This course aims to:

• understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
• be able to apply sequence models to natural language problems, including text synthesis.
• be able to apply sequence models to audio applications, including speech recognition and music synthesis.

This course aims to:

• 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 image, video, and other 2D or 3D data.

This course aims to:

• understand how to diagnose errors in a machine learning system, and
• be able to prioritize the most promising directions for reducing error
• understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance
• know how to apply end-to-end learning, transfer learning, and multi-task learning

This course aims to:

• 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 course aims to:

• 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