Neural networks and fuzzy systems

Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python\'s most popular TensorFlow framework.

Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and KerasKey FeaturesExplore neural network architecture and understand how it functionsLearn algorithms to solve common problems using back propagation and perceptronsUnderstand how to apply neural networks to applications with the help of useful illustrationsBook DescriptionNeural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics.

Use Java and Deeplearning4j to build robust, scalable, and highly accurate AI models from scratchKey FeaturesInstall and configure Deeplearning4j to implement deep learning models from scratchExplore recipes for developing, training, and fine-tuning your neural network models in JavaModel neural networks using datasets containing images, text, and time-series dataBook DescriptionJava is one of the most widely used programming languages in the world.

Bring magic to your mobile apps using TensorFlow Lite and Core MLKey FeaturesExplore machine learning using classification, analytics, and detection tasks.

Implement neural network models in R 3.

Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and KerasKey FeaturesGet to grips with Python's machine learning libraries including scikit-learn, TensorFlow, and KerasImplement advanced concepts and popular machine learning algorithms in real-world projectsBuild analytics, computer vision, and neural network projects Book DescriptionMachine learning is transforming the way we understand and interact with the world around us.

Work through exciting projects to explore the capabilities of Go and Machine LearningKey FeaturesExplore ML tasks and Go's machine learning ecosystemImplement clustering, regression, classification, and neural networks with GoGet to grips with libraries such as Gorgonia, Gonum, and GoCv for training models in GoBook DescriptionGo is the perfect language for machine learning; it helps to clearly describe complex algorithms, and also helps developers to understand how to run efficient optimized code.

Implement machine learning and deep learning methodologies to build smart, cognitive AI projects using PythonKey FeaturesA go-to guide to help you master AI algorithms and concepts8 real-world projects tackling different challenges in healthcare, e-commerce, and surveillanceUse TensorFlow, Keras, and other Python libraries to implement smart AI applicationsBook DescriptionThis book will be a perfect companion if you want to build insightful projects from leading AI domains using Python.

Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful librariesKey FeaturesImplement Q-learning and Markov models with Python and OpenAIExplore the power of TensorFlow to build self-learning modelsEight AI projects to gain confidence in building self-trained applicationsBook DescriptionReinforcement learning is one of the most exciting and rapidly growing fields in machine learning.

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearnKey FeaturesBuild a variety of Hidden Markov Models (HMM)Create and apply models to any sequence of data to analyze, predict, and extract valuable insightsUse natural language processing (NLP) techniques and 2D-HMM model for image segmentationBook DescriptionHidden Markov Model (HMM) is a statistical model based on the Markov chain concept.

Build neural network models in text, vision and advanced analytics using PyTorchKey FeaturesLearn PyTorch for implementing cutting-edge deep learning algorithms.

Understanding and coding advanced deep learning algorithms with the most intuitive deep learning library in existenceKey FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsImplement deep neural networks, autoencoders, GANs, VAEs, and deep reinforcement learningA wide study of GANs, including Improved GANs, Cross-Domain GANs, and Disentangled Representation GANsBook DescriptionRecent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like.

Grasp the fundamental concepts of deep learning using Tensorflow in a hands-on mannerKey Features[*] Get a first-hand experience of the deep learning concepts and techniques with this easy-to-follow guide[*] Train different types of neural networks using Tensorflow for real-world problems in language processing, computer vision, transfer learning, and more[*] Designed for those who believe in the concept of 'learn by doing', this book is a perfect blend of theory and code examplesBook DescriptionDeep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions.

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide.

May 11, 1997, was a watershed moment in the history of artificial intelligence (AI): the IBM supercomputer chess engine, Deep Blue, beat the world Chess champion, Garry Kasparov.

Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI).

An overarching framework for comparing and steering complex adaptive systems is developed through understanding the mechanisms that generate their intricate signal/boundary hierarchies.

Master GenAI techniques to create images and text using variational autoencoders (VAEs), generative adversarial networks (GANs), LSTMs, and large language models (LLMs)Key FeaturesImplement real-world applications of LLMs and generative AIFine-tune models with PEFT and LoRA to speed up trainingExpand your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndexPurchase of the print or Kindle book includes a free eBook in PDF formatBook DescriptionBecome an expert in Generative AI through immersive, hands-on projects that leverage today's most powerful models for Natural Language Processing (NLP) and computer vision.

Foster your NLP applications with the help of deep learning, NLTK, and TensorFlowKey FeaturesWeave neural networks into linguistic applications across various platformsPerform NLP tasks and train its models using NLTK and TensorFlowBoost your NLP models with strong deep learning architectures such as CNNs and RNNsBook DescriptionNatural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas.

Insightful projects to master deep learning and neural network architectures using Python and KerasKey FeaturesExplore deep learning across computer vision, natural language processing (NLP), and image processingDiscover best practices for the training of deep neural networks and their deploymentAccess popular deep learning models as well as widely used neural network architecturesBook DescriptionDeep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier.

A guide to advances in machine learning for financial professionals, with working Python codeKey FeaturesExplore advances in machine learning and how to put them to work in financial industriesClear explanation and expert discussion of how machine learning works, with an emphasis on financial applicationsDeep coverage of advanced machine learning approaches including neural networks, GANs, and reinforcement learningBook DescriptionMachine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending.

Exploring the application and formulation of the EM algorithm, The EM Algorithm and Related Statistical Models offers a valuable method for constructing statistical models when only incomplete information is available, and proposes specific estimation algorithms for solutions to incomplete data problems.

Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and appsPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesImplement -of-the-art graph neural architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook DescriptionGraph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as networks, chemical compounds, or transportation networks.

Not everything is black and white.

From ecosystems to Facebook, from the Internet to the global financial market, some of the most important and familiar natural systems and social phenomena are based on a networked structure.

From ecosystems to Facebook, from the Internet to the global financial market, some of the most important and familiar natural systems and social phenomena are based on a networked structure.

Not everything is black and white.

Get more from your data by creating practical machine learning systems with PythonKey FeaturesDevelop your own Python-based machine learning systemDiscover how Python offers multiple algorithms for modern machine learning systemsExplore key Python machine learning libraries to implement in your projectsBook DescriptionMachine learning allows systems to learn things without being explicitly programmed to do so.

This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and ClusteringKey FeaturesYour handy guide to building machine learning workflows in Go for real-world scenariosBuild predictive models using the popular supervised and unsupervised machine learning techniquesLearn all about deployment strategies and take your ML application from prototype to production readyBook DescriptionMachine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology.

This book is about computational models of reading, or models that explain (and often simulate) the mental processes that allow us to convert the marks on a printed page into the representations that allow us to understand the contents of what we are reading.

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide.

Artificial Neural Network for Drug Design, Delivery and Disposition provides an in-depth look at the use of artificial neural networks (ANN) in pharmaceutical research.

Understand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problemsKey FeaturesGain a comprehensive overview of different machine learning techniquesExplore various methods for selecting a particular algorithmImplement a machine learning project from problem definition through to the final modelBook DescriptionWith huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed.

Discover the use of graph networks to develop a new approach to data science using theoretical and practical methods with this expert guide using Python, printed in colorKey FeaturesCreate networks using data points and informationLearn to visualize and analyze networks to better understand communitiesExplore the use of network data in both - supervised and unsupervised machine learning projectsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionNetwork analysis is often taught with tiny or toy data sets, leaving you with a limited scope of learning and practical usage.

Visualize and build deep learning models with 3D data using PyTorch3D and other Python frameworks to conquer real-world application challenges with easeKey FeaturesUnderstand 3D data processing with rendering, PyTorch optimization, and heterogeneous batchingImplement differentiable rendering concepts with practical examplesDiscover how you can ease your work with the latest 3D deep learning techniques using PyTorch3DBook DescriptionWith this hands-on guide to 3D deep learning, developers working with 3D computer vision will be able to put their knowledge to work and get up and running in no time.

Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs.

Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering how you can use deep learning models in your software applications with Microsoft Cognitive ToolkitKey FeaturesUnderstand the fundamentals of Microsoft Cognitive Toolkit and set up the development environment Train different types of neural networks using Cognitive Toolkit and deploy it to productionEvaluate the performance of your models and improve your deep learning skillsBook DescriptionCognitive Toolkit is a very popular and recently open sourced deep learning toolkit by Microsoft.

Explore a range of Cognitive Services APIs to integrate human-like cognitive capabilities in your applicationsKey FeaturesBuild applications with computer vision, speech recognition, and language processing capabilitiesProcess and analyze data in the form of text, images, and videosBuild smarter applications in Visual Studio using real-world examplesBook DescriptionMicrosoft Cognitive Services is a set of APIs for integrating artificial intelligence in your applications to solve logical business problems.

Build a strong foundation of machine learning algorithms in 7 daysKey FeaturesUse Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a weekKnow when and where to apply data science algorithms using this guideBook DescriptionMachine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data.

Implement supervised, unsupervised, and generative deep learning (DL) models using Keras and Dopamine with TensorFlowKey FeaturesUnderstand the fundamental machine learning concepts useful in deep learningLearn the underlying mathematical concepts as you implement deep learning models from scratchExplore easy-to-understand examples and use cases that will help you build a solid foundation in DLBook DescriptionWith information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning.

Get to grips with building robust XGBoost models using Python and scikit-learn for deploymentKey FeaturesGet up and running with machine learning and understand how to boost models with XGBoost in no timeBuild real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal resultsDiscover tips and tricks and gain innovative insights from XGBoost Kaggle winnersBook DescriptionXGBoost is an industry-proven, open-source software library that provides a gradient boosting framework for scaling billions of data points quickly and efficiently.

Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learnKey FeaturesExploit the power of Python to explore the world of data mining and data analyticsDiscover machine learning algorithms to solve complex challenges faced by data scientists todayUse Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projectsBook DescriptionThe surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions.

Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R librariesKey FeaturesImplement deep learning algorithms to build AI models with the help of tips and tricksUnderstand how deep learning models operate using expert techniquesApply reinforcement learning, computer vision, GANs, and NLP using a range of datasetsBook DescriptionDeep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data.

Understanding and coding advanced deep learning algorithms with the most intuitive deep learning library in existenceKey FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsImplement deep neural networks, autoencoders, GANs, VAEs, and deep reinforcement learningA wide study of GANs, including Improved GANs, Cross-Domain GANs, and Disentangled Representation GANsBook DescriptionRecent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like.

Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs.

Machine Dreaming and Consciousness is the first book to discuss the questions raised by the advent of machine dreaming.

Artificial Neural Network for Drug Design, Delivery and Disposition provides an in-depth look at the use of artificial neural networks (ANN) in pharmaceutical research.

Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and appsPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesImplement -of-the-art graph neural architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook DescriptionGraph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as networks, chemical compounds, or transportation networks.

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and KerasKey FeaturesImplement machine learning algorithms to build, train, and validate algorithmic modelsCreate your own algorithmic design process to apply probabilistic machine learning approaches to trading decisionsDevelop neural networks for algorithmic trading to perform time series forecasting and smart analyticsBook DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML).

Implement neural network architectures by building them from scratch for multiple real-world applications.