└── readme.md /readme.md: -------------------------------------------------------------------------------- 1 | # 🚀This repo is all you need for AI 2 | This cheatsheet serves as a practical roadmap and resource guide for anyone looking to get into GenAI or Agentic AI. 3 | 4 | > *I'm actively exploring more resources and refining this roadmap to make it more detailed and genuinely helpful — so ⭐ it if you find it valuable!* 5 | 6 | --- 7 | 8 | ## 📋 Table of Contents 9 | 10 | - [Math Foundations](#0-math-foundations) 11 | - [Python Basics](#1-python-basics) 12 | - [Streamlit](#2-streamlit) 13 | - [FastAPI](#3-fastapi) 14 | - [Machine Learning — Core Basics](#4-machine-learning--core-basics) 15 | - [Machine Learning — Deep Dive](#5-machine-learning--deep-dive) 16 | - [ML for NLP](#6-ml-for-nlp) 17 | - [Deep Learning Basics](#7-dl-basics) 18 | - [Core Deep Learning](#8-core-dl) 19 | - [DL Frameworks](#9-dl-frameworks) 20 | - [MLOps](#10-mlops) 21 | - [Transformers](#11-transformers) 22 | - [Introduction to Gen AI](#12-introduction-to-gen-ai) 23 | - [Large Language Models (LLMs) - Advanced](#13-large-language-models-llms---advanced) 24 | - [Introduction to LangChain](#14-introduction-to-langchain) 25 | - [RAG (Retrieval Augmented Generation)](#15-rag-retrieval-augmented-generation) 26 | - [Vector Databases](#16-vector-databases) 27 | - [Agentic AI](#17-agentic-ai) 28 | - [LangGraph & Advanced Agents](#18-langgraph--advanced-agents) 29 | - [Model Context Protocol (MCP)](#19-model-context-protocol-mcp) 30 | - [FastAPI (Backend for AI)](#20-fastapi-backend-for-ai) 31 | - [Resources](#-resources) 32 | 33 | --- 34 | 35 | ## **0. Math Foundations** 36 | 37 | | S.No | Topic | Description | Resources | 38 | |------|-------|-------------|-----------| 39 | | 0 | **Math for ML/DL** | Linear Algebra, Probability, Statistics, Calculus | [3Blue1Brown](https://youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) · [CampusX](https://youtube.com/playlist?list=PLKnIA16_RmvbYFaaeLY28cWeqV-3vADST) | 40 | 41 | --- 42 | 43 | ## **1. Python Basics** 44 | 45 | | S.No | Topic | Description | Resources | 46 | |------|-------|-------------|-----------| 47 | | 1 | **Python Fundamentals** | Basics, data structures, file handling, exception handling, OOP | [FreeCodeCamp](https://youtu.be/eWRfhZUzrAc) | 48 | 49 | --- 50 | 51 | ## **2. Streamlit** 52 | 53 | | S.No | Topic | Description | Resources | 54 | |------|-------|-------------|-----------| 55 | | 2 | **Streamlit Basics** | UI building, web apps for ML | [Chai aur Code](https://youtu.be/yKTEC1Y5bEQ) | 56 | 57 | --- 58 | 59 | ## **3. FastAPI** 60 | 61 | | S.No | Topic | Description | Resources | 62 | |------|-------|-------------|-----------| 63 | | 3 | **FastAPI Fundamentals** | REST APIs, async programming, model deployment | [FastAPI Docs](https://fastapi.tiangolo.com/) · [FastAPI Course](https://youtu.be/0sOvCWFmrtA) [FastAPI Course](https://youtube.com/playlist?list=PLKnIA16_RmvZ41tjbKB2ZnwchfniNsMuQ&si=QETHK5CRt8UwhmXR) | 64 | 65 | --- 66 | 67 | ## **4. Machine Learning — Core Basics** 68 | 69 | | S.No | Topic | Description | Resources | 70 | |------|-------|-------------|-----------| 71 | | 4 | **ML Fundamentals** | Classification, Regression, Pipelines, Feature Engineering | [CampusX](https://youtube.com/playlist?list=PLKnIA16_Rmvbr7zKYQuBfsVkjoLcJgxHH) · [Stanford CS229](https://youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU) | 72 | | 5 | **ML Evaluation** | Accuracy, Precision, Recall, Confusion Matrix, ROC-AUC | [StatQuest](https://www.youtube.com/@statquest) | 73 | | 6 | **Feature Scaling** | Normalization, Standardization, MinMax, Robust Scaling | [Scikit-learn Docs](https://scikit-learn.org/stable/modules/preprocessing.html) | 74 | | 7 | **Data Labeling** | Manual annotation, Label Studio, Roboflow | [Label Studio](https://labelstud.io/) · [Roboflow](https://roboflow.com/) | 75 | 76 | ### 🛠 **P1: Core ML Projects** 77 | 78 | | Project | Description | Datasets | Tech Stack | 79 | |---------|-------------|----------|------------| 80 | | **ML Classification App** | Build a classification app using sklearn + Streamlit | Iris, Titanic, MNIST | sklearn, Streamlit, pandas | 81 | | **Regression Price Predictor** | Housing price prediction with feature engineering | Boston Housing, California Housing | scikit-learn, seaborn, matplotlib | 82 | 83 | --- 84 | 85 | ## **5. Machine Learning — Deep Dive** 86 | 87 | | S.No | Topic | Description | Resources | 88 | |------|-------|-------------|-----------| 89 | | 8 | **Unsupervised ML** | Clustering (K-Means, DBSCAN, Hierarchical), Dimensionality Reduction (PCA, t-SNE, UMAP) | [StatQuest](https://www.youtube.com/@statquest) | 90 | | 9 | **Ensemble Methods** | Bagging, Boosting (XGBoost, LightGBM), Stacking | [Krish Naik](https://www.youtube.com/@krishnaik06) | 91 | | 10 | **Hyperparameter Tuning** | GridSearchCV, RandomSearch, Optuna, Bayesian Optimization | [Optuna Docs](https://optuna.org/) | 92 | | 11 | **Core ML Concepts** | Bias-variance tradeoff, Underfitting/Overfitting, Regularization (L1/L2) | [Andrew Ng ML](https://www.coursera.org/learn/machine-learning) | 93 | 94 | --- 95 | 96 | ## **6. ML for NLP** 97 | 98 | | S.No | Topic | Description | Resources | 99 | |------|-------|-------------|-----------| 100 | | 12 | **Traditional NLP** | Text preprocessing, One-Hot Encoding, Bag of Words, TF-IDF, Word2Vec | [Krish Naik](https://youtube.com/playlist?list=PLZoTAELRMXVNNrHSKv36Lr3_156yCo6Nn) | 101 | 102 | ### 🛠 **P2: NLP Projects** 103 | 104 | | Project | Description | Datasets | Tech Stack | 105 | |---------|-------------|----------|------------| 106 | | **Text Classifier** | Spam detection or sentiment analysis using BoW/TF-IDF | SMS Spam, IMDb Reviews | sklearn, NLTK, pandas | 107 | | **Word2Vec Explorer** | Visualize similarity between words using Word2Vec | Google News Word2Vec | Gensim, matplotlib, seaborn | 108 | 109 | --- 110 | 111 | ## **7. DL Basics** 112 | 113 | | S.No | Topic | Description | Resources | 114 | |------|-------|-------------|-----------| 115 | | 13 | **Deep Learning Fundamentals** | Neural Networks, Loss Functions, Optimizers, Activation Functions | [3Blue1Brown](https://youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) · [MIT 6.S191](http://introtodeeplearning.com/) [Campus X](https://youtube.com/playlist?list=PLKnIA16_RmvYuZauWaPlRTC54KxSNLtNn&si=VGTrEEzSt02wLgks)| 116 | 117 | --- 118 | 119 | ## **8. Core DL** 120 | 121 | | S.No | Topic | Description | Resources | 122 | |------|-------|-------------|-----------| 123 | | 14 | **Neural Networks & ANN** | Feedforward networks, backpropagation, gradient descent | [MIT 6.S191](https://youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) · [3Blue1Brown](https://www.youtube.com/@3blue1brown) [Campus X](https://youtube.com/playlist?list=PLKnIA16_RmvYuZauWaPlRTC54KxSNLtNn&si=VGTrEEzSt02wLgks)| 124 | | 15 | **CNN** | Convolutional Neural Networks for computer vision |[Campus X](https://youtube.com/playlist?list=PLKnIA16_RmvYuZauWaPlRTC54KxSNLtNn&si=VGTrEEzSt02wLgks) [MIT 6.S191](https://youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) · [CS231n](https://youtu.be/iOdFUJiB0Zc) | 125 | | 16 | **RNN & LSTM** | Sequential data modeling, time series |[Campus X](https://youtube.com/playlist?list=PLKnIA16_RmvYuZauWaPlRTC54KxSNLtNn&si=VGTrEEzSt02wLgks) [MIT 6.S191](https://youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) · [Colah's Blog](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) | 126 | 127 | --- 128 | 129 | ## **9. DL Frameworks** 130 | 131 | | S.No | Topic | Description | Resources | 132 | |------|-------|-------------|-----------| 133 | | 17 | **PyTorch/TensorFlow** | Tensors, model building, training loops | [PyTorch Docs](https://pytorch.org) · [TensorFlow Docs](https://www.tensorflow.org) · [PyTorch Tutorial](https://youtu.be/Z_ikDlimN6A) | 134 | 135 | ### 🛠 **P3: Deep Learning Projects** 136 | 137 | | Project | Description | Datasets | Tech Stack | 138 | |---------|-------------|----------|------------| 139 | | **Image Classifier** | Build CNN to classify cats vs dogs | Dogs vs Cats (Kaggle) | TensorFlow/Keras, PyTorch | 140 | | **Sentiment with LSTM** | Sentiment prediction using LSTM networks | IMDb, Twitter Sentiment | Keras, PyTorch, torchtext | 141 | 142 | --- 143 | 144 | ## **10. MLOps** 145 | 146 | | S.No | Topic | Description | Resources | 147 | |------|-------|-------------|-----------| 148 | | 18 | **MLOps Fundamentals** | Model versioning, experiment tracking, CI/CD for ML, monitoring | [MLOps Playlist](https://youtube.com/playlist?list=PLupK5DK91flV45dkPXyGViMLtHadRr6sp) · [MLOps Best Practices](https://ml-ops.org/) | 149 | | 19 | **Model Deployment** | Docker, cloud deployment, model serving, A/B testing | [MLOps Playlist](https://youtube.com/playlist?list=PLupK5DK91flV45dkPXyGViMLtHadRr6sp) | 150 | | 20 | **Experiment Tracking** | MLflow, Weights & Biases, model registry | [MLflow Docs](https://mlflow.org/) · [Weights & Biases](https://wandb.ai/) | 151 | 152 | --- 153 | 154 | ## **11. Transformers** 155 | 156 | | S.No | Topic | Description | Resources | 157 | |------|-------|-------------|-----------| 158 | | 21 | **Transformer Architecture** | Self-attention, Multi-head attention, Positional Encoding, Encoder-Decoder | [3Blue1Brown](https://youtu.be/wjZofJX0v4M) · [Campus X](https://youtube.com/playlist?list=PLkBMe2eZMRQ2VKEtoL0GVUrNzEiXfgj07) | 159 | | 22 | **Tokenization** | BPE, SentencePiece, GPT-2 tokenizer, Hugging Face tokenizers | [Campus X](https://youtube.com/playlist?list=PLKnIA16_RmvYuZauWaPlRTC54KxSNLtNn&si=VGTrEEzSt02wLgks) [Andrej Karpathy](https://youtu.be/ZhAz268Hdpw) · [Original Paper](https://arxiv.org/pdf/1706.03762) | 160 | 161 | --- 162 | 163 | ## **12. Introduction to Gen AI** 164 | 165 | | S.No | Topic | Description | Resources | 166 | |------|-------|-------------|-----------| 167 | | 23 | **GenAI Fundamentals** | AI vs ML vs DL vs GenAI, How GPT/LLMs are trained, LLM evolution | [Fireship](https://youtu.be/X7Zd4VyUgL0) · [Two Minute Papers](https://youtu.be/d4yCWBGFCEs) | 168 | | 24 | **LLM Evaluation** | BLEU, ROUGE, Perplexity, Human Evaluation, Benchmarks | [Hugging Face Evaluation](https://huggingface.co/docs/evaluate/index) | 169 | | 25 | **Ethics & AI Safety** | Hallucination, bias, responsible deployment, alignment | [AI Safety Course](https://course.aisafetyfundamentals.com/) | 170 | 171 | --- 172 | 173 | ## **13. Large Language Models (LLMs) - Advanced** 174 | 175 | | S.No | Topic | Description | Resources | 176 | |------|-------|-------------|-----------| 177 | | 26 | **PEFT (Parameter Efficient Fine-Tuning)** | LoRA, QLoRA, AdaLoRA, Prefix Tuning, P-Tuning | [Hugging Face PEFT](https://huggingface.co/docs/peft/index) · [LoRA Paper](https://arxiv.org/abs/2106.09685) | 178 | | 27 | **LoRA & QLoRA** | Low-Rank Adaptation, Quantized LoRA for efficient fine-tuning | [QLoRA Paper](https://arxiv.org/abs/2305.14314) · [Practical LoRA](https://youtu.be/PXWYUTMt-AU) | 179 | | 28 | **Quantization Techniques** | INT8, INT4, GPTQ, AWQ, GGML/GGUF formats | [BitsAndBytes](https://github.com/TimDettmers/bitsandbytes) · [GPTQ](https://arxiv.org/abs/2210.17323) | 180 | | 29 | **Model Compression** | Pruning, Distillation, Quantization-Aware Training | [Neural Compression](https://youtu.be/DQQsNBzp-oI) | 181 | | 30 | **Advanced Fine-tuning** | Full fine-tuning vs PEFT, Instruction tuning, RLHF basics | [Hugging Face Fine-tuning](https://huggingface.co/docs/transformers/training) | 182 | 183 | --- 184 | 185 | ## **14. Introduction to LangChain** 186 | 187 | | S.No | Topic | Description | Resources | 188 | |------|-------|-------------|-----------| 189 | | 31 | **LangChain Fundamentals** | Components, Chains, Agents, Memory | [LangChain Docs](https://python.langchain.com/docs/introduction/) · [LangChain Tutorial](https://youtu.be/X0btK9X0Xnk) | 190 | | 32 | **LLM Integration** | OpenAI, Ollama, Hugging Face, Groq integration | [Ollama Setup](https://ollama.ai/) · [Groq API](https://groq.com/) | 191 | | 33 | **Prompt Engineering** | Zero-shot, few-shot, chain-of-thought, prompt optimization | [OpenAI Cookbook](https://github.com/openai/openai-cookbook) · [Prompt Engineering Guide](https://www.promptingguide.ai/) | 192 | 193 | ### 🛠 **P4: LangChain Projects** 194 | 195 | | Project | Description | Tech Stack | 196 | |---------|-------------|------------| 197 | | **Chatbot with LangChain** | Build intelligent chatbot using LangChain + LLM + Streamlit | LangChain, Streamlit, Ollama/OpenAI | 198 | | **Document Summarizer** | Summarize PDF/Text documents with LLMs | LangChain, PyPDF, Hugging Face Transformers | 199 | 200 | --- 201 | 202 | ## **15. RAG (Retrieval Augmented Generation)** 203 | 204 | | S.No | Topic | Description | Resources | 205 | |------|-------|-------------|-----------| 206 | | 34 | **RAG Fundamentals** | Retrieval pipeline, embedding models, vector similarity | [RAG Tutorial](https://youtu.be/X0btK9X0Xnk) · [LangChain RAG](https://python.langchain.com/docs/tutorials/rag/) | 207 | | 35 | **Advanced RAG** | Multi-query retrieval, re-ranking, hybrid search | [Pinecone RAG Guide](https://www.pinecone.io/learn/retrieval-augmented-generation/) | 208 | 209 | ### 🛠 **P5: RAG Projects** 210 | 211 | | Project | Description | Tech Stack | 212 | |---------|-------------|------------| 213 | | **PDF Q&A with RAG** | Upload PDF → extract → chunk → embed → query via LLM | LangChain, FAISS, OpenAI/Groq, Streamlit | 214 | | **Multi-Document RAG** | Query across multiple documents with source attribution | ChromaDB, LangChain, sentence-transformers | 215 | 216 | --- 217 | 218 | ## **16. Vector Databases** 219 | 220 | | S.No | Topic | Description | Resources | 221 | |------|-------|-------------|-----------| 222 | | 36 | **Vector DB Fundamentals** | FAISS, ChromaDB, Pinecone, Weaviate, similarity search | [Pinecone Docs](https://docs.pinecone.io/) · [ChromaDB](https://docs.trychroma.com/) | 223 | | 37 | **Embedding Models** | sentence-transformers, OpenAI embeddings, custom embeddings | [Sentence Transformers](https://www.sbert.net/) | 224 | 225 | --- 226 | 227 | ## **17. Agentic AI** 228 | 229 | | S.No | Topic | Description | Resources | 230 | |------|-------|-------------|-----------| 231 | | 38 | **AI Agent Fundamentals** | Agent architecture, planning, tool use, memory systems | [Lilian Weng's Blog](https://lilianweng.github.io/posts/2023-06-23-agent/) | 232 | | 39 | **Tool-Using Agents** | Function calling, external APIs, code execution | [OpenAI Function Calling](https://platform.openai.com/docs/guides/function-calling) | 233 | | 40 | **Multi-Agent Systems** | Agent collaboration, communication protocols | [AutoGen](https://github.com/microsoft/autogen) · [CrewAI](https://github.com/joaomdmoura/crewAI) | 234 | | 41 | **ReAct & Planning** | Reasoning + Acting, chain-of-thought for agents | [ReAct Paper](https://arxiv.org/abs/2210.03629) | 235 | 236 | ### 🛠 **P6: Agentic AI Projects** 237 | 238 | | Project | Description | Tech Stack | 239 | |---------|-------------|------------| 240 | | **Research Assistant Agent** | AI agent that can search web, summarize, and synthesize information | LangChain, Tavily/SerpAPI, OpenAI | 241 | | **Code Review Agent** | Agent that reviews code, suggests improvements, runs tests | GitHub API, LangChain, code execution tools | 242 | 243 | --- 244 | 245 | ## **18. LangGraph & Advanced Agents** 246 | 247 | | S.No | Topic | Description | Resources | 248 | |------|-------|-------------|-----------| 249 | | 42 | **LangGraph Fundamentals** | State machines, graph-based workflows for agents | [Campus X](https://youtube.com/playlist?list=PLKnIA16_RmvYsvB8qkUQuJmJNuiCUJFPL&si=mPhMTMc3PnnHBMNG) · [LangGraph Tutorial](https://youtu.be/VaAlSpe2B30) | 250 | | 43 | **Complex Agent Workflows** | Multi-step reasoning, conditional flows, human-in-the-loop | [Campus X](https://youtube.com/playlist?list=PLKnIA16_RmvYsvB8qkUQuJmJNuiCUJFPL&si=mPhMTMc3PnnHBMNG) | 251 | | 44 | **Agent Orchestration** | Managing multiple agents, workflow optimization |[Campus X](https://youtube.com/playlist?list=PLKnIA16_RmvYsvB8qkUQuJmJNuiCUJFPL&si=mPhMTMc3PnnHBMNG) | 252 | 253 | ### 🛠 **P7: LangGraph Projects** 254 | 255 | | Project | Description | Tech Stack | 256 | |---------|-------------|------------| 257 | | **Multi-Step Research Agent** | Agent that plans research, gathers info, and creates reports | LangGraph, multiple LLMs, web search APIs | 258 | | **Customer Service Agent** | Complex customer service with escalation and human handoff | LangGraph, FastAPI, database integration | 259 | 260 | --- 261 | 262 | ## **19. Model Context Protocol (MCP)** 263 | 264 | | S.No | Topic | Description | Resources | 265 | |------|-------|-------------|-----------| 266 | | 45 | **MCP Fundamentals** | Protocol for connecting AI assistants to external data sources and tools | [Campus X](https://youtube.com/playlist?list=PLKnIA16_Rmva_oZ9F4ayUu9qcWgF7Fyc0&si=xcrbYp8oaCFopSln) · [MCP GitHub](https://github.com/modelcontextprotocol) | 267 | | 46 | **MCP Implementation** | Building MCP servers, client integration, tool development | [Krishnaik](https://youtu.be/MDBG2MOp4Go?si=pEbvdUGHK5S0euEH) | 268 | 269 | --- 270 | 271 | ## **20. FastAPI (Backend for AI)** 272 | 273 | | S.No | Topic | Description | Resources | 274 | |------|-------|-------------|-----------| 275 | | 47 | **AI Model Deployment** | Serving ML/DL models, batch processing, monitoring | [MLOps Best Practices](https://ml-ops.org/) · [MLOps Playlist](https://youtube.com/playlist?list=PLupK5DK91flV45dkPXyGViMLtHadRr6sp) | 276 | 277 | --- 278 | 279 | ## 📚 **Resources** 280 | 281 | ### 🎥 **Popular YouTube Channels** 282 | - [3Blue1Brown]([https://www.youtube.com/@krishnaik06](https://www.youtube.com/@3blue1brown)) - AIML indepth intuition 283 | - [CampusX](https://www.youtube.com/@campusx-official) - Indian ML education 284 | - [Krish Naik](https://www.youtube.com/@krishnaik06) - Comprehensive ML/AI tutorials 285 | - [IBM Technology](https://www.youtube.com/@IBMTechnology) - Fast recap while interviews 286 | - [Codebasics](https://www.youtube.com/@codebasics) - extras 287 | - [FreeCodeCamp](https://www.youtube.com/@freecodecamp) - extras 288 | 289 | - [Andrej Karpathy](http://www.youtube.com/channel/UCXUPKJO5MZQN11PqgIvyuvQ) 290 | - [Jeremy Howard](http://www.youtube.com/user/howardjeremyp) 291 | - [3Blue1Brown](http://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw) 292 | - [Serrano Academy](http://www.youtube.com/channel/UCgBncpylJ1kiVaPyP-PZauQ) 293 | - [Lex Fridman](http://www.youtube.com/user/lexfridman) 294 | - [Hamel Husain](http://www.youtube.com/channel/UC__dUuqF5w4OnbW221JxmKg) 295 | - [Machine Learning Street Talk](http://www.youtube.com/channel/UCMLtBahI5DMrt0NPvDSoIRQ) 296 | - [Jason Liu](http://www.youtube.com/channel/UCNhyF7LnO54xA-RW3shkSxA) 297 | - [StatQuest with Josh Starmer](http://www.youtube.com/user/joshstarmer) 298 | - [Dave Ebbelaar](http://www.youtube.com/channel/UCn8ujwUInbJkBhffxqAPBVQ) 299 | 300 | ### 📖 **Essential Books** 301 | - [All Top AIML Books](https://drive.google.com/drive/folders/1uZJrGQpOCv6K17YHvqk83oeBEbzpDTtG?usp=drive_link) - collection in one place 302 | 303 | 304 | ### 📄 **Key Research Papers** 305 | - [Attention Is All You Need](https://arxiv.org/pdf/1706.03762) - Original Transformer paper 306 | - [BERT: Pre-training of Deep Bidirectional Transformers](https://arxiv.org/abs/1810.04805) 307 | - [GPT-3: Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) 308 | - [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) 309 | - [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314) 310 | - [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) 311 | 312 | 313 | 314 | ## 🤝 **Contributing** 315 | 316 | Feel free to contribute to this roadmap by: 317 | - Adding new resources and tutorials 318 | - Suggesting improvements to the learning path 319 | - Sharing your project experiences 320 | - Reporting broken links or outdated content 321 | 322 | --- 323 | 324 | ## ⭐ **Star this repository if you find it helpful!** 325 | 326 | *This roadmap is continuously updated with the latest developments in Generative AI and Machine Learning.* 327 | --------------------------------------------------------------------------------