Learning scikit-learn: Machine Learning in Python.
gmonce/scikit-learn-book 的主要功能包括：Practical Learning Resources。
gmonce/scikit-learn-book 的开源替代品包括： shujiahuang/cpp-primer-plus-6th — This project is a C++ learning resource and study guide consisting of structured notes and programming examples. It… binroot/tensorflow-book — This project is a collection of TensorFlow machine learning examples providing reference implementations for various… jasebell/mlbook — Example code for the Wiley book "Machine Learning - Hands On for Developers and Technical Professionals". jeffreyksmithjr/reactive-machine-learning-systems — Code from the book Machine Learning Systems. jwarmenhoven/islr-python — This project is a machine learning education resource consisting of Python implementations of statistical learning… alexsosn/marslandmlalgo — Code and data for the second edition of the textbook `Machine Learning: An Algorithm Perspective" by Stephen Marsland.
This project is a C++ learning resource and study guide consisting of structured notes and programming examples. It provides practical implementations and exercise solutions covering core language syntax, data types, and control flow. The repository features specialized samples for object-oriented design, including class inheritance, polymorphism, and abstract classes. It includes demonstrations of memory management techniques such as dynamic allocation, move semantics, and placement new, as well as template programming examples for creating generic functions and data structures. The codebas
Example code for the Wiley book "Machine Learning - Hands On for Developers and Technical Professionals"
Code from the book Machine Learning Systems.
This project is a collection of TensorFlow machine learning examples providing reference implementations for various neural network paradigms. It covers supervised, unsupervised, reinforcement, and sequential learning models. The repository includes implementations for convolutional neural networks focused on image classification and ranking, as well as recurrent neural networks for time-series forecasting and sequence-to-sequence translation. It further provides examples of reinforcement learning agents trained via reward optimization and unsupervised learning techniques such as autoencoders