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Coursework for Deep Learning A-Z™: Hands-On Artificial Neural Networks

DeepLearningA-Z

Coursework for Deep Learning A-Z™: Hands-On Artificial Neural Networks

This is a practical course for implementing Deep Learning techniques through the use of Python based open-source libraries as TensorFLow, Theano, Keras and PyTorch. The course structure is divided in two parts, Supervised Neural Networks and Unsupervised. For each part a set of three study cases were provided to implement the theory from a single layer ANN up to Bolztmann Machines and Stacked Autoencoders. The following list describes the problems:

• Artificial Neural Networks to solve a Customer Churn problem
• Convolutional Neural Networks for Image Recognition
• Recurrent Neural Networks to predict Stock Prices
• Self-Organizing Maps to investigate Fraud
• Boltzmann Machines to create a Recomender System
• Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize

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