이 강좌에 대하여: This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. After 3 weeks, you will: - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization.
이 수업의 대상: This class is for: - Learners that took the first course of the specialization: "Neural Networks and Deep Learning" - Anyone that already understands fully-connected neural networks, and wants to learn the practical aspects of making them work well.
강사: Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
강사: Head Teaching Assistant - Kian Katanforoosh, Adjunct Lecturer at Stanford University, deeplearning.ai, Ecole Centrale Paris
강사: Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| 기본 정보 | Deep Learning Specialization 에서 5의 강좌 2 |
| 레벨 | Beginner |
| 약정 | 3 weeks, 3-6 hours per week |
| 언어 | English, 자막: Chinese (Traditional), Chinese (Simplified) |
| 통과 방법 | 강좌를 수료하려면 모든 평가 과제를 통과하십시오. |
| 사용자 등급 |
- 비디오: Bias / Variance
- 비디오: Basic Recipe for Machine Learning
- 비디오: Regularization
- 비디오: Why regularization reduces overfitting?
- 비디오: Dropout Regularization
- 비디오: Understanding Dropout
- 비디오: Other regularization methods
- 비디오: Normalizing inputs
- 비디오: Vanishing / Exploding gradients
- 비디오: Weight Initialization for Deep Networks
- 비디오: Numerical approximation of gradients
- 비디오: Gradient checking
- 비디오: Gradient Checking Implementation Notes
- Notebook: Initialization
- Notebook: Regularization
- Notebook: Gradient Checking
- 비디오: Yoshua Bengio interview
- 비디오: Understanding mini-batch gradient descent
- 비디오: Exponentially weighted averages
- 비디오: Understanding exponentially weighted averages
- 비디오: Bias correction in exponentially weighted averages
- 비디오: Gradient descent with momentum
- 비디오: RMSprop
- 비디오: Adam optimization algorithm
- 비디오: Learning rate decay
- 비디오: The problem of local optima
- Notebook: Optimization
- 비디오: Yuanqing Lin interview
- 비디오: Using an appropriate scale to pick hyperparameters
- 비디오: Hyperparameters tuning in practice: Pandas vs. Caviar
- 비디오: Normalizing activations in a network
- 비디오: Fitting Batch Norm into a neural network
- 비디오: Why does Batch Norm work?
- 비디오: Batch Norm at test time
- 비디오: Softmax Regression
- 비디오: Training a softmax classifier
- 비디오: Deep learning frameworks
- 비디오: TensorFlow
- Notebook: Tensorflow
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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| 강좌 구입 | |
|---|---|
| 강좌 자료 이용 | 이용 가능 |
| 성적 평가 자료 이용 | 이용 가능 |
| 최종 성적 받기 | 이용 가능 |
| 공유 가능한 강좌 수료증 취득 | 이용 가능 |
Good！
Concepts are explained very well :)
Thanks Andrew and your team for teaching DNN to me. Then I will keep moving forward in AI industry based on your guiding afterward and aggressively build up personal AI research ability after courses.
Gary
有时课件中有些小错误，也能理解