Why Deep Learning Is Suddenly Changing Your Life

 

http://fortune.com/ai-artificial-intelligence-deep-machine-learning

 

Decades-old discoveries are now electrifying the computing industry and will soon transform corporate America.

Over the past four years, readers have doubtlessly noticed quantum leaps in the quality of a wide range of everyday technologies.
Most obviously, the speech-recognition functions on our smartphones work much better than they used to. When we use a voice command to call our spouses, we reach them now. We aren’t connected to Amtrak or an angry ex.
In fact, we are increasingly interacting with our computers by just talking to them, whether it’s Amazon’s Alexa, Apple’s Siri, Microsoft’s Cortana, or the many voice-responsive features of Google. Chinese search giant Baidu says customers have tripled their use of its speech interfaces in the past 18 months.
Machine translation and other forms of language processing have also become far more convincing, with Google GOOGL 0.92% , Microsoft MSFT 0.38% , Facebook FB 1.13% , and Baidu BIDU 0.35% unveiling new tricks every month. Google Translate now renders spoken sentences in one language into spoken sentences in another for 32 pairs of languages, while offering text translations for 103 tongues, including Cebuano, Igbo, and Zulu. Google’s Inbox app offers three ready-made replies for many incoming emails.

Using Confusion Matrices to Quantify the Cost of Being Wrong

 

https://www.datasciencecentral.com/profiles/blogs/using-confusion-matrices-to-quantify-the-cost-of-being-wrong

How to visualize decision trees

http://explained.ai/decision-tree-viz/index.html

Python Machine Learning Book 2nd Edition

https://github.com/rasbt/python-machine-learning-book-2nd-edition

Python Machine Learning, 2nd Ed.
published September 20th, 2017
Paperback: 622 pages
Publisher: Packt Publishing
Language: English
ISBN-10: 1787125939
ISBN-13: 978-1787125933
Kindle ASIN: B0742K7HYF

Links

• Amazon Page
• Packt Page

Table of Contents and Code Notebooks

Helpful installation and setup instructions can be found in the README.md file of Chapter 1
To access the code materials for a given chapter, simply click on the open dir links next to the chapter headlines to navigate to the chapter subdirectories located in the code/ subdirectory. You can also click on the ipynb links below to open and view the Jupyter notebook of each chapter directly on GitHub.
In addition, the code/ subdirectories also contain .py script files, which were created from the Jupyter Notebooks. However, I highly recommend working with the Jupyter notebook if possible in your computing environment. Not only do the Jupyter notebooks contain the images and section headings for easier navigation, but they also allow for a stepwise execution of individual code snippets, which -- in my opinion -- provide a better learning experience.
Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.

1. Machine Learning - Giving Computers the Ability to Learn from Data [open dir] [ipynb]
2. Training Machine Learning Algorithms for Classification [open dir] [ipynb]
3. A Tour of Machine Learning Classifiers Using Scikit-Learn [open dir] [ipynb]
4. Building Good Training Sets – Data Pre-Processing [open dir] [ipynb]
5. Compressing Data via Dimensionality Reduction [open dir] [ipynb]
6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [open dir] [ipynb]
7. Combining Different Models for Ensemble Learning [open dir] [ipynb]
8. Applying Machine Learning to Sentiment Analysis [open dir] [ipynb]
9. Embedding a Machine Learning Model into a Web Application [open dir] [ipynb]
10. Predicting Continuous Target Variables with Regression Analysis [open dir] [ipynb]
11. Working with Unlabeled Data – Clustering Analysis [open dir] [ipynb]
12. Implementing a Multi-layer Artificial Neural Network from Scratch [open dir] [ipynb]
13. Parallelizing Neural Network Training with TensorFlow [open dir] [ipynb]
14. Going Deeper: The Mechanics of TensorFlow [open dir] [ipynb]
15. Classifying Images with Deep Convolutional Neural Networks [open dir] [ipynb]
16. Modeling Sequential Data Using Recurrent Neural Networks [open dir] [ipynb]

Keras callbacks guide and code

https://keunwoochoi.wordpress.com/2016/07/16/keras-callbacks/
the code is outdated for latest Keras, but still useful
updated version in https://github.com/keunwoochoi/keras_callbacks_example

Open Machine Learning Course mlcourse.ai

https://mlcourse.ai/

mlcourse.ai is an open Machine Learning course by OpenDataScience. The course is designed to perfectly balance theory and practice; therefore, each topic is followed by an assignment with a deadline in a week. You can also take part in several Kaggle Inclass competitions held during the course and work on your own projects.
Next session starts on October 1, 2018. Fill in this form to participate.
Navigation:

• Prerequisites. Our course is not for total newbies. Though Machine Learning is covered from scratch, still participants are expected to know some math and be able to write code in Python.
• Assignments. Here you’ll find demo versions of assignments. Assignments in a new session of the course will be different.
• News. Here you can track main announcements during the course.
• Resources. Links to other information mirrors of this course like Medium stories, Kaggle Kernels etc.
• Contacts. Ways of reaching OpenDataScience and course team.
• Support. Various ways in which you can help mlcourse.ai to grow.

Machine Learning Cheatsheet

Kumpulan machine learning cheatsheet

• The Machine Learning Cheatsheet https://github.com/remicnrd/ml_cheatsheet
• Machine Learning tips and tricks cheatsheet https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks
• Supervised Learning cheatshee: https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-supervised-learning 
• Deep Learning Cheatsheet: https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-deep-learning
• Machine Learning cheatsheets for Stanford's CS 229 https://github.com/afshinea/stanford-cs-229-machine-learning

Mark RCNN for segmentation

https://github.com/matterport/Mask_RCNN

Learning Rate Finder for Keras

Cyclical Learning Rates for Training Neural Networks https://arxiv.org/pdf/1506.01186.pdf
https://www.kaggle.com/paultimothymooney/learning-rate-finder-for-keras
https://github.com/LucasAnders1/LearningRateFinder
https://github.com/bckenstler/CLR