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.
- Kamil Krzyk - My Road to AI
- About
- Table of Contents
- AI Related Presentations
- AI Implementations
- Algorithm Implementations
- Books
- Courses & Certificates
- Sources
- Contact
Presentation | Where | Date | Slides |
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Welcome to MOOC era! - My experiences with Deep Learning Foundations Nanodegree at Udacity | Speaker - GDG & Women Techmakers - Machine Learning #3 | 18.10.2017 | Link |
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.
Algorithm | Description | Implementation | Dataset | Creation Date | Last Update |
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Linear Regression | - | Python (raw) | Generated Numbers | 18.04.2017 | 15.09.2017 |
- | Python (sklearn) | Generated Numbers | 18.04.2017 | 15.09.2017 | |
- | Python (Tensorflow) | Generated Numbers | 23.09.2017 | 23.09.2017 | |
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 |
Approximating Polynomial of degree 2 | Python (sklearn) | Generated Numbers | 10.06.2017 | 15.09.2017 | |
Approximating Polynomial of degree 3 | Python (raw) | Generated Numbers | 10.06.2017 | 15.09.2017 | |
Approximating Polynomial of degree 3 | Python (sklearn) | Generated Numbers | 10.06.2017 | 15.09.2017 | |
Logistic Regression | Data Analysis, Kaggle Competition | Python (raw) | Titanic Disaster | 19.10.2017 | 24.10.2017 |
KNN | Manhattan, Euclidean Similarity | Python (raw) | iris | 21.07.2017 | 24.09.2017 |
Euclidean Similarity | Python (sklearn) | iris | 22.07.2017 | 24.09.2017 | |
PCA | - | Python (raw) | Generated Numbers | 01.04.2017 | 23.09.2017 |
K-Means Clusters | 3-dimensional data | Python (raw) | Video Game Sales from Kaggle | 01.10.2017 | 05.10.2017 |
Naive Bayes | Gaussian Distribution | Python (raw) | Pima Indian Diabetes | 02.11.2017 | 03.11.2017 |
Lasso Regression | - | - | - | - | - |
SVM | - | - | - | - | - |
Decision Tree | - | - | - | - | - |
Random Forest | - | - | - | - | - |
Problem | Description | Implementation | Dataset | Creation Date | Last Update |
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Digit Classification | 2-layers, mini-batch | Python (raw) | MNIST | 19.06.2017 | 14.08.2017 |
Digit Classification | 2-layers, mini-batch, dropout-regularization | Python (Tensorflow) | MNIST | 29.06.2017 | 18.07.2017 |
Digit Classification | 2-layers, mini-batch | Python (Tensorflow + Keras) | MNIST | 08.07.2017 | 18.07.2017 |
Digit Classification | 2-layers, mini-batch | Python (tflearn) | MNIST | 21.06.2017 | 21.06.2017 |
Digit Classification | 2-layers, mini-batch | Python (Keras) | MNIST | 18.07.2017 | 18.07.2017 |
Prediction of Bike Shop Clients Number | 1-layer, mini-batch | Python (numpy, matplotlib) | Bike-Sharing | 13.08.2017 | 13.08.2017 |
Encrypting data with Autoencoder | 1-layer Encoder, 1-layer Decoder, mini-batch | Python (Tensorflow) | MNIST | 13.07.2017 | 13.07.2017 |
Detecting Text Sentiment | - | - | IMDb | - | - |
Problem | Description | Implementation | Dataset | Creation Date | Last Update |
---|---|---|---|---|---|
Digit Classification | tf.layer module, dropout regularization, batch normalization | Python (Tensorflow) | MNIST | 16.08.2017 | 23.08.2017 |
10 Classes Color Images Classification | tf.nn module, dropout regularization | Python (Tensorflow) | CIFAR-10 | 16.08.2017 | 07.09.2017 |
10 Classes Color Images Classification | tf.layer module, dropout regularization | Python (Tensorflow) | CIFAR-10 | 16.08.2017 | 09.09.2017 |
10 Classes Color Images Classification | tf.layer module, dropout regularization, batch normalization | Python (Tensorflow) | CIFAR-10 | 19.08.2017 | 10.09.2017 |
Problem | Description | Implementation | Dataset | Creation Date | Last Update |
---|---|---|---|---|---|
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 |
"The Simpsons" Script Generation | In form of my DLFND project for now | Python (Tensorflow) | "The Simpsons" script | 06.06.2017 | 14.07.2017 |
Problem | Description | Implementation | Dataset | Creation Date | Last Update |
---|---|---|---|---|---|
Generating Human Face Miniatures | DCGAN | Python (Tensorflow) | CelebA | 11.09.2017 | 13.09.2017 |
Teaching others is best way of teaching yourself. I will try to create tutorials with various implementations of ML&DL models and more. Idea of my tutorials is to build models with small steps, with many comments, ideally including math and links to sources that I use to create them.
Tutorial | Creation Date | Last Update |
---|---|---|
Implementing KNN with comments and basic math | 21.07.2017 | 21.07.2017 |
Implementing PCA with comments and basic math | 01.04.2017 | 01.04.2017 |
In this section I will do my best to provide implementations of models based on research papers. My target framework will be Keras or/and PyTorch.
Paper | Year | Implementation | Dataset | Creation Date | Last Update |
---|---|---|---|---|---|
Deep Photo Style Transfer | 2017 | - | - | - | - |
Spatial Transformer Networks - STN | 2016 | - | - | - | - |
You Only Look Once: Unified, Real-Time Object Detection - YOLO | 2016 | - | - | - | - |
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artwork | 2016 | - | - | - | - |
Colorful Image Colorization | 2016 | - | - | - | - |
Deep Residual Learning for Image Recognition - Microsoft-ResNet | 2015 | - | - | - | - |
Image Super-Resolution Using Deep Convolutional Networks | 2015 | - | - | - | - |
A Neural Algorithm of Artistic Style - GATYS | 2015 | - | - | - | - |
Paper | Year | Implementation | Dataset | Creation Date | Last Update |
---|---|---|---|---|---|
Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation | 2016 | - | - | - | - |
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin | 2015 | - | - | - | - |
A Neural Conversational Model | 2015 | - | - | - | - |
Skip-Thought Vectors | 2015 | - | - | - | - |
Sequence to Sequence Learning with Neural Networks | 2014 | - | - | - | - |
Generating Sequences With Recurrent Neural Networks | 2013 | - | - | - | - |
Paper | Year | Implementation | Dataset | Creation Date | Last Update |
---|---|---|---|---|---|
Generative Adversarial Text to Image Synthesis | 2016 | - | - | - | - |
Deep Convolutional GAN: DCGAN | 2015 | - | - | - | - |
Usually I prefer online sources for studying, but I believe in the power of books and try to fit them into my daily agenda.
Book | Author | Started | Finished |
---|---|---|---|
Dive Into Python 3 | Mark Pilgrim | Aug 2017 | Sep 2017 |
Automate the Boring Stuff with Python | Al Sweigart | Sept 2017 | Oct 2017 |
Book | Author | Started | Finished |
---|---|---|---|
Python Data Science Handbook | Jake VanderPlas | Nov 2017 | - |
Book | Author | Started | Finished |
---|---|---|---|
Grokking Deep Learning | Andrew Trask | Nov 2017 | - |
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?
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Programming languages:
- Programming for Everybody (Getting Started with Python) (Feb 2017) (Coursera - University of Michigan - Charles Severance)
- Python Data Structures (Feb 2017) (Coursera - University of Michigan - Charles Severance)
- Using Python to Access Web Data (Feb 2017) (Coursera - University of Michigan - Charles Severance)
- Using Databases with Python (Feb 2017) (Coursera - University of Michigan - Charles Severance)
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Algorithms:
- Divide and Conquer, Sorting and Searching, and Randomized Algorithms (Sep 2017) (Coursera - University of Stanford)
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AI related:
- Machine Learning (Nov 2016 - Feb 2017) (Coursera - Stanford - Andrew Ng)
- Deep Learning Nanodegree (Mar 2017 - Aug 2017) (Udacity - Siraj Raval, Mat Leonard, Brok Bucholtz + guest lessions by: Ian Goodfellow, Andrew Trask)
- Neural Networks and Deep Learning (Oct 2017) (Coursera - deeplearning.ai - Andrew Ng)
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.
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Courses
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Online Lectures and YouTube Channels
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Blogs
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Podcasts
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Cheatsheets
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Repositories
- https://github.com/terryum/awesome-deep-learning-papers
- https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap
- https://github.com/fchollet/deep-learning-with-python-notebooks
- https://github.com/ZuzooVn/machine-learning-for-software-engineers
- https://github.com/eriklindernoren/ML-From-Scratch
- https://github.com/junyanz/CycleGAN
- https://github.com/llSourcell
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Other
- https://www.kaggle.com/
- https://www.tensorflow.org/
- https://www.quora.com/How-can-beginners-in-machine-learning-who-have-finished-their-MOOCs-in-machine-learning-and-deep-learning-take-it-to-the-next-level-and-get-to-the-point-of-being-able-to-read-research-papers-productively-contribute-in-an-industry/answer/Andrew-Ng?share=c26bd326
- Twitter: @F1sherKK
- Medium: @krzyk.kamil
- E-mail: [email protected]
- Android related open-source contributions: AzimoLabs