Techniques And Tools For Artificial Intelligence Neural Networks Via R And Python
Download Techniques And Tools For Artificial Intelligence Neural Networks Via R And Python PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Techniques And Tools For Artificial Intelligence Neural Networks Via R And Python book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
TECHNIQUES AND TOOLS FOR ARTIFICIAL INTELLIGENCE. NEURAL NETWORKS VIA R AND PYTHON
Artificial Intelligence combines mathematical algorithms and Machine Learning, Deep Learning and Big Data techniques to extract the knowledge contained in data and present it in a comprehensible and automatic way. In this book, the use of neural networks for supervised and unsupervised learning is discussed in depth. Regarding supervised learning, the most common architectures are considered, such as Multilayer Perceptron, Radial Basis Network, ADALINE Networks, HOPFIELD Networks, Probabilistic Networks, Linear Networks, Generalised Regression Networks, LVQ Networks, Linear Networks and Networks for Regression Model Optimisation. In this section of supervised analysis, special attention should be paid to Neural Networks for Time Series Prediction such as the LSTM Network, GRU Networks, Recurrent Neural Networks RNN, NARX Networks, NNAR Networks and, in general, Dynamic Neural Networks. Unsupervised learning develops Pattern Recognition and Cluster Analysis Networks such as KOHONEN Networks (SOM Self-Organising Maps), Pattern Recognition Networks, Autoencoder Neural Networks, Transfer Learning Networks, Anomaly Detection Networks and Convolutional Neural Networks. The following topics describe methodologically the architectures of the different types of neural networks and their usefulness in practical applications. In addition, for each type of neural network, examples are presented with an optimal syntax in the R and Python languages.
Applied Software Development With Python & Machine Learning By Wearable & Wireless Systems For Movement Disorder Treatment Via Deep Brain Stimulation
The book presents the confluence of wearable and wireless inertial sensor systems, such as a smartphone, for deep brain stimulation for treating movement disorders, such as essential tremor, and machine learning. The machine learning distinguishes between distinct deep brain stimulation settings, such as 'On' and 'Off' status. This achievement demonstrates preliminary insight with respect to the concept of Network Centric Therapy, which essentially represents the Internet of Things for healthcare and the biomedical industry, inclusive of wearable and wireless inertial sensor systems, machine learning, and access to Cloud computing resources.Imperative to the realization of these objectives is the organization of the software development process. Requirements and pseudo code are derived, and software automation using Python for post-processing the inertial sensor signal data to a feature set for machine learning is progressively developed. A perspective of machine learning in terms of a conceptual basis and operational overview is provided. Subsequently, an assortment of machine learning algorithms is evaluated based on quantification of a reach and grasp task for essential tremor using a smartphone as a wearable and wireless accelerometer system.Furthermore, these skills regarding the software development process and machine learning applications with wearable and wireless inertial sensor systems enable new and novel biomedical research only bounded by the reader's creativity.Related Link(s)
Deep Learning for Coders with fastai and PyTorch
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala