References for the book Grokking Machine Learning

General references

• Github repository: www.github.com/luisguiserrano/manning
• YouTube videos: www.youtube.com/c/LuisSerrano
• General information: https://serrano.academy 
• Book information: https://serrano.academy/grokking-machine-learning

Courses

• Udacity machine learning nanodegree: https://www.udacity.com/course/machine-learning-engineer-nanodegree–nd009t
• Coursera machine learning course: https://www.coursera.org/learn/machine-learning
• Coursera machine learning specialization (University of Washington): https://www.coursera.org/specializations/machine-learning 
• End to end machine learning: https://end-to-end-machine-learning.teachable.com/courses

Blogs and YouTube channels

• Machine learning videos by Brandon Rohrer: https://www.youtube.com/user/BrandonRohrer
• StatQuest with Josh Starmer: https://www.youtube.com/user/joshstarmer
• Chris Olah blog: https://colah.github.io/
• Jay Alammar blog: https://jalammar.github.io/
• Alexis cook blog: https://alexisbcook.github.io/
• Dhruv Parthasarathy blog: https://medium.com/@dhruvp 
• 3blue1brown: http://youtube.com/c/3blue1brown 
• Machine learning mastery: https://machinelearningmastery.com 

Books

• Pattern Recognition and Machine Learning by Christopher Bishop: https://www.springer.com/gp/book/9780387310732 

Chapter 1

Videos

• General machine learning videos:
  https://serrano.academy/general-machine-learning/
• A friendly introduction to machine learning video: www.youtube.com/watch?v=IpGxLWOIZy4
• Monty Python spam sketch: www.youtube.com/watch?v=zLih-WQwBSc

Chapter 2

Datasets:

• MNIST dataset: http://yann.lecun.com/exdb/mnist/ 
• CIFAR dataset: https://www.cs.toronto.edu/~kriz/cifar.html 

Videos

• Supervised machine learning videos:
  https://serrano.academy/linear-models/
• Unsupervised machine learning videos:
  https://serrano.academy/unsupervised-learning/
• Generative machine learning videos:
  https://serrano.academy/generative-models 
• Reinforcement learning videos: https://serrano.academy/reinforcement-learning
• Deep learning videos: https://serrano.academy/neural-networks

Books

• Grokking Deep Reinforcement Learning by Miguel Morales: https://www.manning.com/books/grokking-deep-reinforcement-learning

Courses

• UCL course on reinforcement learning, by David Silver (http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
• Udacity Deep Reinforcement Learning Nanodegree Program. (https://www.udacity.com/course/deep-reinforcement-learning-nanodegree–nd893)

Chapter 3

Code

• Github repo: https://github.com/luisguiserrano/manning/tree/master/Chapter_3_Linear_Regression 

Datasets

• Hyderabad housing dataset: https://www.kaggle.com/ruchi798/housing-prices-in-metropolitan-areas-of-india (License: CC0: Public Domain)

Videos

• Linear regression video: https://www.youtube.com/watch?v=wYPUhge9w5c
• Polynomial regression video: https://www.youtube.com/watch?v=HmmkA-EFaW0

Chapter 4

Code:

• Github repo: https://github.com/luisguiserrano/manning/tree/master/Chapter_4_Testing_Overfitting_Underfitting 

Videos

• Machine learning: Testing and Error metrics: https://www.youtube.com/watch?v=aDW44NPhNw0 
• Ridge (L2) regression (StatQuest): https://www.youtube.com/watch?v=Q81RR3yKn30
• Lasso (L1) regression (StatQuest): https://www.youtube.com/watch?v=NGf0voTMlcs 

Chapter 5

Code

• Github repo: https://github.com/luisguiserrano/manning/tree/master/Chapter_5_Perceptron_Algorithm

Datasets

• IMDB movie reviews dataset: https://www.kaggle.com/stefanoleone992/imdb-extensive-dataset (License: CC0: Public Domain)

Videos

• Logistic regression and the perceptron algorithm video: https://www.youtube.com/watch?v=jbluHIgBmBo

Chapter 6

Code

• Github repo: https://github.com/luisguiserrano/manning/tree/master/Chapter_6_Logistic_Regression 

Videos

• Logistic regression and the perceptron algorithm video: https://www.youtube.com/watch?v=jbluHIgBmBo

Chapter 7

Videos

• Machine learning: Testing and Error metrics: https://www.youtube.com/watch?v=aDW44NPhNw0 

Chapter 8

Code

• Github repo:https://github.com/luisguiserrano/manning/tree/master/Chapter_8_Naive_Bayes

Datasets

• Spam filter dataset: https://www.kaggle.com/karthickveerakumar/spam-filter https://www.kaggle.com/karthickveerakumar/spam-filter

Videos

• Naive Bayes: https://www.youtube.com/watch?v=Q8l0Vip5YUw 

Chapter 9

Code

• Github repo: https://github.com/luisguiserrano/manning/tree/master/Chapter_9_Decision_Trees 

Datasets

• Admissions dataset: https://www.kaggle.com/mohansacharya/graduate-admissions?select=Admission_Predict.csv
  Mohan S Acharya, Asfia Armaan, Aneeta S Antony : A Comparison of Regression Models for Prediction of Graduate Admissions, IEEE International Conference on Computational Intelligence in Data Science 2019. (License: CC0: Public Domain)

Videos

• Decision trees (StatQuest): https://www.youtube.com/watch?v=7VeUPuFGJHk 
• Regression decision trees (StatQuest): https://www.youtube.com/watch?v=g9c66TUylZ4 
• Decision trees (Brandon Rohrer): https://www.youtube.com/watch?v=9w16p4QmkAI 
• Gini impurity index: https://www.youtube.com/watch?v=u4IxOk2ijSs 
• Shannon entropy and information gain: https://www.youtube.com/watch?v=9r7FIXEAGvs 

Blog posts

• Shannon entropy, information gain, and picking balls from buckets: https://medium.com/udacity/shannon-entropy-information-gain-and-picking-balls-from-buckets-5810d35d54b4  

Chapter 10

Code

• Github repo: https://github.com/luisguiserrano/manning/tree/master/Chapter_10_Neural_Networks 

Datasets

• MNIST dataset. Deng, L. (2012). The mnist database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine, 29(6), 141–142.
• Admissions dataset: https://www.kaggle.com/mohansacharya/graduate-admissions?select=Admission_Predict.csv
  Mohan S Acharya, Asfia Armaan, Aneeta S Antony : A Comparison of Regression Models for Prediction of Graduate Admissions, IEEE International Conference on Computational Intelligence in Data Science 2019

Videos

• Deep learning and neural networks: https://www.youtube.com/watch?v=BR9h47Jtqyw 
• Convolutional neural networks: https://www.youtube.com/watch?v=2-Ol7ZB0MmU 
• Recurrent neural networks: https://www.youtube.com/watch?v=UNmqTiOnRfg 
• How neural networks work (Brandon Rohrer): https://www.youtube.com/watch?v=ILsA4nyG7I0
• Recurrent neural networks (RNN) and Long short-term memory (LSTM) (Brandon Rohrer): https://www.youtube.com/watch?v=WCUNPb-5EYI 

Books

• Grokking Deep Learning, by Andrew Trask: https://www.manning.com/books/grokking-deep-learning
• Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: https://www.deeplearningbook.org/ 

Courses

• Udacity deep learning course: https://www.udacity.com/course/deep-learning-pytorch–ud188 

Blog posts 

• Using transfer learning to classify images with Keras, by Alexis Cook: https://alexisbcook.github.io/2017/using-transfer-learning-to-classify-images-with-keras/
• Global average pooling layers for object localization, by Alexis Cook: https://alexisbcook.github.io/2017/global-average-pooling-layers-for-object-localization/
• A brief history of CNNs in image segmentation: From R-CNN to Mask R-CNN, by Dhruv Parthasarathy: https://blog.athelas.com/a-brief-history-of-cnns-in-image-segmentation-from-r-cnn-to-mask-r-cnn-34ea83205de4?gi=baf5b651aa4f 
• Neural networks, manifolds, and topology, by Chris Olah: https://colah.github.io/posts/2014-03-NN-Manifolds-Topology/
• Understanding LSTM networks, by Chris Olah: https://colah.github.io/posts/2015-08-Understanding-LSTMs/ 
• How GPT3 works – Visualizations and animations, by Jay Alammar: https://jalammar.github.io/how-gpt3-works-visualizations-animations/ 
• How to configure the learning rate when training deep learning neural networks, by Jason Brownlee: https://machinelearningmastery.com/learning-rate-for-deep-learning-neural-networks
• Setting the learning rate of your neural network, by Jeremy Jordan: https://www.jeremyjordan.me/nn-learning-rate/
• Selecting the best architecture for artificial neural networks, by Ahmed Gad: https://heartbeat.fritz.ai/selecting-the-best-architecture-for-artificial-neural-networks-7b051f775b4

Tools

• TensorFlow playground: https://playground.tensorflow.org/

Chapter 11

Code

• Github repo: https://github.com/luisguiserrano/manning/tree/master/Chapter_11_Support_Vector_Machines

Datasets

• Hyderabad housing dataset: https://www.kaggle.com/ruchi798/housing-prices-in-metropolitan-areas-of-india (License: CC0: Public Domain)

Videos

• Support vector machines: https://www.youtube.com/watch?v=Lpr__X8zuE8 
• The polynomial kernel (StatQuest): https://www.youtube.com/watch?v=Toet3EiSFcM
• The radial (RBF) kernel (StatQuest): https://www.youtube.com/watch?v=Qc5IyLW_hns 

Blog posts

• Kernels and feature maps: Theory and intuition, by Xavier Bourret Sicotte: https://xavierbourretsicotte.github.io/Kernel_feature_map.html 

Chapter 12

Code

• Github repo: https://github.com/luisguiserrano/manning/tree/master/Chapter_12_Ensemble_Methods 

Videos

• Random forests (StatQuest): https://www.youtube.com/watch?v=J4Wdy0Wc_xQ 
• AdaBoost (StatQuest): https://www.youtube.com/watch?v=LsK-xG1cLYA
• Gradient boosting (StatQuest): https://www.youtube.com/watch?v=3CC4N4z3GJc
• XGBoost (StatQuest): https://www.youtube.com/watch?v=OtD8wVaFm6E

Articles and blog posts

• Explaining AdaBoost, by Robert Schapire: http://rob.schapire.net/papers/explaining-adaboost.pdf
• XGBoost: A Scalable Tree Boosting System, by Tiani Chen and Carlos Guestrin. KDD ‘16: Proceedings of the 22nd ACM SIGKDD International Conference onKnowledge Discovery and Data Mining, August 2016, Pages 785-794. https://doi.org/10.1145/2939672.2939785.
• Winning the Netflix prize: A summary, by Edwin Chen: https://blog.echen.me/2011/10/24/winning-the-netflix-prize-a-summary/

Chapter 13

Code

• Github repo: https://github.com/luisguiserrano/manning/tree/master/Chapter_13_End_to_end_example 

Datasets

• Titanic dataset: https://www.kaggle.com/c/titanic/data