About this 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" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization.
Who is this class for: Prerequisites: Expected: - Programming: Basic Python programming skills, with the capability to work effectively with data structures. Recommended: - Mathematics: Matrix vector operations and notation. - Machine Learning: Understanding how to frame a machine learning problem, including how data is represented will be beneficial. If you have taken my Machine Learning Course here, you have much more than the needed level of knowledge.
Taught by: Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
| Basic Info | Course 1 of 5 in the Deep Learning Specialization |
| Level | Intermediate |
| Commitment | 4 weeks of study, 3-6 hours a week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Video: Welcome
- Video: What is a neural network?
- Video: Supervised Learning with Neural Networks
- Video: Why is Deep Learning taking off?
- Video: About this Course
- Reading: Frequently Asked Questions
- Video: Course Resources
- Reading: How to use Discussion Forums
- Video: Geoffrey Hinton interview
- Video: Binary Classification
- Video: Logistic Regression
- Video: Logistic Regression Cost Function
- Video: Gradient Descent
- Video: Derivatives
- Video: More Derivative Examples
- Video: Computation graph
- Video: Derivatives with a Computation Graph
- Video: Logistic Regression Gradient Descent
- Video: Gradient Descent on m Examples
- Video: Vectorization
- Video: More Vectorization Examples
- Video: Vectorizing Logistic Regression
- Video: Vectorizing Logistic Regression's Gradient Output
- Video: Broadcasting in Python
- Video: A note on python/numpy vectors
- Video: Quick tour of Jupyter/iPython Notebooks
- Video: Explanation of logistic regression cost function (optional)
- Reading: Programming Assignment FAQ
- Notebook: Python Basics with numpy (optional)
- Ungraded Programming: Python Basics with numpy (optional)
- Notebook: Logistic Regression with a Neural Network mindset
- Video: Pieter Abbeel interview
- Video: Neural Networks Overview
- Video: Neural Network Representation
- Video: Computing a Neural Network's Output
- Video: Vectorizing across multiple examples
- Video: Explanation for Vectorized Implementation
- Video: Activation functions
- Video: Why do you need non-linear activation functions?
- Video: Derivatives of activation functions
- Video: Gradient descent for Neural Networks
- Video: Backpropagation intuition (optional)
- Video: Random Initialization
- Notebook: Planar data classification with a hidden layer
- Video: Ian Goodfellow interview
- Video: Deep L-layer neural network
- Video: Forward and Backward Propagation
- Video: Forward Propagation in a Deep Network
- Video: Getting your matrix dimensions right
- Video: Why deep representations?
- Video: Building blocks of deep neural networks
- Video: Parameters vs Hyperparameters
- Video: What does this have to do with the brain?
- Notebook: Building your Deep Neural Network: Step by Step
- Notebook: Deep Neural Network - Application
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
This is one of best course i have ever taken. Now i have clear understanding of how Neural Network work and how to create own Neural Network from scratch.
it's good, but it is really slow to me for my poor english, however ,I 'll try my best to learn it.
Nice basic course, gives a clear look at what is happening inside neural networks, all details are explained in quite clear and understandable form with practical tasks of implementing everything, so that you really know what is going on.
After that course you will have a knowledge of how to implement a simple neural network and it's learning algorithm from zero. Also you will get some knowledge about matrices operations, derivatives and python programming.
I do highly recommend this course for novices and for more skilled people. It was a positive experience.
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GH
简短的课程使学习者理解神经网络和深度的基本知识，特别是各个大牛的采访， 很棒，很鼓舞后来者能更多的应用AI