5987 Open source projects that are alternatives of or similar to Stanford Cs229

吴恩达《深度学习》系列课程笔记及代码 | Notes in Chinese for Andrew Ng Deep Learning Course

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

This is Andrew NG Coursera Handwritten Notes.

该存储库包含由deeplearning.ai提供的相关课程的个人的笔记和实现代码。

深度学习笔记

computer vision learning, include python machine learning action; computer vision based on deep learning ;coursera deeplearning.ai and other cv learning materials collect ...

Python solutions to the problem sets of Stanford's graduate course on Machine Learning, taught by Prof. Andrew Ng

Complete Assignments for CS231n: Convolutional Neural Networks for Visual Recognition

Coursera Machine Learning - Python code

Some work of Andrew Ng's course on Coursera

Resources for students in the Udacity's Machine Learning Engineer Nanodegree to work through Stanford's Convolutional Neural Networks for Visual Recognition course (CS231n).

Unofficial Stanford's CS229 Machine Learning Problem Solutions (summer edition 2019, 2020).

🍟 Stanford CS229: Machine Learning

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

Practical Examples for RNNs in Tensorflow

pytorch version of spatial transformer networks

Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

Pangeo website + discussion of general issues related to the project.

Advanced Statistical Computing at Vanderbilt University Medical Center's Department of Biostatistics

How to do Bayesian statistical modelling using numpy and PyMC3

A collection of Bayesian data analysis recipes using PyMC3

My Machine Learning blog

Self-Supervised Learning of 3D Human Pose using Multi-view Geometry (CVPR2019)

🕵️‍♀️ 监视我的手机：数据都去哪儿了？

A couple of simple GANs in Keras

Code for ICLR 2020 paper "VL-BERT: Pre-training of Generic Visual-Linguistic Representations".

用户贷款风险预测

TensorFlow documentation

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

【运筹OR帷幄|数据科学】pandas教程系列电子书

Deep Learning Specialization by Andrew Ng on Coursera.

Code base for the Learning PySpark book (in preparation)

Code repository for O'Reilly book

主要存储Datawhale组队学习中“数据挖掘/机器学习”方向的资料。

Reference deployment of JupyterHub with docker

Computations and statistics on manifolds with geometric structures.

Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.

Code for some of my tutorials

What would you say if I told you there is a app on the market that tell you if you have a hotdog or not a hotdog.

A few notebooks about deep learning in pytorch

Utility functions for handling MIDI data in a nice/intuitive way.

STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2020)

A course in reinforcement learning in the wild

This is the code for "Make Money with Tensorflow 2.0" by Siraj Raval

Lab Materials for MIT 6.S191: Introduction to Deep Learning

📔 在学院的书架上发现了一本不带脑子就能看懂的书《Python数据挖掘与实战》

Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts

Image augmentation library in Python for machine learning.

A self driving toy car using end-to-end learning

Example code for the Siggraph Asia Tutorial CreativeAI

Welcome to the one point community-driven encyclopedia for anything in technology.

This repository contains my full work and notes on Coursera's NLP Specialization (Natural Language Processing) taught by the instructor Younes Bensouda Mourri and Łukasz Kaiser offered by deeplearning.ai

Apache Airflow tutorial

Native Python WFDB package

GNU Radio decoders for several Amateur satellites

Simple XLNet implementation with Pytorch Wrapper

该资源为作者在CSDN的撰写Python图像处理文章的支撑，主要是Python实现图像处理、图像识别、图像分类等算法代码实现，希望该资源对您有所帮助，一起加油。

Book and code for Think Complexity, 2nd edition