This document summarizes a presentation on training deep neural networks in Python using the Keras framework for human activity classification with inertial sensor data. It discusses common machine learning tasks like regression, classification, and clustering. It then covers steps for data preparation, evaluating algorithms, visualization and presentation of results. Specific deep learning models covered include feedforward neural networks, LSTM networks, and convolutional neural networks. The document provides an overview of concepts like activation functions, cost functions, overfitting, and neural network architectures. It demonstrates examples of training these networks on sensor data for activity classification and prediction.

Training Deep Neural Networks in Python Keras Framework (Tensor Ow Backend) With Inertial Sensor Data For Human Activity Classification PDF

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