About this course: 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.
Who is this class for: 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.
Taught by: Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
| Basic Info | Course 2 of 5 in the Deep Learning Specialization |
| Level | Beginner |
| Commitment | 3 weeks, 3-6 hours per week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Video: Train / Dev / Test sets
- Video: Bias / Variance
- Video: Basic Recipe for Machine Learning
- Video: Regularization
- Video: Why regularization reduces overfitting?
- Video: Dropout Regularization
- Video: Understanding Dropout
- Video: Other regularization methods
- Video: Normalizing inputs
- Video: Vanishing / Exploding gradients
- Video: Weight Initialization for Deep Networks
- Video: Numerical approximation of gradients
- Video: Gradient checking
- Video: Gradient Checking Implementation Notes
- Notebook: Initialization
- Notebook: Regularization
- Notebook: Gradient Checking
- Video: Yoshua Bengio interview
- Video: Mini-batch gradient descent
- Video: Understanding mini-batch gradient descent
- Video: Exponentially weighted averages
- Video: Understanding exponentially weighted averages
- Video: Bias correction in exponentially weighted averages
- Video: Gradient descent with momentum
- Video: RMSprop
- Video: Adam optimization algorithm
- Video: Learning rate decay
- Video: The problem of local optima
- Notebook: Optimization
- Video: Yuanqing Lin interview
- Video: Tuning process
- Video: Using an appropriate scale to pick hyperparameters
- Video: Hyperparameters tuning in practice: Pandas vs. Caviar
- Video: Normalizing activations in a network
- Video: Fitting Batch Norm into a neural network
- Video: Why does Batch Norm work?
- Video: Batch Norm at test time
- Video: Softmax Regression
- Video: Training a softmax classifier
- Video: Deep learning frameworks
- Video: TensorFlow
- Notebook: Tensorflow
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| Purchase Course | Audit | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Available | Not available |
| Receive a final grade | Available | Not available |
| Earn a shareable Course Certificate | Available | Not available |
The best course to know about tuning a neural network. Learned a lot in this course. Andrew is the Best!!!!
Covers batch norm and optimizers nicely.
Concept explanation is very good and complex topics are dealt well.
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ØS
Ng is an excellent teacher, and it was fun to learn about programming frameworks. However, the programming exercises are very simple, and the videos about numerics go very slow, thus 4 stars and not 5.