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: ...@@ -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) * [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/) * [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.) * [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 ## Curriculum
...@@ -55,12 +56,14 @@ The following give a survey (high-level overview of developments in the field) o ...@@ -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). * 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) * 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 #### Statistical Learning
This chapter in ISL introduces R and the concept of statistical learning and some _very important concepts_ for assessing model accuracy. 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) * 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 ### Week 2
...@@ -80,6 +83,7 @@ These papers discuss two key topics: how data is collected and how to work on a ...@@ -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. 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) * 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). * [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). * [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 ...@@ -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. 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) * 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 ### Future Weeks
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