ramapriyakp / f1sherkk-myroadtoai

Repository for storing and tracking my self-study progress.

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

• Kamil Krzyk - My Road to AI
• About
• Table of Contents
• AI Related Presentations
• Mini AI Projects
  • Problem Solving
  • Visualisation
• AI Programming Showcase
  • Raw Python
  • sklearn
  • TensorFlow
  • Keras
• Articles
• Courses & Certificates
• Sources
• Contact

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?

• 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)

• Algorithms:

  • Divide and Conquer, Sorting and Searching, and Randomized Algorithms (Sep 2017) (Coursera - University of Stanford)

• 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)

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

• Twitter: @F1sherKK
• Medium: @krzyk.kamil
• E-mail: [email protected]
• Android related open-source contributions: AzimoLabs
• LinkedIn