Deep Learning Specialization
Master Deep Learning, and Break into AI
About This Specialization
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.
Created by:
Industry Partners:
5 courses
Follow the suggested order or choose your own.
Projects
Designed to help you practice and apply the skills you learn.
Certificates
Highlight your new skills on your resume or LinkedIn.
Projects Overview
Courses
- Intermediate Specialization.
- Some related experience required.
COURSE 1
Neural Networks and Deep Learning
Upcoming session: Nov 7- Commitment
- 4 weeks of study, 3-6 hours a week
- Subtitles
- English
About the Course
If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" tYou can choose to take this course only. Learn more.
COURSE 2
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Upcoming session: Nov 7- Commitment
- 3 weeks, 3-6 hours per week
- Subtitles
- English
About the Course
This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will alYou can choose to take this course only. Learn more.
COURSE 3
Structuring Machine Learning Projects
Upcoming session: Nov 7- Commitment
- 2 weeks of study, 3-4 hours/week
- Subtitles
- English
About the Course
You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, andYou can choose to take this course only. Learn more.
COURSE 4
Convolutional Neural Networks
Starts late October 2017- Subtitles
- English
About the Course
This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization.You can choose to take this course only. Learn more.
COURSE 5
Sequence Models
Starts November 2017- Subtitles
- English
About the Course
This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization.You can choose to take this course only. Learn more.
Creators
Andrew Ng
Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Teaching Assistant - Younes Bensouda Mourri
Mathematical & Computational Sciences, Stanford University
Teaching Assistant - Kian Katanforoosh
M.S. Stanford University (Walter J. Gores 2017), B.S Ecole Centrale Paris
FAQs
More questions? Visit the Learner Help Center.
