[Graph Neural Networks part 1/2]: This tutorial is part one of a two parts GNN series. Graphs helps us understand and visualize the relationship and connection information in a natural and close to human behavior. Graph Neural networks are solving various machine learning problems where CNN or convolutional neural networks can not be applied. This video is designed for the early technology adopters who want to learn graphs and graph neural networks in shortest possible amount of the time... In this video you will get all the required technical details and necessary technical explanations related to graph and graph neural networks so that you can code them in Python using NetworkX and PyG package, and apply your knowledge immediately to your own technical problems. This tutorial is divided into two parts with the following topics: Part 1 (This Video Tutorial): ----------------------------- - Fundamentals of Graph - Mathematics of Graph - Introduction to NetworkX Python Package - Graph Programming with NetworkX - Introduction to GNN - Relationship between GNN and CNN - Introduction to PyG (pytorch_geometric) - Graph Visualization Tools - yEd - Various Graph Data Manipulation Part 2 (https://www.youtube.com/watch?v=VDzrvhgyxsU): ----------------------------- - Graph representations -- Adjacency Matrix -- Feature Matrix -- Incidence Matrix -- Degree Matrix -- Laplacian Matrix - Bag of Nodes - Node Embedding and Node Embedding Space - Applying Convolution to Graph similar to Image - Message Passing - Understanding Graph Datasets available in PyG - Node Classification using MLP & GNN - NetworkX and tSNE visualization of Graphs - GNN Explainer ▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬ - (00:00) Tutorial Introduction - (00:40) Part 1 Tutorial Content - (02:22) Part 2 Tutorial Content - (03:40) Resources & Acknowledgement - (04:54) Graph Data Use Cases - (09:41) Fundamentals of Graph - (20:55) Mathematics of Graph - (32:35) Coding Graph with NetworkX Library - (46:17) Neighbors in Graph - (48:20) Path_graph Type - (49:00) Directed Graph - (51:32) Adjacency Matrix - (55:37) MultiDirected Graph - (59:12) MultiEdge Attributes - (01:00:52) MultiGraph - (01:05:22) Sudoku Graph - (01:07:15) Grid Graph - (01:10:20) Graph Neural Networks (GNN) - (01:16:28) GNN + CNN = GCN - (01:22:44) PyG Introduction - (01:23:36) What is a Tensor? - (01:27:19) Datasets in PyG - (01:39:36) Graph View in yEd - (01:45:15) Create Graph in PyG - (01:52:28) Recap Google colab notebooks used in this tutorial: -https://github.com/prodramp/DeepWorks/tree/main/GraphNeuralNetworks Part 1 PDF document: https://github.com/prodramp/DeepWorks/blob/main/GraphNeuralNetworks/Graph%20Neural%20Networks-Part1.pdf Please visit: ------------------ - Prodramp LLC | https://prodramp.com | @prodramp - https://www.linkedin.com/company/prodramp Content Creator: Avkash Chauhan (@avkashchauhan) - https://www.linkedin.com/in/avkashchauhan Tags: #ai #ml #gnn #mlops #h2oai #driverlessai #machinelearning #cloud #model #collaboration #deeplearning #modelserving #modeldeployment #keras #tensorflow #pytorch #datarobot #datahub #aiplatform #aicloud #modelperformance #modelfit #modeleffect #modelimpact #modelbias #modeldeployment #modelregistery #modelpipeline #neptuneai #streamlit #pythonapps #deepchecks #modeltesting #codeartifact #dataartifact #modelartifact #onnx #aws #supervisor #supervisord #kaggle #keplergl #mapbox #lightgbm #xgboost #classification #regression #dataengineering #pandas #keras #tensorflow #tensorboard #mnist #cnn #convnet #alexnet #prodramp #avkashchauhan #cnnexplainer #gnn #graph #graphneuralnetwork #pyg #networkx #yed #gephi ... https://www.youtube.com/watch?v=YdGN-J322y4

This hands-on workshop shows how to use Langchain LLM application framework with Chroma embedding database to fine-tune an OpenAI GPT-3.5-Turbo LLM model on web data. The final solution returns ChatGPT like interface to your customer web data. You will also learn: - Why it is important to fine-tune LLM models with ad-hoc data - How to use open-source libraries i.e Langchain, ChromaDB The workshop code: - https://github.com/prodramp/DeepWorks/blob/main/ChatGPT/LangChainOpenAI.md == Video Timeline == (00:00) Content Intro (00:58) The Problem (04:12) The Solution (06:13) Working Solution Demo (09:10) Understanding Solution (12:12) Open-source libs (14:22) Web Data as source content (12:30) Testing UI (without action) (15:58) Python Libs Installed (17:08) Python Coding Starts (17:27) Setting OpenAI API Key (18:16) Setting Embeddings (20:12) Setting Chunk Splitter (21:02) Setting embeddings Model (21:43) Create & Persist Embeddings (23:30) Test Embeddings Code (26:08) Setting Langchain App (27:30) Adding Query to KB DB (29:18) Testing Query with KB (30:24) Continuous Queries (32:08) Opensource Libs Review (32:41) OpenAI Billing (33:34) Source code (34:00) Recap === Open-source Libraries Used: === - https://github.com/chroma-core/chroma - https://github.com/hwchase17/chat-langchain - https://github.com/openai Please visit: https://prodramp.com | @prodramp https://www.linkedin.com/company/prodramp Content Creator: Avkash Chauhan (@avkashchauhan) https://www.linkedin.com/in/avkashchauhan Tags: #llm #chatgpt #finetunellm #openai #python #ai #langchain #chromadb ... https://www.youtube.com/watch?v=JB1VT7zvEII