Assignments and projects for all five courses of the Deep Learning Specialization from deeplearning.ai's on Coursera.
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.
We will help you master Deep Learning, understand how to apply it, and build a career in AI.
cd /path/to/where/you/want/it
git clone https://github.com/chivingtoninc/Coursera-Deep-Learning.git
This repo should be used as a reference while taking the Deep Learning Specialization on Couresera.
Familiarity with general Python is assumed. Numpy is covered but if you'd like more a in-depth refresher, here is a great Python Numpy Tutorial by Justin Johnson.
Note: the programming assignments for these courses will not run locally. I have not included the datasets, due to their sizes. This repo is solely for reference purposes only.
Feel free to ask me questions on GitHub, Twitter, Facebook or LinkedIn
Not currently accepting outside contributors, but feel free to clone, fork, modify and use as you wish.
- Thank you to Andrew Ng, Kian Katanforoosh, Geoffrey Hinton, Pieter Abbeel, Ian Goodfellow, and all others who assisted in providing this quality, inexpensive educational content!
This project is licensed under the DO_WHATEVER_YOU_WANT License - see the LICENSE file for details