Forwarded from Python | Machine Learning | Coding | R
┌ Online
┌ Online
┌ Online
┌ Online
┌ Online
┌ Online
┌ Online
┌ Online
┌ Online
┌ Online
#DataScience #Python #DataAnalysis #DataVisualization #RProgramming #DeepLearning #CommandLine #HandsOnLearning #Statistics #Bayesian #Kafka #MachineLearning #AI #Programming #FreeBooks
https://yangx.top/CodeProgrammer✅
Please open Telegram to view this post
VIEW IN TELEGRAM
🔥5👍4❤2
Forwarded from Python | Machine Learning | Coding | R
Keras Cheat Sheet: Neural Networks in Python
#keras #cheatsheet #python #library #programming #guide
https://yangx.top/CodeProgrammer
#keras #cheatsheet #python #library #programming #guide
https://yangx.top/CodeProgrammer
❤1👍1
Forwarded from Python | Machine Learning | Coding | R
This media is not supported in your browser
VIEW IN TELEGRAM
📝 Cheat sheets for data science and machine learning
Link: https://sites.google.com/view/datascience-cheat-sheets
#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN
https://yangx.top/CodeProgrammer✅
Link: https://sites.google.com/view/datascience-cheat-sheets
#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN
https://yangx.top/CodeProgrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
👍4❤3
Forwarded from Python | Machine Learning | Coding | R
Please open Telegram to view this post
VIEW IN TELEGRAM
Please open Telegram to view this post
VIEW IN TELEGRAM
👍4
Forwarded from Python | Machine Learning | Coding | R
Top_100_Machine_Learning_Interview_Questions_Answers_Cheatshee.pdf
5.8 MB
Top 100 Machine Learning Interview Questions & Answers Cheatsheet
#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN #Keras #R
https://yangx.top/CodeProgrammer✅
Please open Telegram to view this post
VIEW IN TELEGRAM
👍7❤1
Forwarded from Python | Machine Learning | Coding | R
Machine Learning from Scratch by Danny Friedman
This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different algorithms create the models they do and the advantages and disadvantages of each one.
This book will be most helpful for those with practice in basic modeling. It does not review best practices—such as feature engineering or balancing response variables—or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.
🌟 Link: https://dafriedman97.github.io/mlbook/content/introduction.html
This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different algorithms create the models they do and the advantages and disadvantages of each one.
This book will be most helpful for those with practice in basic modeling. It does not review best practices—such as feature engineering or balancing response variables—or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.
#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN #Keras #R
https://yangx.top/CodeProgrammer✅
Please open Telegram to view this post
VIEW IN TELEGRAM
👍10
🔥 Trending Repository: build-your-own-x
📝 Description: Master programming by recreating your favorite technologies from scratch.
🔗 Repository URL: https://github.com/codecrafters-io/build-your-own-x
🌐 Website: https://codecrafters.io
📖 Readme: https://github.com/codecrafters-io/build-your-own-x#readme
📊 Statistics:
🌟 Stars: 411K stars
👀 Watchers: 6.2k
🍴 Forks: 38.5K forks
💻 Programming Languages: Markdown
🏷️ Related Topics:
==================================
🧠 By: https://yangx.top/DataScienceM
📝 Description: Master programming by recreating your favorite technologies from scratch.
🔗 Repository URL: https://github.com/codecrafters-io/build-your-own-x
🌐 Website: https://codecrafters.io
📖 Readme: https://github.com/codecrafters-io/build-your-own-x#readme
📊 Statistics:
🌟 Stars: 411K stars
👀 Watchers: 6.2k
🍴 Forks: 38.5K forks
💻 Programming Languages: Markdown
🏷️ Related Topics:
#programming #tutorials #free #awesome_list #tutorial_code #tutorial_exercises
==================================
🧠 By: https://yangx.top/DataScienceM
🔥 Trending Repository: Pake
📝 Description: 🤱🏻 Turn any webpage into a desktop app with Rust. 🤱🏻 利用 Rust 轻松构建轻量级多端桌面应用
🔗 Repository URL: https://github.com/tw93/Pake
📖 Readme: https://github.com/tw93/Pake#readme
📊 Statistics:
🌟 Stars: 41.3K stars
👀 Watchers: 218
🍴 Forks: 7.7K forks
💻 Programming Languages: JavaScript - Rust - Dockerfile
🏷️ Related Topics:
==================================
🧠 By: https://yangx.top/DataScienceM
📝 Description: 🤱🏻 Turn any webpage into a desktop app with Rust. 🤱🏻 利用 Rust 轻松构建轻量级多端桌面应用
🔗 Repository URL: https://github.com/tw93/Pake
📖 Readme: https://github.com/tw93/Pake#readme
📊 Statistics:
🌟 Stars: 41.3K stars
👀 Watchers: 218
🍴 Forks: 7.7K forks
💻 Programming Languages: JavaScript - Rust - Dockerfile
🏷️ Related Topics:
#music #rust #productivity #mac #youtube #twitter #programming #high_performance #gemini #openai #windows_desktop #linux_desktop #tauri #mac_desktop #excalidraw #llm #no_electron #chatgpt #gemini_ai #deepseek
==================================
🧠 By: https://yangx.top/DataScienceM