Neural Networks and Deep Learning

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Neural Networks and Deep Learning

Name: Neural Networks and Deep Learning
Rating: 4.881408831068024 (123677 reviews)

This course is part of Deep Learning Specialization

Instructors: Andrew Ng

Top Instructor

1,509,893 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

123,677 reviews

Intermediate level

Recommended experience

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

123,677 reviews

Intermediate level

Recommended experience

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

Skills you'll gain

Tools you'll learn

Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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This course is part of the Deep Learning Specialization

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

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Gain a foundational understanding of a subject or tool
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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 videos Total 74 minutes

Welcome 6 minutes
What is a Neural Network? 7 minutes
Supervised Learning with Neural Networks 8 minutes
Why is Deep Learning taking off? 10 minutes
About this Course 2 minutes
Geoffrey Hinton Interview 40 minutes

3 readings Total 13 minutes

Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas! 2 minutes
Frequently Asked Questions 10 minutes
Lecture Notes W1 1 minute

1 assignment Total 50 minutes

Introduction to Deep Learning 50 minutes

1 app item Total 1 minute

Intake Survey 1 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 videos Total 161 minutes

Binary Classification 8 minutes
Logistic Regression 6 minutes
Logistic Regression Cost Function 8 minutes
Gradient Descent 11 minutes
Derivatives 7 minutes
More Derivative Examples 10 minutes
Computation Graph 4 minutes
Derivatives with a Computation Graph 15 minutes
Logistic Regression Gradient Descent 7 minutes
Gradient Descent on m Examples 8 minutes
Vectorization 8 minutes
More Vectorization Examples 6 minutes
Vectorizing Logistic Regression 8 minutes
Vectorizing Logistic Regression's Gradient Output 10 minutes
Broadcasting in Python 11 minutes
A Note on Python/Numpy Vectors 7 minutes
Quick tour of Jupyter/iPython Notebooks 4 minutes
Explanation of Logistic Regression Cost Function (Optional) 7 minutes
Pieter Abbeel Interview 16 minutes

5 readings Total 28 minutes

Derivation of DL/dz (Optional) 10 minutes
Lecture Notes W2 1 minute
Deep Learning Honor Code 2 minutes
Programming Assignment FAQ 10 minutes
(Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace 5 minutes

1 assignment Total 50 minutes

Neural Network Basics 50 minutes

2 programming assignments Total 240 minutes

Python Basics with Numpy 60 minutes
Logistic Regression with a Neural Network Mindset 180 minutes

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

What's included

12 videos1 reading1 assignment1 programming assignment

12 videos Total 109 minutes

Neural Networks Overview 4 minutes
Neural Network Representation 5 minutes
Computing a Neural Network's Output 10 minutes
Vectorizing Across Multiple Examples 9 minutes
Explanation for Vectorized Implementation 8 minutes
Activation Functions 11 minutes
Why do you need Non-Linear Activation Functions? 6 minutes
Derivatives of Activation Functions 8 minutes
Gradient Descent for Neural Networks 10 minutes
Backpropagation Intuition (Optional) 16 minutes
Random Initialization 8 minutes
Ian Goodfellow Interview 15 minutes

1 reading Total 1 minute

Lecture Notes W3 1 minute

1 assignment Total 50 minutes

Shallow Neural Networks 50 minutes

1 programming assignment Total 180 minutes

Planar Data Classification with One Hidden Layer 180 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 videos Total 64 minutes

Deep L-layer Neural Network 6 minutes
Forward Propagation in a Deep Network 7 minutes
Getting your Matrix Dimensions Right 11 minutes
Why Deep Representations? 11 minutes
Building Blocks of Deep Neural Networks 9 minutes
Forward and Backward Propagation 10 minutes
Parameters vs Hyperparameters 7 minutes
What does this have to do with the brain? 3 minutes

7 readings Total 44 minutes

Optional Reading: Feedforward Neural Networks in Depth 10 minutes
Clarification For: What does this have to do with the brain? 1 minute
Lecture Notes W4 1 minute
[IMPORTANT] Reminder about end of access to Lab Notebooks 2 minutes
Confusing Output from the AutoGrader 10 minutes
References 10 minutes
Acknowledgments 10 minutes

1 assignment Total 50 minutes

Key Concepts on Deep Neural Networks 50 minutes

2 programming assignments Total 360 minutes

Building your Deep Neural Network: Step by Step 180 minutes
Deep Neural Network - Application 180 minutes

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Instructors

Instructor ratings

(23,421 ratings)

Top Instructor

Andrew Ng

DeepLearning.AI

51 Courses 9,589,674 learners

Offered by

DeepLearning.AI

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Showing 3 of 123677

Reviewed on Aug 20, 2022

Although problems sets are too easy and over simplified, the course has good content, I learned a lot and I have a better intuition now on how NNs work.

Reviewed on Jul 28, 2020

Pretty well organized with really helpful examples and assignment especially. Definitely made the basis for deep learning algorithms. Looking forward to the next modules and dive deep in this domain.

Reviewed on Feb 7, 2023

An amazing course and gives quite a detailed and beginner-friendly description of deep learning and neural networks. This course helped me immensely in overcoming my intimidation towards these topics.

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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.