Size: 1.19 GB
Generative Adversarial Networks and Variational Autoencoders in Python, Theano, and Tensorflow
What you’ll learn
- Learn the basic principles of generative models
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Build a variational autoencoder in Theano and Tensorflow
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Build a GAN (Generative Adversarial Network) in Theano and Tensorflow
Requirements
- Know how to build a neural network in Theano and/or Tensorflow
- Probability
- Multivariate Calculus
- Numpy, etc.
Description
Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently. Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs.- Calculus
- Probability
- Object-oriented programming
- Python coding: if/else, loops, lists, dicts, sets
- Numpy coding: matrix and vector operations
- Linear regression
- Gradient descent
- Know how to build a feedforward and convolutional neural network in Theano and TensorFlow
- Watch it at 2x.
- Take handwritten notes. This will drastically increase your ability to retain the information.
- Write down the equations. If you don’t, I guarantee it will just look like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- Realize that most exercises will take you days or weeks to complete.
- Write code yourself, don’t just sit there and look at my code.
- Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)
Who this course is for:
- Anyone who wants to improve their deep learning knowledge
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