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
- Mini AI Projects
- AI Programming Showcase
- Articles
- Courses & Certificates
- Sources
- Contact
| 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 |
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 | 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 |
| 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 |
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.
| 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 |
| 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 |
| 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 |
| Algorithm | Description | Implementation | Dataset | Creation Date | Last Update |
|---|---|---|---|---|---|
| Linear Regression | - | Python (Tensorflow) | Generated Numbers | 23.09.2017 | 23.09.2017 |
| 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 |
| 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 |
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 |
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)
- Practical Machine Learning (Nov - Dec 2017) (DataWorkshop - Vladimir Alekseichenko)
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Jan 2018) (Coursera - deeplearning.ai - Andrew Ng)
- Structuring Machine Learning Projects (Mar 2018) (Coursera - deeplearning.ai - Andrew Ng)
- Segmentation and Clustering [Free] (May 2018) (Udacity)
- Data Science Nanodegree Term 1 (Jun 2018 - Currently in progress) (Udacity)
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.
- Twitter: @F1sherKK
- Medium: @krzyk.kamil
- E-mail: [email protected]
- Android related open-source contributions: AzimoLabs