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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| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Great course with practical tools
Again, fantastic. Great way to explain how to tune your algorithms to improve bias and variance. Great explanation of what optimizers are used and how they function. Glad to know the nuts and bolts of the parameters usually defined in machine learning frameworks
Another great class, the final programming assignment is tricky and requires one to get a strong grasp of tensorflow (which is a great thing).
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CZ
Deepened my understanding of how to make deep neural networks work better!