Add ISL exercises for Week 1 and Week 2

parent 74f7e432
......@@ -32,6 +32,7 @@ Almost all of the material has come from the following sources:
* [Deep Learning Papers Reading Roadmap](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap)
* [Practical Deep Learning For Coders, Part 1](https://course.fast.ai/) from [Fast.ai](https://fast.ai/)
* [Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) (ISL) [[pdf]](http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf) (The famous text, [Elements of Statistical Learning](https://web.stanford.edu/~hastie/ElemStatLearn/), by the same authors is also fantastic, but requires significantly more math/stat preliminaries.)
* Dr. Gentle's (GMU) Spring 2015 [CSI 772](http://mason.gmu.edu/~jgentle/csi772/15s/) syllabus.
## Curriculum
......@@ -55,12 +56,14 @@ The following give a survey (high-level overview of developments in the field) o
* Three Giants' Survey: [Deep learning](http://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf); LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton (2015).
* Deep Learning 2018 [Lesson 1: Recognizing Cats and Dogs](https://course.fast.ai/lessons/lesson1.html)
* [Lesson 1 wiki](https://forums.fast.ai/t/wiki-lesson-1/9398)
#### Statistical Learning
This chapter in ISL introduces R and the concept of statistical learning and some _very important concepts_ for assessing model accuracy.
* Introduction to Statistical Learning: Chapter 2 [[pdf]](http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf)
* Exercises 3.1, 3.2, 3.3, and 3.8.
### Week 2
......@@ -80,6 +83,7 @@ These papers discuss two key topics: how data is collected and how to work on a
The following paper follow the "eve" of deep learning when deep learning showed promise and began to take off. The Fast.ai video introduces the Convolutional Neural Network (CNN) which was a milestone in deep learning.
* Deep Learning 2018 [Lesson 2: Convolutional Neural Networks](https://course.fast.ai/lessons/lesson2.html)
* [Lesson 2 wiki](https://forums.fast.ai/t/wiki-lesson-2/9399)
* [A fast learning algorithm for deep belief nets](http://www.cs.toronto.edu/~hinton/absps/ncfast.pdf); Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh (2006).
* [Reducing the dimensionality of data with neural networks](http://www.cs.toronto.edu/~hinton/science.pdf); Hinton, Geoffrey E., and Ruslan R. Salakhutdinov (2006).
......@@ -88,6 +92,7 @@ The following paper follow the "eve" of deep learning when deep learning showed
This chapter of ISL introduces regressions, a tool for finding relationships between two or more numerical data.
* Introduction to Statistical Learning: Chapter 3 [[pdf]](http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf)
* Exercises 3.5, 3.7, 3.9, and 3.14.
### Future Weeks
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