About this Course
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Intermediate Level

Approx. 18 hours to complete

English

Subtitles: Chinese (Traditional), Arabic, French, Ukrainian, Chinese (Simplified), Portuguese (Brazilian), Korean, Turkish, English, Spanish, Japanese...

Skills you will gain

Artificial Neural NetworkBackpropagationPython ProgrammingDeep Learning
Learners taking this Course are
  • Data Scientists
  • Machine Learning Engineers
  • Biostatisticians
  • Researchers
  • Data Engineers

Course 1 of 3 in the

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 18 hours to complete

English

Subtitles: Chinese (Traditional), Arabic, French, Ukrainian, Chinese (Simplified), Portuguese (Brazilian), Korean, Turkish, English, Spanish, Japanese...

Syllabus - What you will learn from this course

Week
1
2 hours to complete

Introduction to deep learning

7 videos (Total 76 min), 2 readings, 1 quiz
7 videos
What is a neural network?7m
Supervised Learning with Neural Networks8m
Why is Deep Learning taking off?10m
About this Course2m
Course Resources1m
Geoffrey Hinton interview40m
2 readings
Frequently Asked Questions10m
How to use Discussion Forums10m
1 practice exercise
Introduction to deep learning20m
Week
2
8 hours to complete

Neural Networks Basics

19 videos (Total 161 min), 7 readings, 3 quizzes
19 videos
Logistic Regression5m
Logistic Regression Cost Function8m
Gradient Descent11m
Derivatives7m
More Derivative Examples10m
Computation graph3m
Derivatives with a Computation Graph14m
Logistic Regression Gradient Descent6m
Gradient Descent on m Examples8m
Vectorization8m
More Vectorization Examples6m
Vectorizing Logistic Regression7m
Vectorizing Logistic Regression's Gradient Output9m
Broadcasting in Python11m
A note on python/numpy vectors6m
Quick tour of Jupyter/iPython Notebooks3m
Explanation of logistic regression cost function (optional)7m
Pieter Abbeel interview16m
7 readings
Clarification about Upcoming Logistic Regression Cost Function Video1m
Clarification about Upcoming Gradient Descent Video1m
Derivation of DL/dz part 110m
Derivation of DL/dz part 210m
Clarification of "dz"10m
Deep Learning Honor Code2m
Programming Assignment FAQ10m
1 practice exercise
Neural Network Basics20m
Week
3
5 hours to complete

Shallow neural networks

12 videos (Total 109 min), 2 readings, 2 quizzes
12 videos
Neural Network Representation5m
Computing a Neural Network's Output9m
Vectorizing across multiple examples9m
Explanation for Vectorized Implementation7m
Activation functions10m
Why do you need non-linear activation functions?5m
Derivatives of activation functions7m
Gradient descent for Neural Networks9m
Backpropagation intuition (optional)15m
Random Initialization7m
Ian Goodfellow interview14m
2 readings
Clarification: Activation Function1m
Clarification about Upcoming Backpropagation intuition (optional)1m
1 practice exercise
Shallow Neural Networks20m
Week
4
5 hours to complete

Deep Neural Networks

8 videos (Total 64 min), 3 readings, 3 quizzes
8 videos
Forward Propagation in a Deep Network7m
Getting your matrix dimensions right11m
Why deep representations?10m
Building blocks of deep neural networks8m
Forward and Backward Propagation10m
Parameters vs Hyperparameters7m
What does this have to do with the brain?3m
3 readings
Clarification about Getting your matrix dimensions right video1m
Clarification about Upcoming Forward and Backward Propagation Video1m
Clarification about What does this have to do with the brain video1m
1 practice exercise
Key concepts on Deep Neural Networks20m
4.9
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Top reviews from Neural Networks and Deep Learning

By AASep 2nd 2019

I highly appreciated the interviews at the end of some weeks. I am currently trying to transition from a research background in Systems/Computational Biology to work professionally in deep learning :)

By JPFeb 12th 2018

I would love some pointers to additional references for each video. Also, the instructor keeps saying that the math behind backprop is hard. What about an optional video with that? Otherwise, awesome!

Instructors

Avatar

Andrew Ng

CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
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Head Teaching Assistant - Kian Katanforoosh

Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
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Teaching Assistant - Younes Bensouda Mourri

Mathematical & Computational Sciences, Stanford University, deeplearning.ai
Computer Science

About deeplearning.ai

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

About the Deep Learning 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....
Deep Learning

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.