Counting 3,146 Big Data & Machine Learning Frameworks, Toolsets, and Examples...
Suggestion? Feedback? Tweet @stkim1

Last Commit
Sep. 8, 2018
Aug. 7, 2018

Deep Learning Specialization

Projects from the Deep Learning Specialization from offered by Coursera.

Instructor: Andrew Ng

Master Deep Learning and Break Into AI

If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.

You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.

AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.

Programming Assignements

  • Course 1: Neural Networks and Deep Learning

    • Week 2 - PA 1 - Logistic Regresssion as a Neural Network
    • Week 2 - PA 2 - Python Basics with Numpy
    • Week 3 - PA 1 - Planar data classification with one hidden layer
    • Week 4 - PA 1 - Building your Deep Neural Network Step by Step
    • Week 4 - PA 2 - Deep Neural Network Application_Image Classification
  • Course 2: Improving Neural Networks

    • Week 1 - PA 1 - Gradient Checcking
    • Week 1 - PA 2 - Regularisation
    • Week 1 - PA 3 - Initialization
    • Week 2 - PA 1 - Optimization Methods
    • Week 3 - PA 1 - Tensorflow Tutorial
  • Course 4: Convolutional Neural Networks

    • Week 1 - PA 1 - Convolutional Model
    • Week 2 - PA 1 - Keras Tutorial
    • Week 2 - PA 2 - ResNets
    • Week 3 - PA 1 - Car Detection for Autonomous Driving
    • Week 4 - PA 1 - Face Recognition
    • Week 4 - PA 2 - Neural Style Transfer
  • Course 5: Sequence Models

    • Week 1 - PA 1 - Building a RNN step by step
    • Week 1 - PA 2 - Dinosaur Island
    • Week 1 - PA 3 - Jazz Imrpovisation with LSTM
    • Week 2 - PA 1 - Emojify
    • Week 2 - PA 2 - Word Vector Representation
    • Week 3 - PA 1 - Machine Translation
    • Week 3 - PA 2 - Trigger Word Detection


The Code is written in Python 3.6.5 . If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip.

To install pip run in the command Line

python -m ensurepip -- default-pip

to upgrade it

python -m pip install -- upgrade pip setuptools wheel

to upgrade Python

pip install python -- upgrade

You will also need to install additional packages depending the Course you are following and the relevant assignement. Seperate ReadMes will guide you for each individual course

Viewing the Jupyter Notebook

In order to better view and work on the jupyter Notebook I encourage you to use nbviewer . You can simply copy and paste the link to this website and you will be able to edit it without any problem. Alternatively you can clone the repository using

git clone

then in the command Line type, after you have downloaded jupyter notebook type

jyputer notebook

locate the notebook and run it.


Please use this repository ONLY as reference or for help and do not hard copy and paste the assignements.


Please read for the process for submitting pull requests.


  • Fotios Kapotos - Initial work

This project is licensed under the MIT License - see the file for details