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Deep learning (Machine learning) --- Learning, Deep (Machine learning) --- Iterative methods (Mathematics) --- Machine learning

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Deep learning (Machine learning) --- Signal processing. --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Learning, Deep (Machine learning) --- Iterative methods (Mathematics) --- Machine learning

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Computer vision. --- Deep learning (Machine learning) --- Computer vision --- Learning, Deep (Machine learning) --- Iterative methods (Mathematics) --- Machine learning --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems

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Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI. After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level. What You Will Learn Know the basic concepts of optimization tuners, search space, and trials Apply different hyper-parameter optimization algorithms to develop effective neural networks Construct new deep learning models from scratch Execute the automated Neural Architecture Search to create state-of-the-art deep learning models Compress the model to eliminate unnecessary deep learning layers.

Deep learning (Machine learning) --- Neural networks (Computer science) --- Python (Computer program language) --- Scripting languages (Computer science) --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Learning, Deep (Machine learning) --- Iterative methods (Mathematics) --- Machine learning

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This edited book covers ongoing research in both theory and practical applications of using deep learning for social media data. Social networking platforms are overwhelmed by different contents, and their huge amounts of data have enormous potential to influence business, politics, security, planning and other social aspects. Recently, deep learning techniques have had many successful applications in the AI field. The research presented in this book emerges from the conviction that there is still much progress to be made toward exploiting deep learning in the context of social media data analytics. It includes fifteen chapters, organized into four sections that report on original research in network structure analysis, social media text analysis, user behaviour analysis and social media security analysis. This work could serve as a good reference for researchers, as well as a compilation of innovative ideas and solutions for practitioners interested in applying deep learning techniques to social media data analytics. .

Deep learning (Machine learning) --- Learning, Deep (Machine learning) --- Iterative methods (Mathematics) --- Machine learning --- Engineering --- Cooperating objects (Computer systems). --- Computational intelligence. --- Big data. --- Social media. --- Data Engineering. --- Cyber-Physical Systems. --- Computational Intelligence. --- Big Data. --- Social Media. --- Data processing. --- User-generated media --- Communication --- User-generated content --- Data sets, Large --- Large data sets --- Data sets --- Intelligence, Computational --- Artificial intelligence --- Soft computing