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Learn Deep Learning (Neural Networks) Models from scratch
- Understand the major technology trends driving Deep Learning
- Be able to build, train and apply fully connected deep neural networks
- Know how to implement efficient (vectorized) neural networks
- Understand the key parameters in a neural network's architecture
- describe what a neural network is, what a deep learning model is, and the difference between them.
- demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines.
- demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks.
- build deep learning models and networks using the Keras library.
- Understand how to build a convolutional neural network, including recent variations such as residual networks.
- Know how to apply convolutional networks to visual detection and recognition tasks.
- Know to use neural style transfer to generate art.
- Be able to apply these algorithms to a variety of images, videos, and other 2D or 3D data.