关于此课程： 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.
此课程适用人群： 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, Adjunct Lecturer at Stanford University, deeplearning.ai, Ecole Centrale Paris
教学方： Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| 基本信息 | 课程 2（共 5 门，Deep Learning Specialization ） |
| 级别 | Beginner |
| 承诺学习时间 | 3 weeks, 3-6 hours per week |
| 语言 | English, 字幕： Chinese (Traditional), Chinese (Simplified) |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
- 视频: Train / Dev / Test sets
- 视频: Bias / Variance
- 视频: Basic Recipe for Machine Learning
- 视频: Regularization
- 视频: Why regularization reduces overfitting?
- 视频: Dropout Regularization
- 视频: Understanding Dropout
- 视频: Other regularization methods
- 视频: Normalizing inputs
- 视频: Vanishing / Exploding gradients
- 视频: Weight Initialization for Deep Networks
- 视频: Numerical approximation of gradients
- 视频: Gradient checking
- 视频: Gradient Checking Implementation Notes
- Notebook: Initialization
- Notebook: Regularization
- Notebook: Gradient Checking
- 视频: Yoshua Bengio interview
- 视频: Mini-batch gradient descent
- 视频: Understanding mini-batch gradient descent
- 视频: Exponentially weighted averages
- 视频: Understanding exponentially weighted averages
- 视频: Bias correction in exponentially weighted averages
- 视频: Gradient descent with momentum
- 视频: RMSprop
- 视频: Adam optimization algorithm
- 视频: Learning rate decay
- 视频: The problem of local optima
- Notebook: Optimization
- 视频: Yuanqing Lin interview
- 视频: Tuning process
- 视频: Using an appropriate scale to pick hyperparameters
- 视频: Hyperparameters tuning in practice: Pandas vs. Caviar
- 视频: Normalizing activations in a network
- 视频: Fitting Batch Norm into a neural network
- 视频: Why does Batch Norm work?
- 视频: Batch Norm at test time
- 视频: Softmax Regression
- 视频: Training a softmax classifier
- 视频: Deep learning frameworks
- 视频: TensorFlow
- Notebook: Tensorflow
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The Best
Very good content. Professor Ng covers a lot of material in a gentle and steady way. A few errors in the assignment and less clarity on some texts and quiz make me give 4 stars, but overall it's a very useful, important course, I think.
Really helpful, especially concepts of Transfer/Multi-task learning
Very practical advice on how to fine tuning your neural networks