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Audiobooks > Non-Fiction > Computing & I.T. > Computer Science > Artificial Intelligence > Neural Networks & Fuzzy Systems
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Neural Networks & Fuzzy Systems

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  • The Deep Learning Workshop : Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras - Mirza Rahim Baig
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  • Hands-On Explainable AI (XAI) with Python : Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps - Denis Rothman
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  • Machine Learning for Algorithmic Trading : Predictive Models to Extract Signals from Market and Alternative Data for Systematic Trading Strategies with Python, 2Nd Edition - Stefan Jansen
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  • The Artificial Intelligence Infrastructure Workshop : Build your own highly scalable and robust data storage systems that can support a variety of cutting-edge AI applications - Chinmay Arankalle
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    Chinmay Arankalle, Gareth Dwyer, Bas Geerdink, Kunal Gera and Kevin Liao
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  • The Reinforcement Learning Workshop : Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems - Alessandro Palmas
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  • Thermodynamics of Complex Systems : Principles and applications - Professor Vladimir N. Pokrovskii
    Thermodynamics of Complex Systems
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    Professor Vladimir N. Pokrovskii
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  • Soft Computing Applications and Techniques in Healthcare : Information Technology, Management and Operations Research Practices - Ashish Mishra
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  • Democratization of Expertise : How Cognitive Systems Will Revolutionize Your Life - Ron Fulbright
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  • Statistical Learning Using Neural Networks : A Guide for Statisticians and Data Scientists with Python - Basilio de Braganca Pereira
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    Basilio de Braganca Pereira, Calyampudi Radhakrishna Rao and Fabio Borges de Oliveira
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  • Hands-On Natural Language Processing with PyTorch 1.x : Build smart, AI-driven linguistic applications using deep learning and NLP techniques - Thomas Dop
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  • Building Machine Learning Pipelines - Hannes Hapke
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    Hannes Hapke and Catherine Nelson
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  • Soft Computing Techniques for Type-2 Diabetes Data Classification - Ramalingaswamy Cheruku
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  • Applied Deep Learning and Computer Vision for Self-Driving Cars : Build autonomous vehicles using deep neural networks and behavior-cloning techniques - Sumit Ranjan
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    Sumit Ranjan and Dr. S. Senthamilarasu
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  • Artificial Intelligence Business: How you can profit from AI : Expert tips and techniques for how to profit from AI - Przemek Chojecki
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  • Detecting Regime Change in Computational Finance : Data Science, Machine Learning and Algorithmic Trading - Jun Chen
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    Jun Chen and Edward P K Tsang
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  • Hands-On Gradient Boosting with XGBoost and scikit-learn : Perform accessible machine learning and extreme gradient boosting with Python - Corey Wade
    Hands-On Gradient Boosting with XGBoost and scikit-learn
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    Corey Wade and Kevin Glynn
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  • Component-Based Systems : Estimating Efforts Using Soft Computing Techniques - Kirti Seth
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    Kirti Seth, Ashish Seth and Aprna Tripathi
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  • Artificial Intelligence Techniques in IoT Sensor Networks : Chapman & Hall/CRC Distributed Sensing and Intelligent Systems Series - Mohamed Elhoseny
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    Mohamed Elhoseny, K Shankar and Mohamed AbdelBasset
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  • Hands-on MuleSoft Anypoint platform Volume 1 : Designing and Implementing RAML APIs with MuleSoft Anypoint Platform (English Edition) - Nanda Nachimuthu
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    Nanda Nachimuthu
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  • Computational Social Psychology : Frontiers of Social Psychology - Robin R. Vallacher
    Computational Social Psychology
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    Robin R. Vallacher, Stephen J. Read and Andrzej Nowak
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  • Mastering PyTorch : Build powerful neural network architectures using advanced PyTorch 1.x features - Ashish Ranjan Jha
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    Ashish Ranjan Jha and Dr. Gopinath Pillai
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  • Computational Models of Reading : A Handbook - Erik D. Reichle
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    Erik D. Reichle
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  • Hands-on MuleSoft Anypoint platform Volume 2 : MuleSoft Anypoint Studio Payload, Components, Flow Controls, Scopes and Error Handling (English Edition) - Nanda Nachimuthu
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    Nanda Nachimuthu
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  • TensorFlow 2 Reinforcement Learning Cookbook : Over 50 recipes to help you build, train, and deploy learning agents for real-world applications - Praveen Palanisamy
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    Praveen Palanisamy
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  • Practical Fairness - Aileen Nielsen
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    Aileen Nielsen
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  • Lognormality Principle And Its Applications In E-security, E-learning And E-health, The : Series In Machine Perception And Artificial Intelligence : Book 88 - Rejean Plamondon
    Lognormality Principle And Its Applications In E-security, E-learning And E-health, The
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    Rejean Plamondon, Angelo Marcelli and Miguel Angel Ferrer-ballester
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  • Deep Learning for Computer Vision with SAS : An Introduction - Robert Blanchard
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    Robert Blanchard
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  • Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter : Build scalable real-world projects to implement end-to-end neural networks on Android and iOS - Anubhav Singh
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    Anubhav Singh and Rimjhim Bhadani
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  • Deep Learning Approaches to Text Production : Synthesis Lectures on Human Language Technologies - Shashi Narayan
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    Shashi Narayan and Claire Gardent
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  • Introduction to Graph Neural Networks : Synthesis Lectures on Artificial Intelligence and Machine Learning - Zhiyuan Liu
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    Zhiyuan Liu and Jie Zhou
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  • Machine Learning Interview Questions - Tech Interviews
    Machine Learning Interview Questions
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  • Artificial Intelligence Interview Questions - Tech Interviews
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  • The Internet of Things: From Data to Insight : From Data to Insight - John Davies
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    John Davies and ?Carolina Fortuna
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  • Deep Learning : A Visual Approach - Andrew Glassner
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    Andrew Glassner
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  • Practical Deep Learning : A Python-Based Introduction - Ronald T. Kneusel
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    Ronald T. Kneusel
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  • Practical Artificial Intelligence and Blockchain : A guide to converging blockchain and AI to build smart applications for new economies - Ganesh Prasad Kumble
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    Ganesh Prasad Kumble and Anantha Krishnan
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    Richard Urwin
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  • Artificial Intelligence : Explore Neural Networks and Deep Learning Algorithms - Alec Mueller
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    Alec Mueller
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  • Age of Machines : Artificial Intelligence and Deep Learning Algorithms - Armando Domingos
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    Armando Domingos
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  • Data Science from Scratch with Python : Step-by-Step Guide - Peter Morgan
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    Peter Morgan
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  • Artificial Intelligence and Machine Learning Fundamentals : Develop real-world applications powered by the latest AI advances - Zsolt Nagy
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    Zsolt Nagy
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    Christian Finlay
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  • Statistical Significance Testing for Natural Language Processing : Synthesis Lectures on Human Language Technologies - Rotem Dror
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    Rotem Dror, Lotem Peled-Cohen, Segev Shlomov and Roi Reichart
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    Paolo Perrotta
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  • Subcritical Brain, The : A Synergy Of Segregated Neural Circuits In Memory, Cognition And Sensorimotor Control - Yoram Baram
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    Yoram Baram
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  • The TensorFlow Workshop : A hands-on guide to building deep learning models from scratch using real-world datasets - Matthew Moocarme
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    Matthew Moocarme, Anthony So and Anthony Maddalone
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  • Learning TensorFlow.js - Gant Laborde
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  • Graph Machine Learning : Take graph data to the next level by applying machine learning techniques and algorithms - Claudio Stamile
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    Claudio Stamile, Aldo Marzullo and Enrico Deusebio
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  • Applied Artificial Neural Network Methods for Engineers and Scientists : Solving Algebraic Equations - Snehashish Chakraverty
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  • Graph Representation Learning : Synthesis Lectures on Artificial Intelligence and Machine Learning - William L. Hamilton
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  • Math for Deep Learning : What You Need to Know to Understand Neural Networks - Ronald T. Kneusel
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  • Advanced Deep Learning with R : Become an expert at designing, building, and improving advanced neural network models using R - Bharatendra Rai
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  • Intelligent Projects Using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras - Santanu Pattanayak
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  • Fuzzy Logic-Based Material Selection and Synthesis - Mustafa B Babanli
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    Anirudh Koul, Siddha Ganju and Meher Kasam
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  • Strengthening Deep Neural Networks : Making AI Less Susceptible to Adversarial Trickery - Katy  Warr
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  • Keras Deep Learning Cookbook : Over 30 recipes for implementing deep neural networks in Python - Rajdeep Dua
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