merged from jwasham/google-interview-university

README.md

@@ -1,7 +1,5 @@
 # Google Interview University
 
-[中文版本](https://github.com/xitu/google-interview-university/blob/master/README-cn.md)
-
 ## What is it?
 
 This is my multi-month study plan for going from web developer (self-taught, no CS degree) to Google software engineer.
@@ -65,6 +63,7 @@ sometimes word-for-word in Google's coaching notes.
     - [Implement system routines](#implement-system-routines)
     - [String searching & manipulations](#string-searching--manipulations)
 - [Final Review](#final-review)
+- [Coding Question Practice](#coding-question-practice)
 - [Books](#books)
 - [Coding exercises/challenges](#coding-exerciseschallenges)
 - [Once you're closer to the interview](#once-youre-closer-to-the-interview)
@@ -320,7 +319,7 @@ You can see my code here:
 
 You don't need to memorize the guts of every algorithm.
 
-Write code on a whiteboard, not a computer. Test with some sample inputs. Then test it out on a computer.
+Write code on a whiteboard or paper, not a computer. Test with some sample inputs. Then test it out on a computer.
 
 ## Prerequisite Knowledge
 
@@ -1184,6 +1183,16 @@ You'll get more graph practice in Skiena's book (see Books section below) and th
 - [ ] Merge Sort: https://www.youtube.com/watch?v=GCae1WNvnZM
    
 
+## Coding Question Practice
+
+- [ ] [Great intro (copied from System Design section): Algorithm design:](http://www.hiredintech.com/algorithm-design/)
+- [ ] [Mathematics for Topcoders](https://www.topcoder.com/community/data-science/data-science-tutorials/mathematics-for-topcoders/)
+- [ ] [Dynamic Programming – From Novice to Advanced](https://www.topcoder.com/community/data-science/data-science-tutorials/dynamic-programming-from-novice-to-advanced/)
+
+- [MIT Interview Materials](https://web.archive.org/web/20160906124824/http://courses.csail.mit.edu/iap/interview/materials.php)
+
+- [Exercises for getting better at a given language](http://exercism.io/languages)
+
 ## Books
 
 ### Mentioned in Google Coaching
@@ -1220,6 +1229,10 @@ You'll get more graph practice in Skiena's book (see Books section below) and th
 
 - [ ] [The Unix Programming Environment](http://product.half.ebay.com/The-UNIX-Programming-Environment-by-Brian-W-Kernighan-and-Rob-Pike-1983-Other/54385&tg=info)
 
+
+    These two were very recommended.
+
+
 - [ ] [Programming Pearls](http://www.amazon.com/Programming-Pearls-2nd-Jon-Bentley/dp/0201657880)
 
 - [ ] [Algorithms and Programming: Problems and Solutions](http://www.amazon.com/Algorithms-Programming-Solutions-Alexander-Shen/dp/0817638474)
@@ -1238,19 +1251,8 @@ You'll get more graph practice in Skiena's book (see Books section below) and th
 Once you've learned your brains out, put those brains to work.
 Take coding challenges every day, as many as you can.
 
-Programming Question Prep:
-
-- [ ] [Great intro (copied from System Design section): Algorithm design:](http://www.hiredintech.com/algorithm-design/)
 - [ ] [How to Find a Solution](https://www.topcoder.com/community/data-science/data-science-tutorials/how-to-find-a-solution/)
 - [ ] [How to Dissect a Topcoder Problem Statement](https://www.topcoder.com/community/data-science/data-science-tutorials/how-to-dissect-a-topcoder-problem-statement/)
-- [ ] [Mathematics for Topcoders](https://www.topcoder.com/community/data-science/data-science-tutorials/mathematics-for-topcoders/)
-- [ ] [Dynamic Programming – From Novice to Advanced](https://www.topcoder.com/community/data-science/data-science-tutorials/dynamic-programming-from-novice-to-advanced/)
-
-- [MIT Interview Materials](https://courses.csail.mit.edu/iap/interview/materials.php)
-
-- [Exercises for getting better at a given language](http://exercism.io/languages)
-
-Programming:
 
 - [LeetCode](https://leetcode.com/)
 - [TopCoder](https://www.topcoder.com/)
@@ -1584,16 +1586,20 @@ You're never really done.
     - [ ] [Tensorflow Tutorials](https://www.tensorflow.org/versions/r0.11/tutorials/index.html)
     - [ ] [Practical Guide to implementing Neural Networks in Python](using Theano)])http://www.analyticsvidhya.com/blog/2016/04/neural-networks-python-theano/)
     - Courses:
-        - [ ] [Great starter course: Machine Learning](https://www.coursera.org/learn/machine-learning)
+        - [Great starter course: Machine Learning](https://www.coursera.org/learn/machine-learning)
               - [videos only](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW)
               - see videos 12-18 for a review of linear algebra (14 and 15 are duplicates)
-        - [ ] [Neural Networks for Machine Learning](https://www.coursera.org/learn/neural-networks)
-        - [ ] [Google's Deep Learning Nanodegree](https://www.udacity.com/course/deep-learning--ud730)
-        - [ ] [Google/Kaggle Machine Learning Engineer Nanodegree](https://www.udacity.com/course/machine-learning-engineer-nanodegree-by-google--nd009)
-        - [ ] [Self-Driving Car Engineer Nanodegree](https://www.udacity.com/drive)
-        - [ ] [Metis Online Course ($99 for 2 months)](http://www.thisismetis.com/explore-data-science)
+        - [Neural Networks for Machine Learning](https://www.coursera.org/learn/neural-networks)
+        - [Google's Deep Learning Nanodegree](https://www.udacity.com/course/deep-learning--ud730)
+        - [Google/Kaggle Machine Learning Engineer Nanodegree](https://www.udacity.com/course/machine-learning-engineer-nanodegree-by-google--nd009)
+        - [Self-Driving Car Engineer Nanodegree](https://www.udacity.com/drive)
+        - [Metis Online Course ($99 for 2 months)](http://www.thisismetis.com/explore-data-science)
     - Resources:
-        - Great book: Data Science from Scratch: First Principles with Python: https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X
+        - Books:
+            - [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/)
+            - [Data Science from Scratch: First Principles with Python](https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X)
+            - [Introduction to Machine Learning with Python](https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413/)
+        - [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)
         - Data School: http://www.dataschool.io/
 
 - ### Go
@@ -1653,7 +1659,54 @@ Sit back and enjoy. "netflix and skill" :P
 
 - [ ] [Computer Science 70, 001 - Spring 2015 - Discrete Mathematics and Probability Theory](https://www.youtube.com/playlist?list=PL-XXv-cvA_iD8wQm8U0gG_Z1uHjImKXFy)
 
-- [ ] [Discrete Mathematics (19 videos)](https://www.youtube.com/playlist?list=PL3o9D4Dl2FJ9q0_gtFXPh_H4POI5dK0yG)
+- [ ] [Discrete Mathematics by Shai Simonson (19 videos)](https://www.youtube.com/playlist?list=PL3o9D4Dl2FJ9q0_gtFXPh_H4POI5dK0yG)
 
 - [ ] CSE373 - Analysis of Algorithms (25 videos)
-    - [Skiena lecture
+    - [Skiena lectures from Algorithm Design Manual](https://www.youtube.com/watch?v=ZFjhkohHdAA&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=1)
+
+- [ ] [UC Berkeley 61B (Spring 2014): Data Structures (25 videos)](https://www.youtube.com/watch?v=mFPmKGIrQs4&list=PL-XXv-cvA_iAlnI-BQr9hjqADPBtujFJd)
+
+- [ ] [UC Berkeley 61B (Fall 2006): Data Structures (39 videos)]( https://www.youtube.com/playlist?list=PL4BBB74C7D2A1049C)
+
+- [ ] [UC Berkeley 61C: Machine Structures (26 videos)](https://www.youtube.com/watch?v=gJJeUFyuvvg&list=PL-XXv-cvA_iCl2-D-FS5mk0jFF6cYSJs_)
+    
+- [ ] [OOSE: Software Dev Using UML and Java (21 videos)](https://www.youtube.com/playlist?list=PLJ9pm_Rc9HesnkwKlal_buSIHA-jTZMpO)
+
+- [ ] [UC Berkeley CS 152: Computer Architecture and Engineering (20 videos)](https://www.youtube.com/watch?v=UH0QYvtP7Rk&index=20&list=PLkFD6_40KJIwEiwQx1dACXwh-2Fuo32qr)
+
+- [ ] [MIT 6.004: Computation Structures (49 videos)](https://www.youtube.com/playlist?list=PLrRW1w6CGAcXbMtDFj205vALOGmiRc82-)
+
+- [ ] [Carnegie Mellon - Computer Architecture Lectures (39 videos)](https://www.youtube.com/playlist?list=PL5PHm2jkkXmi5CxxI7b3JCL1TWybTDtKq)
+
+- [ ] [MIT 6.006: Intro to Algorithms (47 videos)](https://www.youtube.com/watch?v=HtSuA80QTyo&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&nohtml5=False)
+
+- [ ] [MIT 6.033: Computer System Engineering (22 videos)](https://www.youtube.com/watch?v=zm2VP0kHl1M&list=PL6535748F59DCA484)
+
+- [ ] [MIT 6.034 Artificial Intelligence, Fall 2010 (30 videos)](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi)
+
+- [ ] [MIT 6.042J: Mathematics for Computer Science, Fall 2010 (25 videos)](https://www.youtube.com/watch?v=L3LMbpZIKhQ&list=PLB7540DEDD482705B)
+
+- [ ] [MIT 6.046: Design and Analysis of Algorithms (34 videos)](https://www.youtube.com/watch?v=2P-yW7LQr08&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)
+
+- [ ] [MIT 6.050J: Information and Entropy, Spring 2008 (19 videos)](https://www.youtube.com/watch?v=phxsQrZQupo&list=PL_2Bwul6T-A7OldmhGODImZL8KEVE38X7)
+
+- [ ] [MIT 6.851: Advanced Data Structures (22 videos)](https://www.youtube.com/watch?v=T0yzrZL1py0&list=PLUl4u3cNGP61hsJNdULdudlRL493b-XZf&index=1)
+
+- [ ] [MIT 6.854: Advanced Algorithms, Spring 2016 (24 videos)](https://www.youtube.com/playlist?list=PL6ogFv-ieghdoGKGg2Bik3Gl1glBTEu8c)
+
+- [ ] [HARVARD COMPSCI 224: Advanced Algorithms (25 videos)](https://www.youtube.com/playlist?list=PL2SOU6wwxB0uP4rJgf5ayhHWgw7akUWSf)
+
+- [ ] [MIT 6.858 Computer Systems Security, Fall 2014](https://www.youtube.com/watch?v=GqmQg-cszw4&index=1&list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh)
+
+- [ ] [Stanford: Programming Paradigms (27 videos)](https://www.youtube.com/view_play_list?p=9D558D49CA734A02)
+    
+- [ ] [Introduction to Cryptography by Christof Paar](https://www.youtube.com/playlist?list=PL6N5qY2nvvJE8X75VkXglSrVhLv1tVcfy)
+    - [Course Website along with Slides and Problem Sets](http://www.crypto-textbook.com/)
+    
+- [ ] [Mining Massive Datasets - Stanford University (94 videos)](https://www.youtube.com/playlist?list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV)
+
+## Computer Science Courses
+
+- [Directory of Online CS Courses](https://github.com/open-source-society/computer-science)
+- [Directory of CS Courses (many with online lectures)](https://github.com/prakhar1989/awesome-courses)
+