Resource
============================

## Textbook

There is no required textbook for this course. We will make lecture slides and examples used in class available. Additional references are listed below. 

### Deep Learning
1. [Deep learning](https://www.deeplearningbook.org) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (theory and concepts; available online)
2. [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) by Michael Nielsen (concepts and examples; online book)
3. [Deep Learning with Python](https://github.com/fchollet/deep-learning-with-python-notebooks) by Francois Chollet (learning through examples; Keras)

 

### Practical Deep Learning
1. [Machine Learning Yearning](https://www.deeplearning.ai)  by Andrew Ng (practical concepts; available online)
2. [Hands-On Machine Learning with Scikit-Learn and TensorFlow](https://github.com/ageron/handson-ml) by Aurélien Géron (ML and Tensorflow)
3. [TensorFlow Deep Learning Cookbook](https://github.com/PacktPublishing/TensorFlow-1x-Deep-Learning-Cookbook) by Antonio Gulli and Amita Kapoor (cookbook examples)

 

### Machine Learning
1. [A Course in Machine Learning](http://ciml.info/) by Hal Daume III (Introduction; available online)
2. [Machine Learning: A Probabilistic Perspective](https://github.com/probml) by Kevin Murphy (Extensive text)


## Computational Platform

Practical implementation of neural networks requires a computational platform which includes convenient libraries of deep learning elements and operations. When choosing a platform, aspects such as efficiency and scalability for various types of networks, efficient training, usage with various architectures such as GPU/ multiple CPU and seamless usage with cloud services should be taken into account. There are several platforms that support these requirements, such as TensorFlow, Pytorch, Keras, Caffe 2, ONNX, etc.

The material in this course is universal. Network types and algorithms which we will cover can be implemented on various platforms. 

For consistency, we will use the TensorFlow (Links to an external site.) platform with Python throughout the course since it is the most fundamental end-end comprehensive platform. Indeed, many existing codes and examples are in TensorFlow. With a solid knowledge of TensorFlow, you will be able to quickly learn to work with other platforms when needed.

Follow the instructions in the Kaggle setup to work on each of the assignment. You can also use Google CoLab or Local Platform to work on the assignment and upload jupyter notebook to Kaggle for submission.

## Useful manual and tutorial 
### Manual 
1. [Numpy](https://numpy.org/doc/stable) is the fundamental package for scientific computing in Python.
2. [Pandas](https://pandas.pydata.org/docs/user_guide/index.html) is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
3. [Uproot](https://uproot.readthedocs.io/en/latest/) is a library for reading (and soon, writing) ROOT files in pure Python and NumPy.
4. [h5py](https://docs.h5py.org/en/stable) package is a Pythonic interface to the HDF5 binary data format.
5. [Matplotlib](https://matplotlib.org/stable/contents.html) is a comprehensive library for creating static, animated, and interactive visualizations in Python.

### Tutorial 
1. [Fundamental Python tutorial](https://youtube.com/playlist?list=PL-g0fdC5RMboYEyt6QS2iLb_1m7QcgfHk) (zh-tw, using chinese)
2. [2020 Machine Learning Roadmap (still valid for 2021)](https://www.youtube.com/watch?v=pHiMN_gy9mk)
3. [ML learning map create by NTU Professor Hung-yi Lee](http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML20.html)
4. [ML tutorial provided by Pytorch](https://pytorch.org/tutorials/)
5. [NumPy Cheat Sheet](https://www.datacamp.com/community/blog/python-numpy-cheat-sheet)
6. [Keras Cheat Sheet](https://www.datacamp.com/community/blog/keras-cheat-sheet)
7. [Pandas Cheat Sheet](https://www.datacamp.com/community/blog/pandas-cheat-sheet-python)
8. [Python Data Visualization Cheat Sheet](https://www.datacamp.com/community/blog/bokeh-cheat-sheet-python)