このコースについて： 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.
受講対象者： 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.
講師： Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
講師： Head Teaching Assistant - Kian Katanforoosh, Adjunct Lecturer at Stanford University, deeplearning.ai, Ecole Centrale Paris
講師： Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| 基本情報 | Deep Learning Specialization における 5 の 1 コース |
| レベル | Intermediate |
| 所要時間 | 4 weeks of study, 3-6 hours a week |
| 言語 | English, 字幕： Chinese (Traditional), Chinese (Simplified), Portuguese (Brazilian), Japanese |
| 合格するには | すべての採点課題に合格すると、このコースを完了できます。 |
| ユーザーの評価 |
- 動画: What is a neural network?
- 動画: Supervised Learning with Neural Networks
- 動画: Why is Deep Learning taking off?
- 動画: About this Course
- Lecture: Frequently Asked Questions
- 動画: Course Resources
- Lecture: How to use Discussion Forums
- 動画: Geoffrey Hinton interview
- 動画: Logistic Regression
- 動画: Logistic Regression Cost Function
- 動画: Gradient Descent
- 動画: Derivatives
- 動画: More Derivative Examples
- 動画: Computation graph
- 動画: Derivatives with a Computation Graph
- 動画: Logistic Regression Gradient Descent
- 動画: Gradient Descent on m Examples
- 動画: Vectorization
- 動画: More Vectorization Examples
- 動画: Vectorizing Logistic Regression
- 動画: Vectorizing Logistic Regression's Gradient Output
- 動画: Broadcasting in Python
- 動画: A note on python/numpy vectors
- 動画: Quick tour of Jupyter/iPython Notebooks
- 動画: Explanation of logistic regression cost function (optional)
- Lecture: Deep Learning Honor Code
- Lecture: Programming Assignment FAQ
- Bloc-notes: Python Basics with numpy (optional)
- Programmation non notée: Python Basics with numpy (optional)
- Bloc-notes: Logistic Regression with a Neural Network mindset
- 動画: Pieter Abbeel interview
- 動画: Neural Network Representation
- 動画: Computing a Neural Network's Output
- 動画: Vectorizing across multiple examples
- 動画: Explanation for Vectorized Implementation
- 動画: Activation functions
- 動画: Why do you need non-linear activation functions?
- 動画: Derivatives of activation functions
- 動画: Gradient descent for Neural Networks
- 動画: Backpropagation intuition (optional)
- 動画: Random Initialization
- Bloc-notes: Planar data classification with a hidden layer
- 動画: Ian Goodfellow interview
- 動画: Forward Propagation in a Deep Network
- 動画: Getting your matrix dimensions right
- 動画: Why deep representations?
- 動画: Building blocks of deep neural networks
- 動画: Forward and Backward Propagation
- 動画: Parameters vs Hyperparameters
- 動画: What does this have to do with the brain?
- Bloc-notes: Building your Deep Neural Network: Step by Step
- Bloc-notes: Deep Neural Network - Application
Chaque cours fonctionne comme un manuel interactif en proposant des vidéos préenregistrées, des quiz et des projets.
Connectez-vous à des milliers d'autres étudiants et débattez sur des idées, discutez le contenu du cours et obtenez de l'aide pour en maîtriser les concepts.
Obtenez une reconnaissance officielle pour votre travail et partagez votre réussite avec vos amis, vos collègues et vos employeurs.
| コースを購入 | |
|---|---|
| コースの教材へのアクセス | 利用可能 |
| 採点教材へのアクセス | 利用可能 |
| 最終成績の受領 | 利用可能 |
| 共有できるコースの修了証を取得 | 利用可能 |
Great pace of teaching and excellent delivery of materials as expected from Prof. Andrew Ng. Thank you!
I just enjoyed listening these concepts .
Great Job Andrew Ng and Team .
I really loved the basics which are being implemented from the scratch. Excellent for someone who wants to master deep learning.
AS
This course covers some of the basic concepts of -deep- neural networks. For an introduction the course material is quite interesting, especially if one is willing to take other courses in the specialization. Optional videos about some of the "Heroes" of Deep Learning were also interesting.
The only part that I found a bit unsatisfying is some of the programming assignments. I found them too easy, to the point where I felt I wasn't programming, but more like "filling in the gaps". I understood the concepts but I think not implementing them from scratch is rather a drawback at this early stage of learning. I was a bit familiar with Python and Numpy, maybe that didn't serve me in this perspective xD