Neural Networks and Deep Learning
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Neural Networks and Deep Learning
This course is part of Deep Learning Specialization
Instructors: Andrew Ng
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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
- Logistic Regression with a Neural Network Mindset•180 minutes
- Python Basics with Numpy•60 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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Reviewed on Dec 18, 2018
The best and simplest neural network course i have come across. Andrew Ng makes the mathematical concepts subtle and understandle. Neural network for me is no longer a black box.Thank you Andrew Ng
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.
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.
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