This repository contains 3 folder each of which is seperate project. These projects all are related with deep learning. Projects were taken from Kagle and also extra problems were solved in addition to the problems specified in Kagle. Deep learning is widely used machine learning methods which use different kind of neural network algorithms. Each of these algorithms addresses different kind of problems such as time series problems, image processing, image generation, image and voice classification etc. Deep learning methods that we use to solve these kind of problems are: dense network, convolutional neural network (CNN), recurrent neural network (RNN), long-short-term memory (LSTM), gated recurrent units (GRU), directed acyclic graph (DAG), neural style transfer, variational autoencoders. I finished three projects addressing different problems:
- Classification of the leaves;
- Seatle weather prediction;
- Classification of the voices of the cats and dogs.
In each project, the main objective is to find the optimum deep learning algorithm and its architecture that will prevent overfitting, increasing accuracy of the model, and will be computationally more efficeint. Even though this kind of investigation looks like a pure try-error procedure like sensitivity analysis, I used mathematical background of these algorithms to perform optimization procedure more efficiently. For example, understanding back-propagation process and stochastic gradient descent method give me idea how depth of the layers and number of layers can lead overfirtting. I also investigated which features of the data plays more crucial role in classification of data.
- Python
- Excel
- LaTex
Keras library was used for different type of deep learning algorithms in python. In addition to this library, tensor flow, matplotlib, pandas, os, and other data preperation and visualization libraries were used.