Neural Networks and Deep Learning

Neural Networks and Deep Learning

This course is part of Deep Learning Specialization

Instructors: Andrew Ng +2 more

Top Instructor

1,524,349 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

123,751 reviews

Intermediate level

Recommended experience

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

96%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

123,751 reviews

Intermediate level

Recommended experience

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

96%

Most learners liked this course

Skills you'll gain

Category: Artificial Intelligence
Category: Artificial Neural Networks
Category: Model Training
Category: Model Optimization
Category: Supervised Learning
Category: Convolutional Neural Networks
Category: Recurrent Neural Networks (RNNs)
Category: Machine Learning Methods
Category: Deep Learning
Category: Applied Machine Learning

Tools you'll learn

Category: Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Deep Learning Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.

By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Analyze the major trends driving the rise of deep learning, and give examples of where and how it is applied today.

What's included

6 videos3 readings1 assignment1 app item

6 videosTotal 74 minutes

Welcome6 minutes
What is a Neural Network?7 minutes
Supervised Learning with Neural Networks8 minutes
Why is Deep Learning taking off?10 minutes
About this Course2 minutes
Geoffrey Hinton Interview40 minutes

3 readingsTotal 13 minutes

Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!2 minutes
Frequently Asked Questions10 minutes
Lecture Notes W11 minute

1 assignmentTotal 50 minutes

Introduction to Deep Learning 50 minutes

1 app itemTotal 1 minute

Intake Survey1 minute

Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models.

What's included

19 videos5 readings1 assignment2 programming assignments

19 videosTotal 161 minutes

Binary Classification8 minutes
Logistic Regression6 minutes
Logistic Regression Cost Function8 minutes
Gradient Descent11 minutes
Derivatives7 minutes
More Derivative Examples10 minutes
Computation Graph4 minutes
Derivatives with a Computation Graph15 minutes
Logistic Regression Gradient Descent7 minutes
Gradient Descent on m Examples8 minutes
Vectorization8 minutes
More Vectorization Examples6 minutes
Vectorizing Logistic Regression8 minutes
Vectorizing Logistic Regression's Gradient Output10 minutes
Broadcasting in Python11 minutes
A Note on Python/Numpy Vectors7 minutes
Quick tour of Jupyter/iPython Notebooks4 minutes
Explanation of Logistic Regression Cost Function (Optional)7 minutes
Pieter Abbeel Interview16 minutes

5 readingsTotal 28 minutes

Derivation of DL/dz (Optional)10 minutes
Lecture Notes W21 minute
Deep Learning Honor Code2 minutes
Programming Assignment FAQ10 minutes
(Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace5 minutes

1 assignmentTotal 50 minutes

Neural Network Basics 50 minutes

2 programming assignmentsTotal 240 minutes

Python Basics with Numpy60 minutes
Logistic Regression with a Neural Network Mindset180 minutes

Build a neural network with one hidden layer, using forward propagation and backpropagation.

What's included

12 videos1 reading1 assignment1 programming assignment

12 videosTotal 109 minutes

Neural Networks Overview4 minutes
Neural Network Representation5 minutes
Computing a Neural Network's Output10 minutes
Vectorizing Across Multiple Examples9 minutes
Explanation for Vectorized Implementation8 minutes
Activation Functions11 minutes
Why do you need Non-Linear Activation Functions?6 minutes
Derivatives of Activation Functions8 minutes
Gradient Descent for Neural Networks10 minutes
Backpropagation Intuition (Optional)16 minutes
Random Initialization8 minutes
Ian Goodfellow Interview15 minutes

1 readingTotal 1 minute

Lecture Notes W31 minute

1 assignmentTotal 50 minutes

Shallow Neural Networks 50 minutes

1 programming assignmentTotal 180 minutes

Planar Data Classification with One Hidden Layer180 minutes

Analyze the key computations underlying deep learning, then use them to build and train deep neural networks for computer vision tasks.

What's included

8 videos7 readings1 assignment2 programming assignments

8 videosTotal 64 minutes

Deep L-layer Neural Network6 minutes
Forward Propagation in a Deep Network7 minutes
Getting your Matrix Dimensions Right11 minutes
Why Deep Representations?11 minutes
Building Blocks of Deep Neural Networks9 minutes
Forward and Backward Propagation10 minutes
Parameters vs Hyperparameters7 minutes
What does this have to do with the brain?3 minutes

7 readingsTotal 44 minutes

Optional Reading: Feedforward Neural Networks in Depth10 minutes
Clarification For: What does this have to do with the brain?1 minute
Lecture Notes W41 minute
[IMPORTANT] Reminder about end of access to Lab Notebooks2 minutes
Confusing Output from the AutoGrader10 minutes
References 10 minutes
Acknowledgments10 minutes

1 assignmentTotal 50 minutes

Key Concepts on Deep Neural Networks 50 minutes

2 programming assignmentsTotal 360 minutes

Building your Deep Neural Network: Step by Step180 minutes
Deep Neural Network - Application180 minutes

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Instructors

Instructor ratings

(23,430 ratings)

Top Instructor

Andrew Ng

DeepLearning.AI

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Reviewed on Jul 24, 2021

The notation and the description of the course materials are way more comprehensible than that of the ML course. I deeply appreciate all the efforts made so that this course could be presented to us.

Reviewed on Aug 26, 2017

This is a very good course for people who want to get started with neural networks. Andrew did a great job explaining the math behind the scenes. Assignments are well-designed too. Highly recommended.

Reviewed on Aug 29, 2018

Nothing can get better than this course from Professor Andrew Ng. A must for every Data science enthusiast. Gets you up to speed right from the fundamentals. Thanks a lot for Prof Andrew and his team.

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.