关于此课程： 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.
此课程适用人群： 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.
教学方： Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
教学方： Head Teaching Assistant - Kian Katanforoosh, Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
教学方： Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| 基本信息 | 课程 1（共 5 门，Deep Learning Specialization ） |
| 级别 | 中级 |
| 承诺学习时间 | 4 weeks of study, 3-6 hours a week |
| 语言 | English, 字幕： Chinese (Traditional), Chinese (Simplified), Portuguese (Brazilian), Korean, Turkish, Japanese |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
- 视频: What is a neural network?
- 视频: Supervised Learning with Neural Networks
- 视频: Why is Deep Learning taking off?
- 视频: About this Course
- Материал для самостоятельного изучения: Frequently Asked Questions
- 视频: Course Resources
- Материал для самостоятельного изучения: How to use Discussion Forums
- 视频: Geoffrey Hinton interview
- 视频: Logistic Regression
- 视频: Logistic Regression Cost Function
- 视频: Gradient Descent
- 视频: Derivatives
- 视频: More Derivative Examples
- 视频: Computation graph
- 视频: Derivatives with a Computation Graph
- 视频: Logistic Regression Gradient Descent
- 视频: Gradient Descent on m Examples
- 视频: Vectorization
- 视频: More Vectorization Examples
- 视频: Vectorizing Logistic Regression
- 视频: Vectorizing Logistic Regression's Gradient Output
- 视频: Broadcasting in Python
- 视频: A note on python/numpy vectors
- 视频: Quick tour of Jupyter/iPython Notebooks
- 视频: Explanation of logistic regression cost function (optional)
- Материал для самостоятельного изучения: Deep Learning Honor Code
- Материал для самостоятельного изучения: Programming Assignment FAQ
- Блокнот: Python Basics with numpy (optional)
- Неоцениваемое задание по программированию: Python Basics with numpy (optional)
- Блокнот: Logistic Regression with a Neural Network mindset
- 视频: Pieter Abbeel interview
- 视频: Neural Network Representation
- 视频: Computing a Neural Network's Output
- 视频: Vectorizing across multiple examples
- 视频: Explanation for Vectorized Implementation
- 视频: Activation functions
- 视频: Why do you need non-linear activation functions?
- 视频: Derivatives of activation functions
- 视频: Gradient descent for Neural Networks
- 视频: Backpropagation intuition (optional)
- 视频: Random Initialization
- Блокнот: Planar data classification with a hidden layer
- 视频: Ian Goodfellow interview
- 视频: Forward Propagation in a Deep Network
- 视频: Getting your matrix dimensions right
- 视频: Why deep representations?
- 视频: Building blocks of deep neural networks
- 视频: Forward and Backward Propagation
- 视频: Parameters vs Hyperparameters
- 视频: What does this have to do with the brain?
- Блокнот: Building your Deep Neural Network: Step by Step
- Блокнот: Deep Neural Network - Application
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Very awesome and informative.
This course is really helpful and easy to follow
NN&DL is much shorter and much easier than Dr. Ng's original ML course, and the material overlaps a lot. I finished the course in 2 days (not 4 weeks). As always, Dr. Ng's explanations are clear, and the material is beautifully organized. I felt the answers to the assignments were a little too easy. Not really a lot of thinking required. The interviews with the "heroes" of Deep Learning were fantastic. Their ideas about the future were very inspirational. I am looking forward to the next course in the specialization.
k
very good,