關於此課程：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.
此課程適用人群：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.
教學方： Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
教學方： Head Teaching Assistant - Kian Katanforoosh, Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
教學方： Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| 基本信息 | 課程 2（共 5 門，Deep Learning Specialization ） |
| 級別 | 初級 |
| 承諾學習時間 | 3 weeks, 3-6 hours per week |
| 語言 | English, 字幕：Chinese (Traditional), Chinese (Simplified), Korean, Turkish |
| 如何通過 | 通過所有計分作業以完成課程。 |
| 用戶評分 |
- 視頻: Bias / Variance
- 視頻: Basic Recipe for Machine Learning
- 視頻: Regularization
- 視頻: Why regularization reduces overfitting?
- 視頻: Dropout Regularization
- 視頻: Understanding Dropout
- 視頻: Other regularization methods
- 視頻: Normalizing inputs
- 視頻: Vanishing / Exploding gradients
- 視頻: Weight Initialization for Deep Networks
- 視頻: Numerical approximation of gradients
- 視頻: Gradient checking
- 視頻: Gradient Checking Implementation Notes
- Notebook: Initialization
- Notebook: Regularization
- Notebook: Gradient Checking
- 視頻: Yoshua Bengio interview
- 視頻: Understanding mini-batch gradient descent
- 視頻: Exponentially weighted averages
- 視頻: Understanding exponentially weighted averages
- 視頻: Bias correction in exponentially weighted averages
- 視頻: Gradient descent with momentum
- 視頻: RMSprop
- 視頻: Adam optimization algorithm
- 視頻: Learning rate decay
- 視頻: The problem of local optima
- Notebook: Optimization
- 視頻: Yuanqing Lin interview
- 視頻: Using an appropriate scale to pick hyperparameters
- 視頻: Hyperparameters tuning in practice: Pandas vs. Caviar
- 視頻: Normalizing activations in a network
- 視頻: Fitting Batch Norm into a neural network
- 視頻: Why does Batch Norm work?
- 視頻: Batch Norm at test time
- 視頻: Softmax Regression
- 視頻: Training a softmax classifier
- 視頻: Deep learning frameworks
- 視頻: TensorFlow
- Notebook: Tensorflow
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.
| 購買課程 | |
|---|---|
| 訪問課程材料 | 可用 |
| 訪問評分的材料 | 可用 |
| 收到最終成績 | 可用 |
| 獲得可共享的課程證書 | 可用 |
A very good course to learn optimization in Deep Neural Networks. It provides the knowledge in both, broader dimension as well as deeper dimension.
The course was very helpful
Excellent! I've learnt about useful optimizations and some delicate things about neural networks.
Excellent!