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Kamil Krzyk - My Road to AI

About

This is a repository that I have created to track my progress in AI/Data Science related topics in order to organise my knowledge and goals. Purpose of doing this is self-motivation, open source/study material for others, portfolio and TODO list.

Table of contents

AI Related Presentations

Presentation Where Date Slides
Welcome to MOOC era! - My experiences with Deep Learning Foundations Nanodegree at Udacity Speaker - GDG & Women Techmakers - Machine Learning #3 18.10.2017 Link
Soft introduction into MSE based Linear Regression (part 2 of 'What this Machine Learning is all about?' talk) Azimo Lunch & Learn 16.11.2017 Link
Advantages of Batch Normalization in Deep Learning PyData Warsaw, Let’s meet to talk about AI in Bialystok #2 10.04.2018, 01.06.2018 Link

Mini AI Projects

In this section I will focus about digging up relationship and visualising data. I will try to use Machine learning and visualisation methods for problem solving.

Problem Solving

Problem Description Implementation Dataset Creation Date Last Update
Prediction of Bike Shop Clients Number Used MLP with 1-layer, mini-batch Python (numpy, matplotlib) Bike-Sharing 13.08.2017 13.08.2017
Kaggle - Titanic Disaster survivor prediction Used Logistic Regression with ~80% accuracy Python (raw) Titanic Disaster 19.10.2017 24.10.2017

Visualisation

Problem Description Implementation Dataset Creation Date Last Update
Picking best computer game to try Used K-Means Clusters for visualising top positions Python (raw) Kaggle - Video Game Sales 01.10.2017 05.10.2017

AI Programming Showcase

In this section I want to show off my knowledge about various AI related algorithms, frameworks, programming languages, libraries and more. Priority is to show how the algorithm works - not to solve complex and ambitious problems. Usually on classical or generated datasets.

Raw Python

Machine Learning

Algorithm Description Implementation Dataset Creation Date Last Update
Linear Regression - Python (raw) Generated Numbers 18.04.2017 15.09.2017
Ridge Regression Compared result with Linear Regression Python (raw) Generated Numbers 23.09.2017 23.09.2017
Polynomial Regression Approximating Polynomial of degree 2 Python (raw) Generated Numbers 08.06.2017 15.09.2017
Polynomial Regression Approximating Polynomial of degree 3 Python (raw) Generated Numbers 10.06.2017 15.09.2017
KNN Manhattan, Euclidean Similarity Python (raw) iris 21.07.2017 24.09.2017
PCA - Python (raw) Generated Numbers 01.04.2017 23.09.2017
Naive Bayes Gaussian Distribution Python (raw) Pima Indian Diabetes 02.11.2017 03.11.2017

Deep Learning

Net Type Problem Description Implementation Dataset Creation Date Last Update
MLP Digit Classification 2-layers, mini-batch Python (raw) MNIST 19.06.2017 14.08.2017

sklearn

Algorithm Description Implementation Dataset Creation Date Last Update
Linear Regression - Python (sklearn) Generated Numbers 18.04.2017 15.09.2017
Polynomial Regression Approximating Polynomial of degree 2 Python (sklearn) Generated Numbers 10.06.2017 15.09.2017
Polynomial Regression Approximating Polynomial of degree 3 Python (sklearn) Generated Numbers 10.06.2017 15.09.2017
KNN Euclidean Similarity Python (sklearn) iris 22.07.2017 24.09.2017

TensorFlow

Machine Learning

Algorithm Description Implementation Dataset Creation Date Last Update
Linear Regression - Python (Tensorflow) Generated Numbers 23.09.2017 23.09.2017

Deep Learning

Net Type Problem Description Implementation Dataset Creation Date Last Update
MLP Digit Classification 2-layers, mini-batch, dropout-regularization Python (Tensorflow) MNIST 29.06.2017 18.07.2017
MLP Encrypting data with Autoencoder 1-layer Encoder, 1-layer Decoder, mini-batch Python (Tensorflow) MNIST 13.07.2017 13.07.2017
MLP Digit Classification tf.layer module, dropout regularization, batch normalization Python (Tensorflow) MNIST 16.08.2017 23.08.2017
CNN 10 Classes Color Images Classification tf.nn module, dropout regularization Python (Tensorflow) CIFAR-10 16.08.2017 07.09.2017
CNN 10 Classes Color Images Classification tf.layer module, dropout regularization Python (Tensorflow) CIFAR-10 16.08.2017 09.09.2017
CNN 10 Classes Color Images Classification tf.layer module, dropout regularization, batch normalization Python (Tensorflow) CIFAR-10 19.08.2017 10.09.2017
RNN Simple Language Translator In form of my DLFND project for now Python (Tensorflow) Small part of French-English corpus 05.05.2017 24.05.2017
RNN "The Simpsons" Script Generation In form of my DLFND project for now Python (Tensorflow) "The Simpsons" script 06.06.2017 14.07.2017
DCGAN Generating Human Face Miniatures DCGAN Python (Tensorflow) CelebA 11.09.2017 13.09.2017

Keras

Net Type Problem Description Implementation Dataset Creation Date Last Update
MLP Digit Classification 2-layers, mini-batch, BN Python (Keras) MNIST 10.03.2018 10.03.2018
MLP Clothes Images Classification 2-layers, mini-batch, BN Python (Keras) Fashion MNIST 15.04.2018 15.04.2018
MLP Letters Classification 2-layers, mini-batch, BN Python (Keras) EMNIST 24.04.2018 24.04.2018
MLP Review Sentiment Classification Bag of Words Python (Keras) IMDB Reviews 11.03.2018 11.03.2018
MLP Boston House Prices Regression 1 layer, mini-batch Python (Keras) Boston House Prices 19.04.2018 19.04.2018
CNN Ten Color Image Classes Classification VGG15 Python (Keras) CIFAR10 27.03.2018 27.03.2018
CNN Letter Classification 32x32x64x64, 512, BN Python (Keras) EMNIST 25.03.2018 23.03.2018
CNN Clothes Images Classification 16x16x32x32, 256x128, BN Python (Keras) Fashion MNIST 11.03.2018 11.03.2018
CNN Digit Classification 16x32x64, 128, BN Python (Keras) MNIST 24.03.2018 24.03.2018
RNN Next Month Prediction LSTM(128) Python (Keras) Month Order 15.04.2018 15.04.2018
RNN Shakespeare Sonnet's Generation LSTM(700), LSTM(700) Python (Keras) Shakespeare's sonnets 17.04.2018 17.04.2018

Articles

Note: Delayed due to 1,5 month long preparations for organising ML/DL workshops.

Title Link Jupyter Publsh Date Update Date
Coding Deep Learning for Beginners — Start! Medium - 12.02.2018 12.02.2018

Courses & Certificates

When I was younger I played a lot of computer games. I still tend to play today a little as a form of relax and to spend time with friends that live far from me. One thing that I have very enjoyed about gaming was gathering trophies. You made an effort to complete list of challenges or get a great score and then looked at list of your achievements with satisfaction. My current self have inherited this habit and as I study on daily basis I like to gather proves that I have done something - to make it more like a game where each topic is a boss that you have to clear on hard mode. Of course what's in your head is most important but if it helps to motivate you, then why not?

Sources

There is a list of sources that I have used (and found helpful in some way) or keep using in order to produce my repo content.

Contact