Data-Science-Free
Andrew-NG-Notes
Data-Science-Free | Andrew-NG-Notes | |
---|---|---|
7 | 1 | |
283 | 2,248 | |
3.9% | - | |
10.0 | 0.0 | |
almost 2 years ago | 2 months ago | |
Jupyter Notebook | ||
MIT License | - |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Data-Science-Free
Andrew-NG-Notes
What are some alternatives?
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera - Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
machine_learning_complete - A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
yt-channels-DS-AI-ML-CS - A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.
gdrl - Grokking Deep Reinforcement Learning
dive-into-machine-learning - Free ways to dive into machine learning with Python and Jupyter Notebook. Notebooks, courses, and other links. (First posted in 2016.)
fsdl-text-recognizer-2022-labs - Complete deep learning project developed in Full Stack Deep Learning, 2022 edition. Generated automatically from https://github.com/full-stack-deep-learning/fsdl-text-recognizer-2022
DeepNeuralNetworksFromScratch - Different kinds of deep neural networks (DNNs) implemented from scratch using Python and NumPy, with a TensorFlow-like object-oriented API.
strategy-ml-nn - This example shows how to use neural networks for writing a trading system on stocks.
embedml - pytorch like machine learning framework from scratch
Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, CLIP, ViT, ConvNeXt, SwiftFormer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.