Curated front end system design resources for interviews and learning
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Curated front end system design resources for interviews and learning
TensorFlow tutorials and best practices.
A resource for learning about Machine learning & Deep Learning
TranslatedBuild your neural network easy and fast, 莫烦Python (Morvan Python) Chinese teaching
Approaching (Almost) Any Machine Learning Problem
Notebooks and code for the book "Introduction to Machine Learning with Python"
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
Learn Rust with examples (Live code editor included)
TranslatedSay goodbye to boredom; dedicated to creating practical small Python examples. For more conscientious Python tutorials, see https://ai-jupyter.com
Classical equations and diagrams in machine learning
Learn Haskell
Dedicated Resources for the Low-Level System Design. Learn how to design and implement large-scale systems. Prep for the system design interview.
An awesome list of awesome YouTubers that teach about technology. Tutorials about web development, computer science, machine learning, game development, cybersecurity, and more.
22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.
A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
The JavaScript Way book
A very simple toon lit shader example, for you to learn writing custom lit shader in Unity URP
TranslatedMachine Learning Yearning Chinese version - 《机器学习训练秘籍》 (Machine Learning Training Secrets) - by Andrew Ng
TranslatedPython for "Deep Learning": mathematical derivations, principle analysis, and source-code-level implementations for the book "深度学习" (Deep Learning, known as the "Flower Book")
🗺 The Android Developer Roadmap offers comprehensive learning paths to help you understand Android ecosystems.
TranslatedZhou Zhihua's "Machine Learning", also known as the Watermelon Book, is a fairly comprehensive book that introduces in detail different types of algorithms in the field of machine learning (for example: supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, ensemble learning, dimensionality reduction, feature selection, etc.). It records my own line of understanding and extended knowledge points from my learning process, and I hope it helps newcomers reading the Watermelon Book!
Awesome Books
this repository accompanies the book "Grokking Deep Learning"
TranslatedBook_3_"Elements of Mathematics" | Iris Book series: from addition, subtraction, multiplication and division to machine learning; now on sale; corrections are still welcome, and readers who submit many corrections will also receive free books!