AI agents added

nivu · nivu · commit f5c0c95ed4bc · 2025-06-14T01:26:33.000+05:30
diff --git a/generative_ai/LLM.md b/generative_ai/LLM.md
@@ -0,0 +1,78 @@
+
+## LLMs From Scratch Series
+
+1. Andrej Karpathy - [10 Vidoes](https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&si=EuGApF9EXdu1_an5)
+    - The spelled-out intro to neural networks and backpropagation: building micrograd
+    - The spelled-out intro to language modeling: building makemore
+    - Building makemore Part 2: MLP
+    - Building makemore Part 3: Activations & Gradients, BatchNorm
+    - Building makemore Part 4: Becoming a Backprop Ninja
+    - Building makemore Part 5: Building a WaveNet
+    - Let's build GPT: from scratch, in code, spelled out.
+    - State of GPT
+    - Let's build the GPT Tokenizer
+    - Let's reproduce GPT-2 (124M)
+
+2. StatQuest with Josh Starmer - [15 Videos](https://youtube.com/playlist?list=PLblh5JKOoLUIxGDQs4LFFD--41Vzf-ME1&si=DfkDMWz58VJgBrsD)
+    - Recurrent Neural Networks (RNNs), Clearly Explained!!!
+    - Long Short-Term Memory (LSTM), Clearly Explained
+    - Word Embedding and Word2Vec, Clearly Explained!!!
+    - Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!
+    - Attention for Neural Networks, Clearly Explained!!!
+    - Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!
+    - Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!
+    - Tensors for Neural Networks, Clearly Explained!!!
+    - Essential Matrix Algebra for Neural Networks, Clearly Explained!!!
+    - The matrix math behind transformer neural networks, one step at a time!!!
+    - The StatQuest Introduction to PyTorch
+    - Introduction to Coding Neural Networks with PyTorch and Lightning
+    - Long Short-Term Memory with PyTorch + Lightning
+    - Word Embedding in PyTorch + Lightning
+    - Coding a ChatGPT Like Transformer From Scratch in PyTorch
+3. Sebastian Raschka - [5 Videos](https://youtube.com/playlist?list=PLTKMiZHVd_2Licpov-ZK24j6oUnbhiPkm&si=9oqXgWnDumkgA176)
+    - Developing an LLM: Building, Training, Finetuning 
+    - Understanding PyTorch Buffers
+    - Finetuning Open-Source LLMs
+    - Insights from Finetuning LLMs with Low-Rank Adaptation
+    - Building LLMs from the Ground Up: A 3-hour Coding Workshop
+4. CodeEmporium - [12 Videos](https://youtube.com/playlist?list=PLTl9hO2Oobd97qfWC40gOSU8C0iu0m2l4&si=_kt_U8h_i2QtJyPj)
+    - Self Attention in Transformer Neural Networks (with Code!)
+    - Multi Head Attention in Transformer Neural Networks with Code!
+    - Positional Encoding in Transformer Neural Networks Explained
+    - Layer Normalization - EXPLAINED (in Transformer Neural Networks)
+    - Blowing up the Transformer Encoder!
+    - Transformer Encoder in 100 lines of code!
+    - Blowing up Transformer Decoder architecture
+    - Transformer Decoder coded from scratch
+    - Sentence Tokenization in Transformer Code from scratch!
+    - The complete guide to Transformer neural Networks!
+    - The complete Transformer Neural Network Code in 300 lines!
+    - Building a Translator with Transformers
+
+5. Jay Alammar
+    - Coming soon
+
+6. Luis Serrano
+    - Coming soon
+
+
+### Modue 1 - Introduction to Generative AI
+
+| Topic                                                     | References                                                                                                                                   |
+| --------------------------------------------------------- |:-------------------------------------------------------------------------------------------------------------------------------------------- |
+| Introduction to Generative AI,Importance and Applications | [Intro to Generartive AI - Google Cloud Tech▶️](https://www.youtube.com/watch?v=G2fqAlgmoPo&pp=ygUdaW50cm9kdWN0aW9uIHRvIGdlbmVyYXRpdmUgYWk%3D) |
+| Autoencoders and Variational Autoencoders (VAEs)          | [Variational Autoencoders - ArxivInsights▶️](https://www.youtube.com/watch?v=9zKuYvjFFS8&t=346s&pp=ygUXdmFyaWF0aW9uYWwgYXV0b2VuY29kZXI%3D)<br> [Autoencoders Explained Easily▶️](https://youtu.be/SSXDkfiPs7c?si=3KD2T44sQQSMFjdG)<br>[Autoencoders - Jeremy Jordan🧾 ](https://www.jeremyjordan.me/autoencoders/)  |
+|Generative Adversarial Networks (GANs)| [A Friendly Introduction to Generative Adversarial Networks (GANs) - Serrano.Academy▶️](https://www.youtube.com/watch?v=8L11aMN5KY8&t=1076s&pp=ygUER2Fucw%3D%3D) <br> [6 GAN Architectures You Really Should Know - neptune.ai🧾](https://neptune.ai/blog/6-gan-architectures)|
+|Autoregressive Models and RBMs|[Guide to Autoregressive Models- Turing🧾](https://www.turing.com/kb/guide-to-autoregressive-models)<br> [Autoregressive Diffusion Models - Yannic Kilcher▶️](https://www.youtube.com/watch?v=2h4tRsQzipQ)<br>[Restricted Boltzmann Machines (RBM)- Serrano.Academy▶️](https://www.youtube.com/watch?v=Fkw0_aAtwIw)<br>|
+|Text Generation and Language Modeling| [Text Generation-HuggingFace](https://huggingface.co/tasks/text-generation)🧾|
+
+### Module 2 - Deep Learning Based  Natural Language Processing
+| Topic | References |
+| ----- |:---------- |
+|   Word Embedding    |  [Word Embedding and Word2Vec -StatQuest](https://www.youtube.com/watch?v=viZrOnJclY0)▶️<br> [Word2Vec, GloVe, FastText- CodeEmporium](https://www.youtube.com/watch?v=9S0-OC4LFNo&t=386s&pp=ygUQV29yZCBFbWJlZGRpbmdzIA%3D%3D)▶️<br> |
+|Representation Learning|[Representation Learning Complete Guide-AIM](https://analyticsindiamag.com/a-comprehensive-guide-to-representation-learning-for-beginners/#:~:text=Representation%20learning%20is%20a%20class,them%20to%20a%20given%20activity.)🧾|
+|Sequence-to-Sequence Models,Encoder-Decoder Architectures|[Sequence-to-Sequence (seq2seq) EncoderDecoder Neural Networks - StatQuest](https://www.youtube.com/watch?v=L8HKweZIOmg&pp=ygUbU2VxdWVuY2UtdG8tU2VxdWVuY2UgTW9kZWxz)▶️<br>[EncoderDecoder Seq2Seq Models - Kriz Moses](https://medium.com/analytics-vidhya/encoder-decoder-seq2seq-models-clearly-explained-c34186fbf49b)🧾|
+|seq2seq with Attention|[Sequence to Sequence (seq2seq) and Attention - Lena Voita](https://lena-voita.github.io/nlp_course/seq2seq_and_attention.html)🧾<br>[Attention for Neural Networks-StatQuest](https://www.youtube.com/watch?v=PSs6nxngL6k&pp=ygUsU2VxdWVuY2UgdG8gU2VxdWVuY2UgKHNlcTJzZXEpIGFuZCBBdHRlbnRpb24%3D)▶️|
+|Self Attention,Transformers| [Introduction to Transformers - Andrej Karpathy](https://www.youtube.com/watch?v=XfpMkf4rD6E)▶️<br>[Attention for Neural Networks-StatQuest](https://www.youtube.com/watch?v=PSs6nxngL6k)▶️<br>[Self attention-H2O.ai](https://h2o.ai/wiki/self-attention/)🧾<br>[What are Transformer Models and how do they work?-Serrano.Academy](https://www.youtube.com/watch?v=qaWMOYf4ri8)▶️|
+|Self-Supervised Learning |[Self-Supervised Learning: The Dark Matter of Intelligence-Yannic Kilcher](https://www.youtube.com/watch?v=Ag1bw8MfHGQ)▶️<br>[Self-Supervised Learning and Its Applications-neptune.ai🧾](https://neptune.ai/blog/self-supervised-learning)|
+|Advanced NLP|[Stanford CS224N: NLP with Deep Learning▶️](https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4&pp=iAQB)<br>[Natural Language Processing: Advance Techniques🧾](https://medium.com/analytics-vidhya/natural-language-processing-advance-techniques-in-depth-analysis-b67bca5db432) |
diff --git a/generative_ai/README.md b/generative_ai/README.md
@@ -1,80 +1,2 @@
 ## Generative AI
 This is a comprehensive guide to understanding and navigating the realm of Generative AI. Generative AI has gained significant traction in recent years due to its wide range of applications across various domains. From generating realistic images to aiding in natural language processing tasks, Generative AI has revolutionized how we interact with and create content.
-
-### LLMs From Scratch Series
-
-1. Andrej Karpathy - [10 Vidoes](https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&si=EuGApF9EXdu1_an5)
-    - The spelled-out intro to neural networks and backpropagation: building micrograd
-    - The spelled-out intro to language modeling: building makemore
-    - Building makemore Part 2: MLP
-    - Building makemore Part 3: Activations & Gradients, BatchNorm
-    - Building makemore Part 4: Becoming a Backprop Ninja
-    - Building makemore Part 5: Building a WaveNet
-    - Let's build GPT: from scratch, in code, spelled out.
-    - State of GPT
-    - Let's build the GPT Tokenizer
-    - Let's reproduce GPT-2 (124M)
-
-2. StatQuest with Josh Starmer - [15 Videos](https://youtube.com/playlist?list=PLblh5JKOoLUIxGDQs4LFFD--41Vzf-ME1&si=DfkDMWz58VJgBrsD)
-    - Recurrent Neural Networks (RNNs), Clearly Explained!!!
-    - Long Short-Term Memory (LSTM), Clearly Explained
-    - Word Embedding and Word2Vec, Clearly Explained!!!
-    - Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!
-    - Attention for Neural Networks, Clearly Explained!!!
-    - Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!
-    - Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!
-    - Tensors for Neural Networks, Clearly Explained!!!
-    - Essential Matrix Algebra for Neural Networks, Clearly Explained!!!
-    - The matrix math behind transformer neural networks, one step at a time!!!
-    - The StatQuest Introduction to PyTorch
-    - Introduction to Coding Neural Networks with PyTorch and Lightning
-    - Long Short-Term Memory with PyTorch + Lightning
-    - Word Embedding in PyTorch + Lightning
-    - Coding a ChatGPT Like Transformer From Scratch in PyTorch
-3. Sebastian Raschka - [5 Videos](https://youtube.com/playlist?list=PLTKMiZHVd_2Licpov-ZK24j6oUnbhiPkm&si=9oqXgWnDumkgA176)
-    - Developing an LLM: Building, Training, Finetuning 
-    - Understanding PyTorch Buffers
-    - Finetuning Open-Source LLMs
-    - Insights from Finetuning LLMs with Low-Rank Adaptation
-    - Building LLMs from the Ground Up: A 3-hour Coding Workshop
-4. CodeEmporium - [12 Videos](https://youtube.com/playlist?list=PLTl9hO2Oobd97qfWC40gOSU8C0iu0m2l4&si=_kt_U8h_i2QtJyPj)
-    - Self Attention in Transformer Neural Networks (with Code!)
-    - Multi Head Attention in Transformer Neural Networks with Code!
-    - Positional Encoding in Transformer Neural Networks Explained
-    - Layer Normalization - EXPLAINED (in Transformer Neural Networks)
-    - Blowing up the Transformer Encoder!
-    - Transformer Encoder in 100 lines of code!
-    - Blowing up Transformer Decoder architecture
-    - Transformer Decoder coded from scratch
-    - Sentence Tokenization in Transformer Code from scratch!
-    - The complete guide to Transformer neural Networks!
-    - The complete Transformer Neural Network Code in 300 lines!
-    - Building a Translator with Transformers
-
-5. Jay Alammar
-    - Coming soon
-
-6. Luis Serrano
-    - Coming soon
-
-
-### Modue 1 - Introduction to Generative AI
-
-| Topic                                                     | References                                                                                                                                   |
-| --------------------------------------------------------- |:-------------------------------------------------------------------------------------------------------------------------------------------- |
-| Introduction to Generative AI,Importance and Applications | [Intro to Generartive AI - Google Cloud Tech▶️](https://www.youtube.com/watch?v=G2fqAlgmoPo&pp=ygUdaW50cm9kdWN0aW9uIHRvIGdlbmVyYXRpdmUgYWk%3D) |
-| Autoencoders and Variational Autoencoders (VAEs)          | [Variational Autoencoders - ArxivInsights▶️](https://www.youtube.com/watch?v=9zKuYvjFFS8&t=346s&pp=ygUXdmFyaWF0aW9uYWwgYXV0b2VuY29kZXI%3D)<br> [Autoencoders Explained Easily▶️](https://youtu.be/SSXDkfiPs7c?si=3KD2T44sQQSMFjdG)<br>[Autoencoders - Jeremy Jordan🧾 ](https://www.jeremyjordan.me/autoencoders/)  |
-|Generative Adversarial Networks (GANs)| [A Friendly Introduction to Generative Adversarial Networks (GANs) - Serrano.Academy▶️](https://www.youtube.com/watch?v=8L11aMN5KY8&t=1076s&pp=ygUER2Fucw%3D%3D) <br> [6 GAN Architectures You Really Should Know - neptune.ai🧾](https://neptune.ai/blog/6-gan-architectures)|
-|Autoregressive Models and RBMs|[Guide to Autoregressive Models- Turing🧾](https://www.turing.com/kb/guide-to-autoregressive-models)<br> [Autoregressive Diffusion Models - Yannic Kilcher▶️](https://www.youtube.com/watch?v=2h4tRsQzipQ)<br>[Restricted Boltzmann Machines (RBM)- Serrano.Academy▶️](https://www.youtube.com/watch?v=Fkw0_aAtwIw)<br>|
-|Text Generation and Language Modeling| [Text Generation-HuggingFace](https://huggingface.co/tasks/text-generation)🧾|
-
-### Module 2 - Deep Learning Based  Natural Language Processing
-| Topic | References |
-| ----- |:---------- |
-|   Word Embedding    |  [Word Embedding and Word2Vec -StatQuest](https://www.youtube.com/watch?v=viZrOnJclY0)▶️<br> [Word2Vec, GloVe, FastText- CodeEmporium](https://www.youtube.com/watch?v=9S0-OC4LFNo&t=386s&pp=ygUQV29yZCBFbWJlZGRpbmdzIA%3D%3D)▶️<br> |
-|Representation Learning|[Representation Learning Complete Guide-AIM](https://analyticsindiamag.com/a-comprehensive-guide-to-representation-learning-for-beginners/#:~:text=Representation%20learning%20is%20a%20class,them%20to%20a%20given%20activity.)🧾|
-|Sequence-to-Sequence Models,Encoder-Decoder Architectures|[Sequence-to-Sequence (seq2seq) EncoderDecoder Neural Networks - StatQuest](https://www.youtube.com/watch?v=L8HKweZIOmg&pp=ygUbU2VxdWVuY2UtdG8tU2VxdWVuY2UgTW9kZWxz)▶️<br>[EncoderDecoder Seq2Seq Models - Kriz Moses](https://medium.com/analytics-vidhya/encoder-decoder-seq2seq-models-clearly-explained-c34186fbf49b)🧾|
-|seq2seq with Attention|[Sequence to Sequence (seq2seq) and Attention - Lena Voita](https://lena-voita.github.io/nlp_course/seq2seq_and_attention.html)🧾<br>[Attention for Neural Networks-StatQuest](https://www.youtube.com/watch?v=PSs6nxngL6k&pp=ygUsU2VxdWVuY2UgdG8gU2VxdWVuY2UgKHNlcTJzZXEpIGFuZCBBdHRlbnRpb24%3D)▶️|
-|Self Attention,Transformers| [Introduction to Transformers - Andrej Karpathy](https://www.youtube.com/watch?v=XfpMkf4rD6E)▶️<br>[Attention for Neural Networks-StatQuest](https://www.youtube.com/watch?v=PSs6nxngL6k)▶️<br>[Self attention-H2O.ai](https://h2o.ai/wiki/self-attention/)🧾<br>[What are Transformer Models and how do they work?-Serrano.Academy](https://www.youtube.com/watch?v=qaWMOYf4ri8)▶️|
-|Self-Supervised Learning |[Self-Supervised Learning: The Dark Matter of Intelligence-Yannic Kilcher](https://www.youtube.com/watch?v=Ag1bw8MfHGQ)▶️<br>[Self-Supervised Learning and Its Applications-neptune.ai🧾](https://neptune.ai/blog/self-supervised-learning)|
-|Advanced NLP|[Stanford CS224N: NLP with Deep Learning▶️](https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4&pp=iAQB)<br>[Natural Language Processing: Advance Techniques🧾](https://medium.com/analytics-vidhya/natural-language-processing-advance-techniques-in-depth-analysis-b67bca5db432) |
diff --git a/generative_ai/agents.md b/generative_ai/agents.md
@@ -0,0 +1,74 @@
+## AI Agents Roadmap
+
+📌 Level 1: Learning the Basics of GenAI and RAG
+
+1. GenAI Introduction
+- Key Concepts to Learn:
+a. Overview of Generative AI and its applications.
+b. Differences between Generative and traditional AI.
+
+2. Basics of LLMs
+- Key Concepts to Learn:
+a. Transformer architecture and attention mechanisms.
+b. Tokenization and embeddings.
+
+3. Basics of Prompt Engineering
+- Key Concepts to Learn:
+a. Using zero-shot, few-shot, and chain-of-thought prompting.
+b. Techniques like temperature control for refining output.
+
+4. Data Handling and Processing
+- Key Concepts to Learn:
+a. Cleaning and structuring data for training and inference.
+b. Preprocessing techniques like tokenization and normalization.
+
+5. Introduction to API Wrappers
+- Key Concepts to Learn:
+a. Automating tasks using API calls.
+b. Basics of REST and GraphQL APIs.
+
+6. RAG Essentials
+- Key Concepts to Learn:
+a. Basics of Retrieval-Augmented Generation (RAG).
+b. Embedding-based search with vector databases like ChromaDB, Milvus.
+
+📌 Level 2: AI Agent-Focused Learning
+
+1. Introduction to AI Agents
+- Key Concepts to Learn
+a. Agent-environment interaction.
+
+2. Learn Agentic Frameworks
+- Key Concepts to Learn
+a. Agent workflows with frameworks like LangChain.
+b. Explore low-code langflow
+
+3. Building a Simple AI Agent
+- Key Concepts to Learn
+a. Creating an agent with framework 
+b. LLM APIs keys and integration
+
+4. Basics of Agentic Workflow
+- Key Concepts to Learn:
+a. Break tasks into logical steps and optimize orchestration for seamless agent collaboration.
+b. Learning to Implement robust error recovery mechanisms
+
+5. Learning About Agentic Memory
+- Key Concepts to Learn
+a. Short-term vs long-term memory vs episodic memory
+b. Storage and retrieval mechanism (vector, key-value, knowledge graph)
+
+6. Basics of Agentic Evaluation
+- Key Concepts to Learn
+a. Measuring success metrics like accuracy and response time
+b. Evaluating agent decision-making and context retention
+
+7. Basics of Multi-Agent Collaboration
+- Key Concepts:
+a. Collaboration strategies and agent dependencies
+b. Agent communication protocols
+
+8. Learning Agentic RAG
+- Key Concepts:
+a. Context handling and memory
+b. Building agentic pipelines
diff --git a/generative_ai/anthropic.md b/generative_ai/anthropic.md
@@ -0,0 +1,42 @@
+
+
+1. Prompting guide
+https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
+
+2. Claude Code: Best practices for agentic coding
+https://www.anthropic.com/engineering/claude-code-best-practices
+
+3. Building effective agents
+https://www.anthropic.com/engineering/building-effective-agents
+
+4. AI Fluency: The AI Fluency Framework
+https://www.anthropic.com/ai-fluency/overview
+
+5. Build with Claude
+https://docs.anthropic.com/en/home
+
+6. Claude for Work
+https://www.anthropic.com/learn/claude-for-work
+
+7. Anthropic API fundamentals
+https://github.com/anthropics/courses/blob/master/anthropic_api_fundamentals/README.md#anthropic-api-fundamentals
+
+8. Real world prompting
+https://github.com/anthropics/courses/blob/master/real_world_prompting/README.md
+
+9. Prompt evaluations
+https://github.com/anthropics/courses/blob/master/prompt_evaluations/README.md
+
+10. Claude Customer Support Agent
+https://github.com/anthropics/anthropic-quickstarts/tree/main/customer-support-agent
+
+# Anthropic courses
+
+Welcome to Anthropic's educational courses. This repository currently contains five courses.  We suggest completing the courses in the following order:
+
+1. [Anthropic API fundamentals](./anthropic_api_fundamentals/README.md) - teaches the essentials of working with the Claude SDK: getting an API key, working with model parameters, writing multimodal prompts, streaming responses, etc.
+2. [Prompt engineering interactive tutorial](./prompt_engineering_interactive_tutorial/README.md) - a comprehensive step-by-step guide to key prompting techniques. [[AWS Workshop version](https://catalog.us-east-1.prod.workshops.aws/workshops/0644c9e9-5b82-45f2-8835-3b5aa30b1848/en-US)]
+3. [Real world prompting](./real_world_prompting/README.md) - learn how to incorporate prompting techniques into complex, real world prompts. [[Google Vertex version](https://github.com/anthropics/courses/tree/vertex/real_world_prompting)] 
+4. [Prompt evaluations](./prompt_evaluations/README.md) - learn how to write production prompt evaluations to measure the quality of your prompts.
+5. [Tool use](./tool_use/README.md) - teaches everything you need to know to implement tool use successfully in your workflows with Claude.
+
