Jupyter人工智能實驗+深度學習模型+機器學習資源

參考網站http://www.educg.net.cn/resources.html

https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README-zh-CN.md

https://github.com/rasbt/deeplearning-models

https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks

https://www.oracle.com/database/technologies/bigdatalite-v411.html

1、ipython-notebooks

This repo contains various IPython notebooks I've created to experiment with libraries and work through exercises, and explore subjects that I find interesting. I've included notebook viewer links below. Click the link to see a live rendering of the notebook.

Language

These notebooks contain introductory content such as an overview of the language and a review of IPython's functionality.

Introduction To Python
IPython Magic Commands

Libraries

Examples using a variety of popular "data science" Python libraries.

NumPy
SciPy
Matplotlib
Pandas
Statsmodels
Scikit-learn
Seaborn
NetworkX
PyMC
NLTK
DEAP
Gensim

Machine Learning Exercises

Implementations of the exercises presented in Andrew Ng's "Machine Learning" class on Coursera.

Exercise 1 - Linear Regression
Exercise 2 - Logistic Regression
Exercise 3 - Multi-Class Classification
Exercise 4 - Neural Networks
Exercise 6 - Support Vector Machines
Exercise 7 - K-Means Clustering & PCA
Exercise 8 - Anomaly Detection & Recommendation Systems

Tensorflow Deep Learning Exercises

Implementations of the assignments from Google's Udacity course on deep learning.

Assignment 1 - Intro & Data Prep
Assignment 2 - Regression & Neural Nets
Assignment 3 - Regularization
Assignment 4 - Convolutions
Assignment 5 - Word Embeddings
Assignment 6 - Recurrent Nets

Spark Big Data Labs

Lab exercises for the original Spark classes on edX.

Lab 0 - Learning Apache Spark
Lab 1 - Building A Word Count Application
Lab 2 - Web Server Log Analysis
Lab 3 - Text Analysis & Entity Resolution
Lab 4 - Introduction To Machine Learning
ML Lab 3 - Linear Regression
ML Lab 4 - Click-Through Rate Prediction
ML Lab 5 - Principal Component Analysis

Fast.ai Lessons

Notebooks from Jeremy Howard's fast.ai class.

Lesson 1 - Image Classification
Lesson 2 - Multi-label Classification
Lesson 3 - Structured And Time Series Data
Lesson 4 - Sentiment Classification
Lesson 5 - Recommendation Using Deep Learning
Lesson 6 - Language Modeling With RNNs
Lesson 7 - Convolutional Networks In Detail

Deep Learning With Keras

Notebooks using Keras to implement deep learning models.

Part 1 - Structured And Time Series Data
Part 2 - Convolutional Networks
Part 3 - Recommender Systems
Part 4 - Recurrent Networks
Part 5 - Anomaly Detection
Part 6 - Generative Adversarial Networks

Misc

Notebooks covering various interesting topics!

Comparison Of Various Code Optimization Methods
A Simple Time Series Analysis of the S&P 500 Index
An Intro To Probablistic Programming
Language Exploration Using Vector Space Models
Solving Problems With Dynamic Programming
Time Series Forecasting With Prophet
Markov Chains From Scratch
A Sampling Of Monte Carlo Methods

 

2、Deep Learning Models

A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.

Traditional Machine Learning

• Perceptron
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

• Logistic Regression
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

• Softmax Regression (Multinomial Logistic Regression)
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

• Softmax Regression with MLxtend's plot_decision_regions on Iris
     [PyTorch: GitHub | Nbviewer]

Multilayer Perceptrons

• Multilayer Perceptron
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

• Multilayer Perceptron with Dropout
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

• Multilayer Perceptron with Batch Normalization
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

• Multilayer Perceptron with Backpropagation from Scratch
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

Convolutional Neural Networks

Basic

• Convolutional Neural Network
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

• Convolutional Neural Network with He Initialization
     [PyTorch: GitHub | Nbviewer]

Concepts

• Replacing Fully-Connnected by Equivalent Convolutional Layers
     [PyTorch: GitHub | Nbviewer]

Fully Convolutional

• Fully Convolutional Neural Network
     [PyTorch: GitHub | Nbviewer]

LeNet

• LeNet-5 on MNIST
     [PyTorch: GitHub | Nbviewer]

• LeNet-5 on CIFAR-10
     [PyTorch: GitHub | Nbviewer]

• LeNet-5 on QuickDraw
     [PyTorch: GitHub | Nbviewer]

AlexNet

• AlexNet on CIFAR-10
     [PyTorch: GitHub | Nbviewer]

VGG

• Convolutional Neural Network VGG-16
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

• VGG-16 Gender Classifier Trained on CelebA
     [PyTorch: GitHub | Nbviewer]

• VGG-16 Dogs vs Cats Classifier
     [PyTorch: GitHub | Nbviewer]

• Convolutional Neural Network VGG-19
     [PyTorch: GitHub | Nbviewer]

DenseNet

• DenseNet-121 Digit Classifier Trained on MNIST
     [PyTorch: GitHub | Nbviewer]

• DenseNet-121 Image Classifier Trained on CIFAR-10
     [PyTorch: GitHub | Nbviewer]

ResNet

• ResNet and Residual Blocks
     [PyTorch: GitHub | Nbviewer]

• ResNet-18 Digit Classifier Trained on MNIST
     [PyTorch: GitHub | Nbviewer]

• ResNet-18 Gender Classifier Trained on CelebA
     [PyTorch: GitHub | Nbviewer]

• ResNet-34 Digit Classifier Trained on MNIST
     [PyTorch: GitHub | Nbviewer]

• ResNet-34 Object Classifier Trained on QuickDraw
     [PyTorch: GitHub | Nbviewer]

• ResNet-34 Gender Classifier Trained on CelebA
     [PyTorch: GitHub | Nbviewer]

• ResNet-50 Digit Classifier Trained on MNIST
     [PyTorch: GitHub | Nbviewer]

• ResNet-50 Gender Classifier Trained on CelebA
     [PyTorch: GitHub | Nbviewer]

• ResNet-101 Gender Classifier Trained on CelebA
     [PyTorch: GitHub | Nbviewer]

• ResNet-101 Trained on CIFAR-10
     [PyTorch: GitHub | Nbviewer]

• ResNet-152 Gender Classifier Trained on CelebA
     [PyTorch: GitHub | Nbviewer]

Network in Network

• Network in Network CIFAR-10 Classifier
     [PyTorch: GitHub | Nbviewer]

Normalization Layers

• BatchNorm before and after Activation for Network-in-Network CIFAR-10 Classifier
     [PyTorch: GitHub | Nbviewer]

• Filter Response Normalization for Network-in-Network CIFAR-10 Classifier
     [PyTorch: GitHub | Nbviewer]

Metric Learning

• Siamese Network with Multilayer Perceptrons
     [TensorFlow 1: GitHub | Nbviewer]

Autoencoders

Fully-connected Autoencoders

• Autoencoder (MNIST)
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

• Autoencoder (MNIST) + Scikit-Learn Random Forest Classifier
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

Convolutional Autoencoders

• Convolutional Autoencoder with Deconvolutions / Transposed Convolutions
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

• Convolutional Autoencoder with Deconvolutions and Continuous Jaccard Distance
     [PyTorch: GitHub | Nbviewer]

• Convolutional Autoencoder with Deconvolutions (without pooling operations)
     [PyTorch: GitHub | Nbviewer]

• Convolutional Autoencoder with Nearest-neighbor Interpolation
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

• Convolutional Autoencoder with Nearest-neighbor Interpolation -- Trained on CelebA
     [PyTorch: GitHub | Nbviewer]

• Convolutional Autoencoder with Nearest-neighbor Interpolation -- Trained on Quickdraw
     [PyTorch: GitHub | Nbviewer]

Variational Autoencoders

• Variational Autoencoder
     [PyTorch: GitHub | Nbviewer]

• Convolutional Variational Autoencoder
     [PyTorch: GitHub | Nbviewer]

Conditional Variational Autoencoders

• Conditional Variational Autoencoder (with labels in reconstruction loss)
     [PyTorch: GitHub | Nbviewer]

• Conditional Variational Autoencoder (without labels in reconstruction loss)
     [PyTorch: GitHub | Nbviewer]

• Convolutional Conditional Variational Autoencoder (with labels in reconstruction loss)
     [PyTorch: GitHub | Nbviewer]

• Convolutional Conditional Variational Autoencoder (without labels in reconstruction loss)
     [PyTorch: GitHub | Nbviewer]

Generative Adversarial Networks (GANs)

• Fully Connected GAN on MNIST
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

• Fully Connected Wasserstein GAN on MNIST
     [PyTorch: GitHub | Nbviewer]

• Convolutional GAN on MNIST
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

• Convolutional GAN on MNIST with Label Smoothing
     [TensorFlow 1: GitHub | Nbviewer]
     [PyTorch: GitHub | Nbviewer]

• Convolutional Wasserstein GAN on MNIST
     [PyTorch: GitHub | Nbviewer]

• "Deep Convolutional GAN" (DCGAN) on Cats and Dogs Images
     [PyTorch: GitHub | Nbviewer]

• "Deep Convolutional GAN" (DCGAN) on CelebA Face Images
     [PyTorch: GitHub | Nbviewer]

Graph Neural Networks (GNNs)

• Most Basic Graph Neural Network with Gaussian Filter on MNIST
     [PyTorch: GitHub | Nbviewer]

• Basic Graph Neural Network with Edge Prediction on MNIST
     [PyTorch: GitHub | Nbviewer]

• Basic Graph Neural Network with Spectral Graph Convolution on MNIST
     [PyTorch: GitHub | Nbviewer]

Recurrent Neural Networks (RNNs)

Many-to-one: Sentiment Analysis / Classification

• A simple single-layer RNN (IMDB)
     [PyTorch: GitHub | Nbviewer]

• A simple single-layer RNN with packed sequences to ignore padding characters (IMDB)
     [PyTorch: GitHub | Nbviewer]

• RNN with LSTM cells (IMDB)
     [PyTorch: GitHub | Nbviewer]

• RNN with LSTM cells (IMDB) and pre-trained GloVe word vectors
     [PyTorch: GitHub | Nbviewer]

• RNN with LSTM cells and Own Dataset in CSV Format (IMDB)
     [PyTorch: GitHub | Nbviewer]

• RNN with GRU cells (IMDB)
     [PyTorch: GitHub | Nbviewer]

• Multilayer bi-directional RNN (IMDB)
     [PyTorch: GitHub | Nbviewer]

• Bidirectional Multi-layer RNN with LSTM with Own Dataset in CSV Format (AG News)
     [PyTorch: GitHub | Nbviewer]

• Bidirectional Multi-layer RNN with LSTM with Own Dataset in CSV Format (Yelp Review Polarity)
     [PyTorch: GitHub | Nbviewer]

• Bidirectional Multi-layer RNN with LSTM with Own Dataset in CSV Format (Amazon Review Polarity)
     [PyTorch: GitHub | Nbviewer]

Many-to-Many / Sequence-to-Sequence

• A simple character RNN to generate new text (Charles Dickens)
     [PyTorch: GitHub | Nbviewer]

Ordinal Regression

• Ordinal Regression CNN -- CORAL w. ResNet34 on AFAD-Lite
     [PyTorch: GitHub | Nbviewer]

• Ordinal Regression CNN -- Niu et al. 2016 w. ResNet34 on AFAD-Lite
     [PyTorch: GitHub | Nbviewer]

• Ordinal Regression CNN -- Beckham and Pal 2016 w. ResNet34 on AFAD-Lite
     [PyTorch: GitHub | Nbviewer]

Tips and Tricks

• Cyclical Learning Rate
     [PyTorch: GitHub | Nbviewer]

• Annealing with Increasing the Batch Size (w. CIFAR-10 & AlexNet)
     [PyTorch: GitHub | Nbviewer]

• Gradient Clipping (w. MLP on MNIST)
     [PyTorch: GitHub | Nbviewer]

Transfer Learning

• Transfer Learning Example (VGG16 pre-trained on ImageNet for Cifar-10)
     [PyTorch: GitHub | Nbviewer]

Visualization and Interpretation

• Vanilla Loss Gradient (wrt Inputs) Visualization (Based on a VGG16 Convolutional Neural Network for Kaggle's Cats and Dogs Images)
     [PyTorch: GitHub | Nbviewer]

• Guided Backpropagation (Based on a VGG16 Convolutional Neural Network for Kaggle's Cats and Dogs Images)
     [PyTorch: GitHub | Nbviewer]

PyTorch Workflows and Mechanics

Custom Datasets

• Custom Data Loader Example for PNG Files
     [PyTorch: GitHub | Nbviewer]

• Using PyTorch Dataset Loading Utilities for Custom Datasets -- CSV files converted to HDF5
     [PyTorch: GitHub | Nbviewer]

• Using PyTorch Dataset Loading Utilities for Custom Datasets -- Face Images from CelebA
     [PyTorch: GitHub | Nbviewer]

• Using PyTorch Dataset Loading Utilities for Custom Datasets -- Drawings from Quickdraw
     [PyTorch: GitHub | Nbviewer]

• Using PyTorch Dataset Loading Utilities for Custom Datasets -- Drawings from the Street View House Number (SVHN) Dataset
     [PyTorch: GitHub | Nbviewer]

• Using PyTorch Dataset Loading Utilities for Custom Datasets -- Asian Face Dataset (AFAD)
     [PyTorch: GitHub | Nbviewer]

• Using PyTorch Dataset Loading Utilities for Custom Datasets -- Dating Historical Color Images
     [PyTorch: GitHub | Nbviewer]

• Using PyTorch Dataset Loading Utilities for Custom Datasets -- Fashion MNIST
     [PyTorch: GitHub | Nbviewer]

Training and Preprocessing

• Generating Validation Set Splits
     [PyTorch: GitHub | Nbviewer]

• Dataloading with Pinned Memory
     [PyTorch: GitHub | Nbviewer]

• Standardizing Images
     [PyTorch: GitHub | Nbviewer]

• Image Transformation Examples
     [PyTorch: GitHub | Nbviewer]

• Char-RNN with Own Text File
     [PyTorch: GitHub | Nbviewer]

• Sentiment Classification RNN with Own CSV File
     [PyTorch: GitHub | Nbviewer]

Parallel Computing

• Using Multiple GPUs with DataParallel -- VGG-16 Gender Classifier on CelebA
     [PyTorch: GitHub | Nbviewer]

Other

• Sequential API and hooks
     [PyTorch: GitHub | Nbviewer]

• Weight Sharing Within a Layer
     [PyTorch: GitHub | Nbviewer]

• Plotting Live Training Performance in Jupyter Notebooks with just Matplotlib
     [PyTorch: GitHub | Nbviewer]

Autograd

• Getting Gradients of an Intermediate Variable in PyTorch
     [PyTorch: GitHub | Nbviewer]

TensorFlow Workflows and Mechanics

Custom Datasets

• Chunking an Image Dataset for Minibatch Training using NumPy NPZ Archives
     [TensorFlow 1: GitHub | Nbviewer]

• Storing an Image Dataset for Minibatch Training using HDF5
     [TensorFlow 1: GitHub | Nbviewer]

• Using Input Pipelines to Read Data from TFRecords Files
     [TensorFlow 1: GitHub | Nbviewer]

• Using Queue Runners to Feed Images Directly from Disk
     [TensorFlow 1: GitHub | Nbviewer]

• Using TensorFlow's Dataset API
     [TensorFlow 1: GitHub | Nbviewer]

Training and Preprocessing

• Saving and Loading Trained Models -- from TensorFlow Checkpoint Files and NumPy NPZ Archives
     [TensorFlow 1: GitHub | Nbviewer]

 

3、機器學習資源大全

預備知識

這個小章節是一些在每日計劃開始前我想去了解的一些預備知識與一些有趣的信息。

•  Data Analytics，Data Analysis，數據挖掘，數據科學，機器學習，大數據的區別是什麼？

•  學習如何去學習

•  不要斬斷鎖鏈

•  如何自學

每日計劃

每一個主題都不須要用一成天來徹底理解它們，你能夠天天完成它們中的多個。

天天我都會從下面的列表中選一個出來，一遍又一遍的讀，作筆記，練習，用Python或R語言實現它。

動機

•  夢

機器學習概論

•  形象的機器學習簡介

•  一份溫柔的機器學習指南

•  爲開發者準備的機器學習簡介

•  菜鳥的機器學習基礎

•  你如何向非計算機專業的人來解釋機器學習與數據挖掘？

•  在罩子下的機器學習，博文簡單明瞭地介紹了機器學習的原理

•  機器學習是什麼？它是如何工做的呢？

•  深度學習——一份非技術性的簡介

掌握機器學習

•  掌握機器學習的方法

•  程序員的機器學習

•  掌握並運用機器學習

•  Python機器學習小課程

•  機器學習算法小課程

有趣的機器學習

•  機器學習真有趣！

•  Part 2: 使用機器學習來創造超級馬里奧的關卡

•  Part 3: 深度學習與卷積神經網絡

•  Part 4: 現代人臉識別與深度學習

•  Part 5: 翻譯與深度學習和序列的魔力

•  Part 6: 如何使用深度學習進行語音識別

•  Part 7: 使用生成式對抗網絡創造 8 像素藝術

•  Part 8: 如何故意欺騙神經網絡

機器學習簡介(用手指沾上墨水來書寫機器學習簡介)

•  Part 1 : 什麼是機器學習？

•  Part 2 : 監督學習與非監督學習

一本深刻的機器學習指南

•  概述，目標，學習類型和算法

•  數據的選擇，準備與建模

•  模型的評估，驗證，複雜性與改進

•  模型性能與偏差分析

•  無監督學習，相關領域與實踐中的機器學習

故事與經驗

•  一週的機器學習

•  一年的機器學習

•  我是如何在3天內寫出個人第一個機器學習程序的

•  學習路徑：你成爲機器學習專家的導師

•  不是PhD你也能夠成爲機器學習的搖滾明星

•  如何6個月成爲一名數據科學家：一名黑客的職業規劃

• 視頻

• 幻燈片

•  5個你成爲機器學習工程師必需要掌握的技能

•  你是一個自學成才的機器學習工程師嗎？你是怎麼作的？花了多長時間？

•  一我的如何成爲一名優秀的機器學習工程師？

•  一個專一於機器學習的學術假期

機器學習算法

•  用「士兵」來表示10種機器學習算法

•  Top10的數據挖掘算法

•  介紹10種機器學習的術語

•  機器學習算法之旅

•  機器學習工程師須要知道的10種算法

•  比較監督學習算法

• 收集的最簡化、可執行的機器學習算法

入門書籍

•  《Data Smart: Using Data Science to Transform Information into Insight》第 1 版

•  《Data Science for Business: What you need to know about data mining and data analytic-thinking》

•  《Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die》

實用書籍

•  Hacker 的機器學習

• GitHub repository(R)

• GitHub repository(Python)

•  Python 機器學習

• GitHub repository

•  集體智慧編程: 建立智能 Web 2.0 應用

•  機器學習: 算法視角，第二版

• GitHub repository

• Resource repository

•  Python 機器學習簡介: 數據科學家指南

• GitHub repository

•  數據挖掘: 機器學習工具與技術實踐，第 3 版

• Teaching material        - 1-5 章幻燈片(zip)        - 6-8 章幻燈片(zip)

•  Machine Learning in Action

• GitHub repository

•  Reactive Machine Learning Systems(MEAP)

• GitHub repository

•  An Introduction to Statistical Learning

• GitHub repository(R)

• GitHub repository(Python)    - 視頻

•  使用 Python 構建機器學習系統

• GitHub repository

•  學習 scikit-learn: 用 Python 進行機器學習

• GitHub repository

•  Probabilistic Programming & Bayesian Methods for Hackers

•  Probabilistic Graphical Models: Principles and Techniques

•  Machine Learning: Hands-On for Developers and Technical Professionals

• Machine Learning Hands-On for Developers and Technical Professionals review

• GitHub repository

•  從數據中學習    - 在線教程

•  強化學習——簡介(第 2 版)

• GitHub repository

•  使用TensorFlow(MEAP)進行機器學習

• GitHub repository

Kaggle知識競賽

•  Kaggle競賽：怎麼樣，在哪裏開始？

•  一個初學者如何用一個小項目在機器學習入門並在Kaggle競爭

•  如何競爭Kaggle的Master

系列視頻

•  Machine Learning for Hackers

•  Fresh Machine Learning

•  Josh Gordon 的機器學習菜譜

•  在 30 分鐘之內瞭解機器學習的一切

•  一份友好的機器學習簡介

•  Nuts and Bolts of Applying Deep Learning - Andrew Ng

•  BigML Webinar    - 視頻    - 資源

•  mathematicalmonk's Machine Learning tutorials

•  Machine learning in Python with scikit-learn

• GitHub repository    - 博客

•  播放清單 - YouTuBe 上最熱門的機器學習、神經網絡、深度學習視頻

•  16 個必看的機器學習教程

•  DeepLearning.TV

•  Learning To See

•  神經網絡課程 - Université de Sherbrooke

•  2016年的21個深度學習視頻課程

•  2016年的30個頂級的機器學習與人工智能視頻教程 Top Videos, Tutorials & Courses on Machine Learning & Artificial Intelligence from 2016

•  程序員的深度學習實戰

MOOC

•  edX 的人工智能導論

•  Udacity的機器學習導論

• 複習Udacity機器學習導論

•  Udacity的監督學習、非監督學習及深刻

•  Machine Learning Foundations: A Case Study Approach

•  Coursera的機器學習

• 視頻

• 複習Coursera機器學習

• Coursera的機器學習路線圖

•  機器學習提純

•  BigML training

•  Coursera的神經網絡課程

• 由Geoffrey Hinton（神經網絡的先驅）執教

•  使用TensorFlow建立深度學習應用

•  描述統計學概論

•  推理統計學概論

•  6.S094: 自動駕駛的深度學習

•  6.S191: 深度學習簡介

•  Coursera 深度學習教程

資源

•  一個月學會機器學習

•  一份「非技術性」的機器學習與人工智能指南

•  Google機器學習工程師最佳實踐教程

•  Hacker News的《軟件工程師的機器學習》

•  開發者的機器學習

•  爲人類🤖👶準備的機器學習

•  給開發者的關於機器學習的建議

•  機器學習入門

•  爲新手準備的機器學習入門教程

•  初學者如何自學機器學習

•  機器學習自學資源

•  提高你的機器學習技能

•  一份'坦誠'的機器學習指南

•  用機器學習讓Hacker News更具可讀性

• 視頻

• 幻燈片

•  深刻機器學習

•  軟件工程師的{機器、深度}學習

•  深度學習入門

•  深度學習基礎

•  機器學習思惟導圖/小抄

• 大學中的機器學習課程

•  斯坦福

•  機器學習夏令營

•  牛津

•  劍橋

• Flipboard的主題

• 機器學習

• 深度學習

• 人工智能

• Medium的主題

• 機器學習

• 深度學習

• 人工智能

• 每個月文章Top10

• 2016年9月        - 2016年10月-11月

• 2016年7月

• 2016年8月

• 2016年9月        - 2016年10月        - 2016年11月        - 2016年

• 機器學習

• 算法

• 全面的數據科學家的資源

• DigitalMind的人工智能資源

• 使人驚歎的機器學習

• CreativeAi的機器學習

成爲一名開源貢獻者

•  tensorflow/magenta: Magenta: 用機器智能生成音樂與藝術

•  tensorflow/tensorflow: 使用數據流圖進行計算進行可擴展的機器學習

•  cmusatyalab/openface: 使用深層神經網絡進行面部識別

•  tensorflow/models/syntaxnet: 神經網絡模型語法

遊戲

• Halite：AI編程遊戲

• Vindinium: 挑戰AI編程

• Video Game AI比賽

• 憤怒的小鳥AI比賽

• The AI Games

• Fighting Game AI Competition

• CodeCup

• 星際爭霸AI學生錦標賽

• AIIDE星際爭霸AI競賽

• CIG星際爭霸AI競賽

• CodinGame - AI Bot Games

播客

• 適合初學者的播客：

• Talking Machines

• Linear Digressions

• Data Skeptic

• This Week in Machine Learning & AI

• 「更多」進階的播客：

• Partially Derivative

• O’Reilly Data Show

• Not So Standard Deviation

• 盒子外的播客：

• Data Stories

社區

• Quora

• 機器學習

• 統計學

• 數據挖掘

• Reddit

• 機器學習

• 計算機視覺

• 天然語言處理

• 數據科學

• 大數據

• 統計學

• Data Tau

• Deep Learning News

• KDnuggets

相關會議

 - (NIPS)

• (ICLR)

• (AAAI)  - (IEEE CIG)  - (IEEE ICMLA)

• (ICML)

面試問題

•  如何準備機器學習職位的面試

•  40個機器學習與數據科學的面試問題

•  21個必需要知道的數據科學問題與回答

•  Top 50 機器學習面試問題與回答

•  機器學習面試問題

•  經常使用的機器學習面試問題

•  機器學習面試問題有哪些相同的？

•  什麼是評價一個機器學習研究者的最好的問題？

•  機器學習面試問題大搜集

•  121個須要掌握的問題與回答

我崇拜的公司

•  ELSA - 你虛擬的口語教練