# Table of Contents 1. [Context](#org6dbe148) 2. [Structure of the Repository](#org65603ff) 3. [PORTFOLIO](#orga34978d) 1. [Projects](#org6755429) 4. [Education](#orgcfaa906) 5. [PROFICIENCY](#orge067afe) # Context This repo is my 10,000 Hours of Machine Learning. **IMPORTANT: this repository eventually grew in scope and thus [abaj.ai](https://abaj.ai) was born. All of the files in this repository are a submodule of that repo, and all of the code is tangled with org files in the site** > "Machine Learning is just lego for adults" - Dr. Kieran Samuel Owens > "S/he who has a why, can bear almost any how." - Friedrich Nietzche *Why do anything else, when the thing that you could do, would do, everything else?* --- To become an expert at anything, there is a common denominator:

10,000 hours of deliberate practise on the subject.

# Structure of the Repository There are 3 main features: 1. PORTFOLIO - Briefly, these are solutions to **classical** problems, MNIST, Boston Housing, XOR, etc. 2. EDUCATION - This folder contains coursework from my universities and MOOCs (that which I am allowed to share). Additionally my textbook solutions are included here. 3. PROFICIENCY - These are my more complex and non-trivial projects. They are more fun, but also more novel and thus less deterministic; Kanye West chatbot, Peg Solitare Reinforcement Learner, Ultimate Frisbee Computer Vision, etc. # PORTFOLIO In no particular order, here are a list of the methods you will find in the notebooks. The emphasis is on understanding their limitations, benefits and constructions. - Least Squares Regression - Random Forests - Boosting, Bagging - Ensemble Methods - Multilayer Perceptrons - Naive Bayes - K-means regression - K-nearest Neighbours Clustering - Logistic Regression - Decision Trees - SVM - Kernel Methods - GAN's - Stable Diffusion - Recurrent Neural Networks - Convolutional Neural Networks - Transformers - word2vec, GLoVE and NLP - LLM ## Projects To gain proficiency in all of the above methods, I have solved classical problems that lend themselves well to that particular method:
Dataset Accuracy Model
MNIST 92% Logistic Regression
FMNIST B% Random Forest
KMNIST C% 2-layer CNN
CIFAR D% CNN
IRIS E% SVM
ImageNet F% ResNet50
Sentiment140 G% LSTM
Boston Housing H% Linear Regression
Wine Quality I% Gradient Boosting
Pima Indians Diabetes J% Decision Tree
IMDB Reviews K% BERT
KDD Cup 1999 L% K-Means Clustering
Digits M% Gaussian Mixture Model
CartPole N% Deep Q-Network
# Education For mastery, a formal education is also required; either by way of open-courseware, or by paying an institution. I have done both, and overall benefitted as a result. - [X] UNSW AI - [X] UNSW Machine Learning and Data Mining - [X] UNSW Deep Learning and Neural Networks - [ ] UNSW Computer Vision - [ ] Stanford CS229 (Machine Learning) - [ ] Stanford CS230 (Deep Learning) - [ ] Mathematics for Machine Learning, Ong et al. - [ ] HOML (Hands on Machine Learning) - [ ] All of Statistics, Larry Wasserman - [X] Coursera Machine Learning Specialisation - [X] Coursera Deep Learning Specialisation # PROFICIENCY To become proficient, I have applied my ML skills to solve problems of personal and social interest. - [ ] Kanye West Producer - [ ] KiTS19 Grand Challenge: Kidney and Kidney Tumour Segmentation - [ ] Non-descriptive Ultimate Frisbee Statistics - [ ] OCR - [ ] Peg Solitaire RL > "Read 2 papers a week" - Andrew Ng