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Deep Learning Nanodegree


I enrolled to Deep Learning Nanodegree in 2020. Because I did not want to pay I only had one month to finish, so it was quite a challenge. Although I am quite familiar with the course content finishing all the projects in one month required a big effort.

This is a nanodegree includes the three aspects of Machine Learning; supervised learning, deep learning and unsupervised learning.

This Nanodegree has five parts and three projects.

Part 1 - Introduction to Deep Learning

Orientation part, to explain how the Nanodegree works.

Part 2 - Neural Networks

In this part, supervised learning is discussed which is the most commonly used class of methods for model construction.

Project: Finding Donors Project
Part 3 - Convolutional Neural Networks

In this part, foundations of neural network design and training in TensorFlow is discussed with an Image classifier project.

Project: Dog Breed Classifier
Part 4 - Recurrent Neural Networks

In this part, unsupervised learning methods is used to be used for unlabelled data originating from different kinds of problem domains.

Project: Generate TV Scripts
Part 5 - Generative Adversarial Networks


Part 6 - Deploying a Model
Project: Deploying a Sentiment Analysis Model

End of the nanodegree with some extra stuff on Python.