Data Processing Fundamentals: Data Access and Filtering (Part 1/4)
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.
▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬ - (00:00) Video Starts - (00:20) What is covered? - (01:25) Environment Setup - (07:00) Limit Records - (12:30) Columns or Fields - (15:40) Data Filtering By Value - (20:00) Filtering Validations - (25:10) Column Datatype Validation - (27:10) Filter Result Validation
Redux is a Predictable State Container for JavaScripts Applications.
- Redux helps you write applications that behave consistently, run in different environments (client, server, and native), and are easy to test.
- Centralizing your application's state and logic enables powerful capabilities like undo/redo, state persistence, and much more.
- The Redux DevTools make it easy to trace when, where, why, and how your application's state changed. Redux's architecture lets you log changes, use "time-travel debugging", and even send complete error reports to a server.
- Redux works with any UI layer, and has a large ecosystem of addons to fit your needs.
This hands-on tutorial is designed for the front-end developers who have Chakra UI or CoreUI based frontend and would like to add redux support to your enterprise ready frontend application. The same concept will work with other JavaScript based framework also.
In this tutorial you will learn the following:
1. Adding required package to package.json
2. Add store backend using store.js at the src-root (src/store.js).
3. Add store and action based middleware into middleware.js at same path where store.js
4. Add support for actions in agents.js at the src-root (src/agents.js)
5. Now add implement all the reducer specific code for each component and combiner all reducers :
- src/state/actions/index.js
- src/state/reducers/homeReducers.js (Component specific implementation)
- src/state/reducers/index.js (combine all reducer implementations
6. Add payload helper.js for dto.payload
7. Implement redux support to component specific container code
8. In the component implementation code access redux based API and access state data
9. Update App.js to support Redux Provider and connect with state store.
Provider[store={store}] = AppRouter
Video Timeline:
-------------------------
(00:00) Video Start
(00:07) Content intro
(01:52) Redux Data Flow
(03:44) Code walkthrough at GitHub (starter and full code)
(07:10) Starting exercise from base Chakra UI code, ready with routes
(08:00) Opening code in Visual Studio Code IDE
(09:33) Adding react-redux modules into package.json
(11:04) Add store backend using store.js
(12:41) Adding agents (src/agents.js)
(14:08) Implement component reducer code and combine all reducers
(16:23) Add payload helper.js for dto.payload
(18:37) Add redux support to component specific container
(26:16) Implement component with Redux store
(28.27) Update app.js with Redux store Provider
(29:26) Add store and action based middleware
(32.23) Start Testing code with Redux
(34.47) Update backend API code
(36:16) Recap all steps
(40:08) Pushing all code to GitHub
(41:48) Credits
Instructions URL:
https://github.com/prodramp/publiccode/tree/master/chakra-ui-tutorials/redux-states
Workshop Source code:
https://github.com/prodramp/publiccode/tree/master/chakra-ui-tutorials/redux-states
GitHub Repo:
URL: https://github.com/prodramp/publiccode
Note: Please clone the repo and visit specific folder to get the full source code.
Chakra UI:
https://chakra-ui.com/docs/getting-started
Please visit:
https://prodramp.com
@prodramp
https://www.linkedin.com/company/prodramp
Content Creator:
Avkash Chauhan (@avkashchauhan)
https://www.linkedin.com/in/avkashchauhan
Tags:
#webdevelopment, #frontend #react #chakraui #fullstack #uidevelopment #uicomponents #layout #header #footer #floatingpanel #sidebar #menubar #redux #statemanager #react-redux #JavaScript
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https://www.youtube.com/watch?v=9IwA6E8crTo
If you are a python pandas library user and have experience processing datasets using pandas you must know that in order to collect input dataset-specific details you may need to run lots of commands to collect various dataset information and then combine it all together.
So what if there is a python library which can provide the detailed report in a visually appealing and interactive format with just a few lines of code, wouldn’t you be excited? Indeed, absolutely.
In this video, I will be giving you an in-depth walkthrough to the pandas-profiling library which you can use with pandas to generate details report about your dataset which includes maximum details about each column individually in your dataset along with interactions, correlations, histogram, missing and duplicate values, and lots of other very useful details, all in a fully interactive and exportable HTML report format.
Content Timeline:
---------------------------
- (00:00) Video Start
- (00:07) Video Content Intro
- (01:56) Code & Jupyter Notebook Introduction
- (02:49) Library Installation
- (03:39) Pandas-profiling Library Introduction
- (07:29) Demo with Titanic Dataset
- (13:36) Demo with Titanic Dataset - Interactions
- (14:50) Demo with Titanic Dataset - Correlations
- (15:17) Demo with Titanic Dataset - Missing Values
- (17:02) Saving HTML Report
- (18:43) Profiling large dataset
- (19:51) Minimal Profiling Reporting Setting
- (20:41) Demo with Titanic Dataset - Config Metadata
- (21:36) Demo with Titanic Dataset - Config Details
- (22:37) Demo with Titanic Dataset - Config Param
- (23:17) Demo with Titanic Dataset - View Widgets
- (25:46) Demo with Titanic Dataset - Histogram Config
- (27:19) Streamlit Application with Profiling Report
- (31:31) Recap
- (32:16) Credits
GitHub URL for the samples in the Video:
https://github.com/prodramp/publiccode/tree/master/python/data-profiling
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 #model #collaboration #deeplearning #modelserving #modeldeployment #keras #tensorflow #pytorch #datarobot #datahub #aiplatform #aicloud #cometml #modelmonitoring #drift #modelregistry #modelmanagement #pandas #pandasprofiling
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https://www.youtube.com/watch?v=RLSZbN9RA9A
Runway, the generative AI startup that co-created last year’s breakout text-to-image model Stable Diffusion, has released an AI model GEN-1 that can transform existing videos into new ones by applying any style specified by a text prompt or reference image.
Links associated with the video:
- https://research.runwayml.com/gen1
- https://runwayml.com/
- https://github.com/Hannibal046/Awesome-LLM
== Video Timeline ==
(00:00) Content Intro
(01:15) GEN-1 Intro Video
(03:03) Request Beta Access
Please visit:
https://prodramp.com | @prodramp
https://www.linkedin.com/company/prodramp
Content Creator:
Avkash Chauhan (@avkashchauhan)
https://www.linkedin.com/in/avkashchauhan
Tags:
#deeplearning #llm #chatgpt #promptengineering #openai #python #mindmaps
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https://www.youtube.com/watch?v=58K-cpBLDc0
This video is all you need to learn about adding a Python Gradio library based UI on top of Stable Diffusion Models with various functionalities added into various tabs in the UI.
Source Code GitHub :
- https://github.com/prodramp/DeepWorks/tree/main/Stable-Diffusion-TF-WebUI
▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬
- (00:00) UI Introduction
- (01:40) Why this video created?
- (03:35) Full UI Walkthrough
- (09:35) Code Walkthrough
- (13:35) Content Summary
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 #scikitlearn #dataengineering
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https://www.youtube.com/watch?v=ZvHSKNztjBg
If you are Google colab user and want to save your notebook to GitHub public and private repos, this tutorial is for you.
In this tutorial you will enable GitHub (first public and then private repos) access from Google colab just in few click under 15 seconds.
Tutorial Content timeline:
------------------------------------------
(00:00) video start
(00:07) Content intro
(01:20) GitHub and Google colab setup
(2:35) Connecting Colab with GitHub public repos
(4:36) Notebook save validation to public repo folder
(5:24) Integration Re-test with a new notebook
(6:05) Enabling private repo access through Colab settings
(7:50) Recap
(8:15) Credits
Tutorial Test URL:
https://github.com/prodramp/publiccode/tree/master/machine_learning
Please visit:
https://prodramp.com | @prodramp
https://www.linkedin.com/company/prodramp
Content Creator:
Avkash Chauhan (@avkashchauhan)
https://www.linkedin.com/in/avkashchauhan
Tags:
#webdevelopment, #frontend #react #chakraui #fullstack #uidevelopment #uicomponents #redux #react-redux #JavaScript #python #googlecolab #github #access #appdev #devops
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https://www.youtube.com/watch?v=A5A-M22WxXA
[For Absolute Beginners] If you are new to neural networks and always wanted to create your first neural network but could not get yourself started, this tutorial is exactly for you.
This tutorial is totally created for absolute beginners who want to create their very first neural network without consideration of math, or stats, or any machine learning knowledge.
In this tutorial, we will be using both Keras and TensorFlow as the foundation for our deep learning framework and utilize the power of Keras and TensorFlow to write the neural network from scratch by defining the input, output and the layers in-between. There are various other concepts discussed and created using the network visualization method to make the learning easier and effective. Neural Network visualization along with source code, is used side by side to make sure each line in the code is understood properly.
At last we have used a pre-trained network for prediction and visualized its layers to understand what a full working neural network looks like after it is ready, to provide the actual scope of learning to the students.
The content timeline is as below:
- (00:00) Tutorial Starts
- (00:40) Content Intro
- (04:41) Getting started
- (12:18) Helpful Reference
- (13:04) Neural Network Visualization
- (25:15) Creating sequential neural network
- (35:51) Neural Network Parameters and Feed Forward Network
- (52:35) Pre-trained Network (full example)
- (01:05:55) Fully Connected Layers
- (01:09:45) Recap
- (01:11:22) Tutorial Source Code
Jupyter notebook Source code:
https://github.com/prodramp/python-projects/tree/main/deeplearning/neural-network-starter
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 #model #collaboration #deeplearning #modelserving #modeldeployment #keras #tensorflow #pytorch #datarobot #datahub #aiplatform #aicloud #modelperformance #modelfit #modeleffect #modelimpact #modelbias #modeldeployment #modelregistery #modelpipeline #neptuneai #pythondsp #pythonaudio #streamlit #pythonapps #deepchecks #modeltesting #codeartifact #dataartifact #modelartifact #onnx #aws #supervisor #supervisord #kaggle #kepler.gl #mapbox mapboxgl #wildfireml #lightgbm #xgboost #classification #regression #react #chakraui #datavisualization #dataengineering #frontenddevelopment #wildfire #modin #pandas #keras #tensorflow #tensorboard #neuralnetwork
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https://www.youtube.com/watch?v=lnkqiJ1wOfI
Another full open source large language model from H2Oa.o AI team with 12B and 20B parameters, trained on the Pile open-source dataset is released with the following features:
- Open-source repository with fully permissive, commercially usable code, data and models
- Code for preparing large open-source datasets as instruction datasets for fine-tuning of large language models (LLMs), including prompt engineering
- Code for fine-tuning large language models (currently up to 20B parameters) on commodity hardware and enterprise GPU servers (single or multi node)
- Code for enabling LoRA (low-rank approximation) and 8-bit quantization for memory-efficient fine-tuning and generation.
- Code to run a chatbot on a GPU server, with shareable end-point with Python client API
- Code to evaluate and compare the performance of fine-tuned LLMs
== Video Timeline ==
(00:00) Content Intro
(00:15) Introducing H2O
(01:30)H2O.ai Intro
(03:05) h2ogpt
(04:55) h2ogpt Chatbot
(09:35) h2ogpt Details
(10:40) H2O llmstudio Intro
(13:20) Conclusion
=== Resources ===
- https://h2o.ai/
- https://h2o.ai/events/h2o-world/
- https://github.com/h2oai/h2ogpt
- https://github.com/h2oai/h2o-llmstudio
- https://huggingface.co/spaces/h2oai/h2ogpt-chatbot2
- https://huggingface.co/spaces/h2oai/h2ogpt-chatbot
- https://www.kaggle.com/code/philippsinger/openassistant-conversations-dataset-oasst1
Please visit:
https://prodramp.com | @prodramp
https://www.linkedin.com/company/prodramp
Content Creator:
Avkash Chauhan (@avkashchauhan)
https://www.linkedin.com/in/avkashchauhan
Tags:
#stablelm #stableai #finetunellm #openai #python #ai #langchain #chromadb
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https://www.youtube.com/watch?v=aFU3VRGE2gk
In this hands-on tutorial you will learn the following:
- Postgres DB Setup in Docker
- Using docker-compose script to launch Postgres DB
- Using Docker Desktop to setup and manage Postgres DB instance
- Both Postgres version 12.x and 14.x running side by side
- Solving host port conflict with multiple Postgres running at host
- Managing Postgres DB with command line and pgAdmin tool
- Quick tips and tricks with Postgres DB management
The video timeline is as below:
- (00:00) Start of Tutorial
- (00:06) Intro
- (1:35) pgAdmin and Docker installation
- (4:10) Using docker-compose script to launch Postgres DB
- (5:20) Using Docker Desktop to setup and manage Postgres DB instance
- (6:40) Using pgAdmin manage Postgres DB instance
- (8:10) Command line access to Postgres DB Instance
- (11:00) Updating docker-compose script to run both v12 and v14 Postgres DB side by side
- (11:30) Both Postgres version 12.x and 14.x running side by side
- (14:00) Managing both Postgres DB v12 and v14 side by side using pgAdmin
- (15:24) Recap
Script URL in Tutorials:
https://github.com/prodramp/publiccode/blob/master/postgresql-docker-scripts/postgresql-docker-script.md
Please visit:
https://prodramp.com
@prodramp
Content Creator:
Avkash Chauhan (@avkashchauhan)
...
https://www.youtube.com/watch?v=thak0TV-d2c
Are you really interested in creating dynamic and animated data visualization with Matplotlib in python? If yes, you are in the right place...
For any data engineer or anyone working on data, the need to build dynamic and animated data visualizations for a better representation of information.
If you are a python engineer you can achieve the same objective using the Matplotlib library and use animation functionalities
In this tutorial, you will learn the following:
- Creating an animated sin wave using:
-- Matplotlib Funanimation methods
-- HTML5 video modules
- Creating an animated pie graph using Matplotlib and HTML5 video modules
- Creating an animated bar graph using Matplotlib and HTML5 video modules
- Using bar_chart_race python module to build the race bar chart
- Process worldwide population, covid-19 cases dataset and make dynamic race bar charts
GitHub URL:
https://github.com/prodramp/publiccode/tree/master/matplotlib-animation
Please visit:
https://prodramp.com
@prodramp
https://www.linkedin.com/company/prodramp
Content Creator:
Avkash Chauhan (@avkashchauhan)
https://www.linkedin.com/in/avkashchauhan
Tags:
#webdevelopment, #frontend #react, #python, #layout, #fullstackdevelopment #pandas #matplotlib #datavisualization
...
https://www.youtube.com/watch?v=hapLjnmlRZs