title	🔗 Link
1	Retrieval-Augmented Generation (Lewis et al., 2020)	Link
2	Advanced RAG: Precise Zero-Shot Dense Retrieval with HyDE	Link
3	Retrieval Augmented Generation (RAG) from Scratch — Tutorial For Dummies	Link
4	Multi-modal RAG	Link
5	Beginner’s Guide to RAG by Tom Yeh	Link
6	Retrieval Augmented Generation ,Ragas	Link
7	Simplest Method to improve RAG pipeline: Re-Ranking (2023)	link

RLHF

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#	📄 Title	🔗 Link
1	Reinforcement Learning from Human Feedback by Nathan Lambert	Link
2	RLHF: Reinforcement Learning from Human Feedback by Chip Huyen	Link

Reinforcement Learning

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#	📄 Title	🔗 Link
1	Berkeley CS 294: Deep Reinforcement Learning	Link
2	Spinning Up in Deep Reinforcement Learning - A free deep reinforcement learning course by OpenAI (2019)	Link
3	comprehensive overview of Reinforcement Learning methods	Link

AI Youtube Channel

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#	📄 Title	🔗 Link
1	𝗧𝘄𝗼 𝗠𝗶𝗻𝘂𝘁𝗲 𝗣𝗮𝗽𝗲𝗿𝘀	Link
2	𝗗𝗲𝗲𝗽𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗜	Link
3	𝗟𝗲𝘅 𝗙𝗿𝗶𝗱𝗺𝗮𝗻	Link
4	3𝗕𝗹𝘂𝗲1𝗕𝗿𝗼𝘄𝗻	Link
5	𝗔𝗻𝗱𝗿𝗲𝗷 𝗞𝗮𝗿𝗽𝗮𝘁𝗵𝘆	Link
6	𝗦𝗲𝗻𝘁𝗱𝗲𝘅	Link
7	𝗠𝗮𝘁𝘁 𝗪𝗼𝗹𝗳𝗲	Link

AI Blog

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#	📄 Title	🔗 Link
1	𝗧𝗼𝘄𝗮𝗿𝗱𝘀𝗗𝗮𝘁𝗮𝗦𝗰𝗶𝗲𝗻𝗰𝗲	Link
2	𝗢𝗽𝗲𝗻𝗔𝗜 𝗕𝗹𝗼𝗴	Link
3	𝗠𝗮𝗿𝗸𝗧𝗲𝗰𝗵𝗣𝗼𝘀𝘁	Link
4	𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱 𝗕𝗹𝗼𝗴	Link
5	𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗕𝗹𝗼𝗴	Link
6	𝗕𝗲𝗿𝗸𝗲𝗹𝗲𝘆 𝗕𝗮𝗶𝗿	Link
7	𝗛𝘂𝗴𝗴𝗶𝗻𝗴𝗳𝗮𝗰𝗲 𝗕𝗹𝗼𝗴	Link
8	google Research	Link
9	Mehmet Burak Sayıcı	Link

Embedding

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#	📄 Title	🔗 Link
1	LLM Embedding Explained	Link

AI Math

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#	📄 Title	🔗 Link
1	Matrix Calulus for Machine Learning and Beyond	Link
2	history of mathematics	Link

Neural Network

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#	📄 Title	🔗 Link
1	LLMs work by 3b1b	Link

Books

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#	📄 Title	🔗 Link
1	Python Crash course	Link
2	Cloud Data Science for Dummies	Link
3	Cost-Effective Data Pipelines	Link
4	DATA ENGINEER With Python	Link
5	Data Pipelines Pocket Reference	Link
6	Data Internals A Deep Dive into How Distributed data systems work	Link
7	Deciphering Data Architectures	Link
8	Foundations of Scalable systems	Link
9	Fundamentals of Data Engineering_ Plan and Build Robust Data Systems	Link
10	Hadoop The Definitive Guide	Link
11	Introduction to Machine Learning with Python	Link
12	SQL for Data Analysis	Link
13	Storytelling with Data_ A Data Visualization Guide for Business Professionals	Link
14	Terraform Up and Running	Link
15	The Data Engineer Skills & Tools Guide	Link
16	The Data Warehouse Toolkit	Link
17	Think Stats, 2nd Edition_ Exploratory Data Analysis	Link
18	kafka the definitive guide	Link
19	practical synthetic data generation balancing privacy and the broad availability of data	Link
20	Understanding Deep Learning	Link
21	Dive into Deep Learning	Link

LLM Reinforcement Learning

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#	📄 Title	🔗 Link
1	Asynchronous Deep Reinforcement Learning (Google Deepmind 2016)	Link
2	Reinforcement Learning from Human (OpenAI 2017)	Link
3	Proximal Policy Optimization (OpenAI 2017)	Link
4	Fine-Tuning Language Models from Human Preferences (OpenAI 2020)	Link
5	Learning to Summarize from Human Feedback (OpenAI 2022)	Link
6	Direct Preference Optimization( Stanford University 2023)	Link
7	Group Relative Policy Optimization ( DeepSeek 2024)	Link
8	Reinforcement learning with verifiable rewards (DeepSeek 2025)	Link
9	Reinforcement Learning from Human Feedback (Nathan Lambert)	Link

Mixture of expert

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#	📄 Title	🔗 Link
1	OUTRAGEOUSLY LARGE NEURAL NETWORKS: THE SPARSELY-GATED MIXTURE-OF-EXPERTS LAYER	Link

UC Berkeley University

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#	📄 Title	🔗 Link
1	Large Language Model Agents ,(Fall 2024)	Link
2	Advanced Large Language Model Agents(spring 2025)	Link
3	CS 294-131: Special Topics in Deep Learning Fall, 2016	Link
4	CS 294-131: Special Topics in Deep Learning Spring 2017	Link
5	CS 294-131: Special Topics in Deep Learning Fall 2017	Link
6	CS 294-131: Special Topics in Deep Learning Spring 2018	Link
7	CS 294-131: Trustworthy Deep Learning (Special Topics in Deep Learning) Spring 2019	Link
8	CS294/194-196: Responsible GenAI and Decentralized Intelligence Fall 2023	Link
9	CS294-267/CS194-267 Understanding Large Language Models: Foundations and Safety Spring 24	Link

Fine-tuning

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#	📄 Title	🔗 Link
1	finetune Phi-4 for free on Colab	Link
2	Understanding Parameter-Efficient Finetuning of Large Language Models: From Prefix Tuning to LLaMA-Adapters	Link
3	Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation)	Link
4	PEFT: Parameter-Efficient Fine-Tuning of Billion-Scale Models on Low-Resource Hardware	Link

Tensor

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#	📄 Title	🔗 Link
1	Tensor Product Attention Is All You Need	Link

Supervised Learning

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#	📄 Title	🔗 Link
1	Supervised Learning: A Comprehensive Guide	Link

IISC Bangalore

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#	📄 Title	🔗 Link
1	started with AI from Basics to Advance as were taught to me at IISC Bangalore as part of Mtech AI	Link

AI Agent

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#	📄 Title	🔗 Link
1	Hugging Face Agents Course	Link
2	Agents by Chip Huyen	Link
3	Large Language Model Agents MOOC, Fall 2024	Link
4	Advanced Large Language Model Agents MOOC, Spring 2025	Link

Artificial Intelligence

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#	📄 Title	🔗 Link
1	lgorithms for AI & ML	Link

Prompt Engineering

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#	📄 Title	🔗 Link
1	prompt engineering white paper	Link

Statistical

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#	📄 Title	🔗 Link
1	Practical Statistics for data scientists	Link
2	The Elements of Statistical Learning	Link
3	Naked Statistics: stripping the dread from the data	Link
4	How to Lie with Statistics	Link
5	All of Statistics	Link

Generative AI

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#	📄 Title	🔗 Link
1	Microsoft launched the best course on Generative AI	Link

Stanford University

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#	📄 Title	🔗 Link
1	STATS 202: Data Mining and Analysis	Link
2	CS109: Introduction to Probability for Computer Scientists	Link
3	CS231N: Convolutional Neural Networks for Visual Recognition	Link
4	CS224N: Natural Language Processing with Deep Learning	Link
5	CS229: Machine Learning	Link
6	CS221: Artificial Intelligence: Principles and Techniques	Link
7	CS228: Probabilistic Graphical Models: Principles and Techniques	Link
8	CS234: Reinforcement Learning	Link
9	CS238: Decision Making under Uncertainty (AA 228)	Link
10	CS224W: Machine Learning with Graphs	Link
11	CS246: Mining Massive Data Sets	Link
12	CS230: Deep Learning	Link
13	CS236: Deep Generative Models	Link
14	EE263: Introduction to Linear Dynamical Systems	Link
15	CS336: Robot Perception and Decision-Making	Link

Top AI Papers of the Week

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#	📄 Title	Week
1	1. Phi-4-Mini-Reasoning 2. Building Production-Ready AI Agents with Scalable Long-Term Memory 3. UniversalRAG 4.DeepSeek-Prover-V2 5. Kimi-Audio 6. MiMo-7B 7.Advances and Challenges in Foundation Agents 8.MAGI 9.A Survey of Efficient LLM Inference Serving 10. LLM for Engineering	(April 28 - May 4,2025)

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