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 |
very useful course
Very helpful! This course helps me step into the details of deep neural networks in practice, and teaches me how to fix those issues, as well as Tensorflow which is a popular deep learning programming framework using Python.
Great course!
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ns
Excellent course from the legend Andrew Ng and his team.