Knowing python programming with its application into data processing is one of the most demanding aspect of Python programming language and this video is here to assist you. - Part 0: Course Introduction (https://www.youtube.com/watch?v=0aw2aFxDmxM) - Part 1: Data Loading, Reading, and value based Filtering (https://youtu.be/660nOAR2vOM) - Part 2: Data Filtering by Type with Inline coding (https://youtu.be/uJES3DLg5ss) - Part 3 (a/b): Writing Smart Python code through classes - Part 3a - https://youtu.be/7A6VZYuIKAU - Part 3b - https://youtu.be/4heu1splH64 - Part 4 (a/b): Adding Dynamic GUI to Python Data Application - Part 4a (https://youtu.be/pEO9c2HLGNE) - Part 4b (https://youtu.be/2kqwIF14AV8) Content covered in this video will be located in the following GitHub repo: - https://github.com/prodramp/DeepWorks/tree/main/DeveloperProgram ▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬ - (00:00) Video Starts - (00:30) What is covered? - (02:00) Filter data by Category - (07:55) Filter Result Validation - (10:00) Handling Multiple Datasets - (14:10) Filter by Object Support - (17:40) Avoid Code Duplication - (21:50) Adding Inline Code - (26:46) GitHub Resources Connect ------------------ - Prodramp LLC (@prodramp) - Website - https://prodramp.com - LinkedIn - https://www.linkedin.com/company/prod... - GitHub- https://github.com/prodramp/ - AngelList - https://angel.co/company/prodramp - Facebook - https://www.facebook.com/Prodramp Content Creator: Avkash Chauhan (@avkashchauhan) - https://www.linkedin.com/in/avkashcha... - https://twitter.com/avkashchauhan Tags: #python #pandas #dataengineering #dataprocessing #deeplearning #seaborn ... https://www.youtube.com/watch?v=uJES3DLg5ss

This video is all you need to install DLib with Nvidia CUDA and cudNN support in your Python 3.x installation created using Conda. You will also learn fixing the problem specific to following error: ImportError: /home/avkash/anaconda3/envs/dw39/lib/python3.9/site-packages/zmq/backend/cython/../../../../.././libstdc++.so.6: version `GLIBCXX_3.4.29' not found (required by /home/avkash/.cache/torch_extensions/py39_cu113/fused/fused.so) This video covers the following: 0. Ubuntu 22.04 with latest Kernel 1. dlib (latest from GitHub) 2. Cuda Toolkit 11.7 and cuDNN 8.4 Install 3. Python 3.9 with Conda 4. Torch, TensorFlow and JAX with GPU Support GitHub Resources: https://github.com/prodramp/DeepWorks/tree/main/Dlib-CUDA-Install dlib GitHub: - https://github.com/davisking/dlib ▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬ - (00:00) Problem Introduction - (02:05) Ubuntu 22.04 Cuda/Intro - (03:51) Cloning dlib from GitHub - (04:24) Building dlib code - (05:17) Checking cuda is used in build - (06:15) Installing dlib in python environment with conda - (07:18) Testing installation Connect ------------------ - Prodramp LLC (@prodramp) - Website - https://prodramp.com - LinkedIn - https://www.linkedin.com/company/prodramp - GitHub- https://github.com/prodramp/ - AngelList - https://angel.co/company/prodramp - Facebook - https://www.facebook.com/Prodramp Content Creator: Avkash Chauhan (@avkashchauhan) - https://www.linkedin.com/in/avkashchauhan - https://twitter.com/avkashchauhan Tags: #nvidia #cuda #dlib #cnn #ml #lime #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #pytorch #datarobot #datahub #streamlit #modeltesting #codeartifact #dataartifact #modelartifact #onnx #aws #kaggle #mapbox #lightgbm #xgboost #dataengineering #pandas #keras #tensorflow #tensorboard #cnn #prodramp #avkashchauhan #LIME #mli #xai #cuda #cuda-nn ... https://www.youtube.com/watch?v=6OTrrB6tTEo

If you are front-end developer, looking to integrate maps in your web, mobile or desktop application in React ChakraUI, this tutorial is for you. JavaScript library react-map-gl makes using Mapbox GL JS in React applications easy. It uses Mapbox GL JS Map as a fully controlled reactive component for React Integration. It is extensible and comes with additional React interfaces such as context and hooks to support custom components. It is part of vis.gl's Framework Suite. JavaScript library, deck.gl is a WebGL-powered framework for visual exploratory data analysis of large datasets. Content Details: -------------------------- (00:00) Video Start (0:07) Content Intro (1:19) Tutorial Plan (1:50) Source Code Walkthrough at GitHub (3:04) Loading base starter code in VSCODE IDE (4:00) Adding /mapHome route to code (10:20) Integrating MapBox Access Token (12:51) ReactMapGL (17:49) DeckGL Examples Review (19:14) DeckGL Introduction with StaticMap (21:29) Heatmap Layer (25:55) ScatterPlot Layer (30:35) Arc Layer (38:46) Text Layer (43:08) Text + Scatterplot Layers (53:26) Hexagon Layer (57:50) Text + Hex + Scatterplot Layers (1:03:05) Recap (1:03:36) Code push to GitHub (1:04:50) Credits 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 #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #featurestore #tecton #aws #databricks #featureengineering #mlmodel #azureml #sagemaker #model #collaboration #h2ohydrogentorch #pytorch #tensorflow #h2oai #mapbox #googlemaps #reactmapgl #deckgl #mapping #geojson #geotagging #keplergl #react #chakraui ... https://www.youtube.com/watch?v=SClgxqZUsXU

[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