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| A scene rendered with your own ray tracer |
I like learning by doing. In my opinion the only real self test whether you understood something is a working prototype. For example, when learning more about the details of deep learning or xgboost, I implemented my own auto differentiation and gradient boosted trees.
Recently I found several books that guide you through implementations of fairly complex pieces of software. After finishing a chapter it feels like making progress on a project. So it is easier to follow along and you don't have to search for all the references needed yourself. You still have the benefit from learning by doing.
The book based projects I found are:
- A ray tracer [1]
- A go AI [2]
- A bitcoin implementation [3]
- A Prolog interpreter [4]
The prolog interpreter needed a little more adjustment since the book's topic is classic
AI in lisp and I wanted to implement my prolog in scala.
The previous projects on neural networks were based on Goodfellow's Deep Learning book [5] and many other references such as online classes so it was not as easy to code along. The same is true for the gradient boosting implementation which was mostly based on papers and books.
However the other projects simply involved writing code and the book was the only thing required to get into the topic. The quality all of these books show is that it is not a collection of separated code pieces that explain a topic but the topic's are explained by a coherent code based that you develop yourself form scratch.
Harder parts include pseudo code. The book covers the basic math library and then uses the defined primitives to construct more and more complex features.
The end result is a full ray tracer with shadows reflections and multiple primitives.
My code bases are all on github. Some are work in progress [WIP] since
i did not finish the books yet. For example, in books 2 and 3 I only went through the first chapters:
AI in lisp and I wanted to implement my prolog in scala.
The previous projects on neural networks were based on Goodfellow's Deep Learning book [5] and many other references such as online classes so it was not as easy to code along. The same is true for the gradient boosting implementation which was mostly based on papers and books.
However the other projects simply involved writing code and the book was the only thing required to get into the topic. The quality all of these books show is that it is not a collection of separated code pieces that explain a topic but the topic's are explained by a coherent code based that you develop yourself form scratch.
1. A Ray Tracer
The book "The Ray Tracer Challenge: A Test Driven Guide to Your First 3D Rendered" [1], is giving you no actual code but behavior specifications (cucumber). The code has to be written by yourself.Harder parts include pseudo code. The book covers the basic math library and then uses the defined primitives to construct more and more complex features.
The end result is a full ray tracer with shadows reflections and multiple primitives.
2. A Go AI
In the next book: "Deep Learning And The Game Of Go" [2], you build a go AI using the code provided by the authors. The books walks you through the implementation of the go board and rules first. Then the authors walk through classic game AI algorithms such as monte carlo tree search. In the end the authors introduce game state evaluation using convolutional neural networks using Keras.
The resulting system resembles an early version of AlphaGO
3. Bitcoin
The book "Programming Bitcoin" [3] is a mix between the first two. Lot's of code is provided in the form of skeletons but the reader still has to fill in the blanks for several assignments. The book
covers everything from basic cryptography to scripting bitcoin. The end result is a full bitcoin
implementation.
Wrap up
The great thing about all the books mentioned is that all these books do cover the primitives needed too. You don't need to rely on a library but the authors walk you front to back.
My code bases are all on github. Some are work in progress [WIP] since
i did not finish the books yet. For example, in books 2 and 3 I only went through the first chapters:
REFERENCES:
[1] Buck, "The Ray Tracer Challenge: A Test Driven Guide to Your First 3D Rendered", Pragmatic Bookshelf, 2019
[2] Pumperla, Furguson, "Deep Learning And The Game Of Go", Manning, 2019
[3] Song, "Programming Bitcoin: Learn How To Program Bitcoin From Scratch", Oreilly, 2019
[4] Norvig: "Paradigmes of Artificial Intelligence Programming: Case Studies in Lisp", Morgan Kaufmann, 1992
[5] Ian Goodfellow, "Deep Learning", MIT Press, 2016
[5] Ian Goodfellow, "Deep Learning", MIT Press, 2016