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. |
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| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
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Complete and interesting
Amazing!
Great cover of fundamentals. My background in optimization helps with understanding tuning so it wasn't overwhelming. Great learning opportunity for NN tuning and intro to Tensorflow.
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It's the easiest course for various deep learning techniques I have seen so far while touch base on critical concepts. Programming assignments are not hard to follow even for someone with no technical background.