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洋書 - TensorFlow for Deep Learning

AI / Machine Learning Python

TensorFlow for Deep Learning

TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

Table of Contents

1. Introduction to Deep Learning

2. Introduction to TensorFlow Primitives

3. Linear and Logistic Regression with TensorFlow

4. Fully Connected Deep Networks

5. Hyperparameter Optimization

6. Convolutional Neural Networks

7. Recurrent Neural Networks

8. Reinforcement Learning

9. Training Large Deep Networks

10. The Future of Deep Learning

Python - Deep Learning / TensorFlow参考情報

AI / Machine Learning Python

Deep Learning参考ビデオ・書籍

Lynda.com

Safari Online

その他リンク

Amazonからの購入

Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

実践 Deep Learning ―PythonとTensorFlowで学ぶ次世代の機械学習アルゴリズム (オライリー・ジャパン)

実践 Deep Learning ―PythonとTensorFlowで学ぶ次世代の機械学習アルゴリズム (オライリー・ジャパン)

TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

Python - Data Analysis (NumPy, SciPy, Pandas) 参考情報

AI / Machine Learning Python

Data Analysis参考ビデオ・書籍

Lynda.com

Safari Online

その他リンク

f:id:blog-guy:20181215212539p:plain

Amazonからの購入

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Hands-On Data Analysis with NumPy and pandas: Implement Python packages from data manipulation to processing (English Edition)

Hands-On Data Analysis with NumPy and pandas: Implement Python packages from data manipulation to processing (English Edition)

Python - OpenCV参考情報

AI / Machine Learning Python

OpenCV Python参考ビデオ・書籍

Lynda.com

Safari Online

その他リンク

Amazonからの購入

OpenCV 3.x with Python By Example: Make the most of OpenCV and Python to build applications for object recognition and augmented reality, 2nd Edition

OpenCV 3.x with Python By Example: Make the most of OpenCV and Python to build applications for object recognition and augmented reality, 2nd Edition

Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library

Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

洋書 - Hands-On Machine Learning with Scikit-Learn and TensorFlow

AI / Machine Learning IT / 組込み / テクノロジー書籍

Hands-On Machine Learning with Scikit-Learn and TensorFlow

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Table of Contents

I. The Fundamentals of Machine Learning

  • 1. The Machine Learning Landscape
  • 2. End-to-End Machine Learning Project
  • 3. Classification
  • 4. Training Models
  • 5. Support Vector Machines
  • 6. Decision Trees
  • 7. Ensemble Learning and Random Forests
  • 8. Dimensionality Reduction

II. Neural Networks and Deep Learning

  • 9. Up and Running with TensorFlow
  • 10. Introduction to Artificial Neural Networks
  • 11. Training Deep Neural Nets
  • 12. Distributing TensorFlow Across Devices and Servers
  • 13. Convolutional Neural Networks
  • 14. Recurrent Neural Networks
  • 15. Autoencoders
  • 16. Reinforcement Learning

Appendix

  • A. Exercise Solutions
  • B. Machine Learning Project Checklist
  • C. SVM Dual Problem
  • D. Autodiff
  • E. Other Popular ANN Architectures

Machine Learning勉強用podcast

AI / Machine Learning

www.shopify.com

紹介されているpodcast

  1. http://ocdevel.com/podcasts/machine-learning
  2. https://dataskeptic.com/
  3. https://twimlai.com/
  4. https://www.oreilly.com/topics/oreilly-data-show-podcast
  5. https://www.thetalkingmachines.com/
  6. https://www.datacamp.com/community/podcast
  7. http://lineardigressions.com/
  8. https://concerning.ai/
  9. http://datastori.es/
  10. http://www.learningmachines101.com/about-learning-machines-101/

Machine Learning (機械学習) 英語 - 英日対訳

AI / Machine Learning

基本中の基本

  • AI (Artificial Intelligence) : 人工知能
  • Machine Learning : 機械学習
  • Deep Learning : 深層学習・ディープラーニング

機械学習

機械学習の方法

  • Supervised Learning : 教師あり学習
  • Unsupervised Learning : 教師なし学習
  • Reinforcement Learning : 強化学習

機械学習の手法

  • Classification : 分類
    • Binary classification : 二項（2クラス）分類 +Multiclass classification : 多項（多クラス）分類
  • Regression : 回帰
  • Lazy Learning : 遅延学習 <=> Eager Learning
  • Logistics Regression : ロジスティック回帰
  • Linear SVM (Support Vector Machine) : 線形SVM
  • Non-Linear SVM (Support Vector Machine) : 非線形SVM
  • Decision Tree : 決定木
  • Random Forest : ランダムフォレスト
  • k-NN(Nearest Neighbors) : k近傍法

Deep Learning

  • Neural Network : ニューラルネットワーク
  • Deep Neural Network : ディープニューラルネットワーク
  • Weight Parameter : 重みパラメータ
  • CNN (Convolutional Neural Network) : 畳み込みニューラルネットワーク

本 - Pythonで動かして学ぶ！あたらしい深層学習の教科書 機械学習の基本から深層学習まで

AI / Machine Learning Python

Pythonで動かして学ぶ! あたらしい深層学習の教科書 機械学習の基本から深層学習まで (AI & TECHNOLOGY)

Pythonで動かして学ぶ! あたらしい深層学習の教科書 機械学習の基本から深層学習まで (AI & TECHNOLOGY)

目次

  • 第1章から第3章 : 機械学習の基本
  • 第4章から第6章 : Pythonの基礎知識
  • 第7章から第9章 : NumPyやPandasの基礎知識
  • 第10章から第13章 : 可視化の基礎知識
  • 第14章から第15章 : データの扱い方の基本
  • 第16章から第18章 : 教師あり学習やハイパーパラメータとチューニング
  • 第19章から第22章 : 深層学習について基本か応用

サンプル実行環境構築 (自分の環境)

洋書 - Machine Learning with Python Cookbook

AI / Machine Learning Python

Machine Learning with Python Cookbook

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

評価

約200個のわかりやすいサンプルコードが収録。真面目に勉強するには非常によい！

Table of Contents

1. Vectors, Matrices and Arrays [numpy]
  • 基本 : np.array(), scipy.sparse.csr_matrix(matrix), matrix.shape, matrix.size, matrix.ndim, np.vectorize(lamdba function), np.max(matrix), np.min(matrix), np.mean(matrix), np.var(matrix), np.std(matrix)
  • 高度な関数・メソッド : matrix.reshape(m, n), matrix.T, matrix.flatten(), np.linalg.matrix_rank(matrix), np.linalg.det(matrix), matrix.diagnal(), matrix.trace(), np.linalg.eig(matrix), np.dot(vector_a, vector_b), np.add(matrix_a, matrix_b), np.subtract(matrix_a, matrix_b), np.dot(matrix_a, matrix_b), np.linalg.inv(matrix)
  • Random : np.random.seed(n), np.random.random(n), np.random.logistic(...), np.random.uniform(...)
2. Loading Data [scikit-learn, pandas]
  • "toy" datasets : load_boston, load_iris, load_digits, data, target
  • make_regression, make_classification, make_blobs
  • pandas.read_csv(url), pandas.read_json(url), pandas.read_sql_query(...)
3. Data Wrangling [pandas]
  • 基本 : pd.read_csv(url), pd.DataFrame(), dataframe['Col1'] = [val1, val2], dataframe.shape, dataframe.describe()
  • Search : dataframe.iloc[1:4], dataframe.loc["Allen"], dataframe[(dataframe['Sex'] == 'female')]
  • Replace / Delete / Merge... : dataframe.replace(1, "One"), dataframe.drop_duplicates(), pd.concat(...), pd.merge(...)
4. Handling Numerical Data [pandas, scikit-learn]
5. Handling Categorical Data [pandas, scikit-learn]
6. Handling Text [NLTK, scikit-learn]
  • Python標準string : string.strip() for string in text_data, string.replace(".", "") for string in strip_whitespace, string.upper()
  • Regular Expression : re.sub
  • scraping HTML : BeautifulSoup
  • NLTK(Natural Language Toolkit)
7. Handling Dates and Times [pandas]
8. Handling Images [OpenCV, matplotlib]
  • OpenCV : cv2.imread("images/plane.jpg"), cv2.imwrite("images/plane_new.jpg", image), cv2.resize(image, (256, 256)), image_cropped = image[:,:128], cv2.blur(image, (5,5))
9. Dimensionality Reduction Using Feature Extraction [scikit-learn]
10. Dimensionality Reduction Using Feature Selection [scikit-learn]
11. Model Evaluation [scikit-learn]
12. Model Selection [scikit-learn]
13. Linear Regression [scikit-learn]
14. Trees and Forests [scikit-learn]
15. K-Nearest Neighbors [scikit-learn]
16. Logistic Regression [scikit-learn]
17. Support Vector Machines [scikit-learn]
18. Naive Bayes [scikit-learn]
19. Clustering [scikit-learn]
20. Neural Networks [keras]
21. Saving and Loading Trained Models [scikit-learn, keras]

サンプル環境構築 (自分の環境)

  • OS : Windows 10
  • Python : Python 3.6.7 64 bit (Anacondaは容量が多きぎるので、使っていない。標準pythonとpipのみで問題ない！)
    • 2018/12時点でtensorflowは3.7は未対応であるため、3.6.xをインストール
    • Pythonを入れなおす時、Uninstall後も下記のところを自分で綺麗に消さないと相当苦労する。
      • C:\Users\sejahn\AppData\Local\Programs\Python
      • C:\Users\sejahn\AppData\Local\pip
      • RegeditでPythonが入っているところ
    • PIP : Python 3.6.x同梱のPIP。「python -m pip install --upgrade pip」でpip upgradeした。
  • Library : 下記pip freeze結果（pip install時バージョンは指定しなくても大丈夫だった）
cycler==0.10.0
kiwisolver==1.0.1
matplotlib==3.0.2
numpy==1.15.4
pandas==0.23.4
pyparsing==2.3.0
python-dateutil==2.7.5
pytz==2018.7
scikit-learn==0.20.1
scipy==1.1.0
six==1.11.0
sklearn==0.0
  • Editor : なんでもいいだろうけど、sublime text