Take a look at Deep Learning concepts with Keras by analysing an image recognition project and learning to develop the model from start to finish.

Examine the business needs of a project and design a solution, create a multi layer network and get an intro to some more sophisticated practices including implementing different types of networks for image recognition, using dropouts and random noise to improve results, and select the proper architecture.

DL Basics

• Introduction to Deep Learning
• Key ML concepts and terminology
• Examples in industry and research
• Formalizing your own DL problem

The Perceptron

• Theory
• Implementing it from scratch
• Implementing it in keras

Feedforward Neural Networks

• Training neural networks, optimization methods, error back-propagation
• Introduction to Keras and Pytorch

Lab 2: Implement and train a feed-forward neural network in Keras
Tackling the problem of facial expression recognition

Recurrent Neural Networks

• Understanding recurrent architectures
• Elman, LSTM and GRU units
• Bi-directional architectures
• Combining RNNs with convolutional and feed-forward layers
• Applications to speech
• Biological sequences and information retrieval

Lab 4: Implementing RNNs using Keras
Implementing and training RNNs using LSTM units on a simple natural language processing task

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