Neural networks and fuzzy systems

A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable modelsKey FeaturesLearn how to extract easy-to-understand insights from any machine learning modelBecome well-versed with interpretability techniques to build fairer, safer, and more reliable modelsMitigate risks in AI systems before they have broader implications by learning how to debug black-box modelsBook DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation?

Publisher's Note: A new edition of this book is out now that includes working with GPT-3 and comparing the results with other models.

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystemKey FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook DescriptionIn order to build robust deep learning systems, you'll need to understand everything from how neural networks work to training CNN models.

Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problemsKey FeaturesMaster supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and moreBuild, deploy, and scale end-to-end deep neural network models in a production environmentBook DescriptionThis Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms.

Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data.

Create AI applications in Python and lay the foundations for your career in data scienceKey FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail with interesting examplesMaster core AI concepts with engaging activitiesBook DescriptionMachine learning and neural networks are pillars on which you can build intelligent applications.

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.

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.

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.

Explore TensorFlow's capabilities to perform efficient deep learning on imagesKey FeaturesDiscover image processing for machine visionBuild an effective image classification system using the power of CNNsLeverage TensorFlow's capabilities to perform efficient deep learningBook DescriptionTensorFlow is Google's popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks.

Build, train, and deploy intelligent applications using Java librariesKey FeaturesLeverage the power of Java libraries to build smart applicationsBuild and train deep learning models for implementing artificial intelligenceLearn various algorithms to automate complex tasksBook DescriptionArtificial intelligence (AI) is increasingly in demand as well as relevant in the modern world, where everything is driven by technology and data.

A practical guide to mastering reinforcement learning algorithms using KerasKey FeaturesBuild projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into actionGet to grips with Keras and practice on real-world unstructured datasetsUncover advanced deep learning algorithms such as Monte Carlo, Markov Decision, and Q-learningBook DescriptionReinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks.

Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow.

Get into the world of smart data security using machine learning algorithms and Python librariesKey FeaturesLearn machine learning algorithms and cybersecurity fundamentalsAutomate your daily workflow by applying use cases to many facets of securityImplement smart machine learning solutions to detect various cybersecurity problemsBook DescriptionCyber threats today are one of the costliest losses that an organization can face.

Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examplesKey FeaturesAI-based examples to guide you in designing and implementing machine intelligenceBuild machine intelligence from scratch using artificial intelligence examplesDevelop machine intelligence from scratch using real artificial intelligenceBook DescriptionAI has the potential to replicate humans in every field.

Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and KerasKey FeaturesUnderstand the common architecture of different types of GANsTrain, optimize, and deploy GAN applications using TensorFlow and KerasBuild generative models with real-world data sets, including 2D and 3D dataBook DescriptionDeveloping Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand.

Build and train scalable neural network models on various platforms by leveraging the power of Caffe2Key FeaturesMigrate models trained with other deep learning frameworks on Caffe2Integrate Caffe2 with Android or iOS and implement deep learning models for mobile devicesLeverage the distributed capabilities of Caffe2 to build models that scale easilyBook DescriptionCaffe2 is a popular deep learning library used for fast and scalable training and inference of deep learning models on various platforms.

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.

Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in PythonKey FeaturesDiscover neural network architectures (like CNN and LSTM) that are driving recent advancements in AIBuild expert neural networks in Python using popular libraries such as KerasIncludes projects such as object detection, face identification, sentiment analysis, and moreBook DescriptionNeural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more.

Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges.

Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more Key FeaturesApply popular machine learning algorithms using a recipe-based approachImplement boosting, bagging, and stacking ensemble methods to improve machine learning modelsDiscover real-world ensemble applications and encounter complex challenges in Kaggle competitionsBook DescriptionEnsemble modeling is an approach used to improve the performance of machine learning models.

Implement TensorFlow\'s offerings such as TensorBoard, TensorFlow.

Skip the theory and get the most out of Tensorflow to build production-ready machine learning modelsKey FeaturesExploit the features of Tensorflow to build and deploy machine learning modelsTrain neural networks to tackle real-world problems in Computer Vision and NLPHandy techniques to write production-ready code for your Tensorflow modelsBook DescriptionTensorFlow is an open source software library for Machine Intelligence.

Implement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologiesKey FeaturesLearn advanced concepts in Azure ML and the Cortana Intelligence Suite architectureExplore ML Server using SQL Server and HDInsight capabilitiesImplement various tools in Azure to build and deploy machine learning modelsBook DescriptionImplementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage.

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python librariesKey FeaturesBuild a strong foundation in neural networks and deep learning with Python librariesExplore advanced deep learning techniques and their applications across computer vision and NLPLearn how a computer can navigate in complex environments with reinforcement learningBook DescriptionWith the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands.

Explore various approaches to organize and extract useful text from unstructured data using JavaKey FeaturesUse deep learning and NLP techniques in Java to discover hidden insights in textWork with popular Java libraries such as CoreNLP, OpenNLP, and MalletExplore machine translation, identifying parts of speech, and topic modelingBook DescriptionNatural Language Processing (NLP) allows you to take any sentence and identify patterns, special names, company names, and more.

Grasp the fundamentals of Artificial Intelligence and build your own intelligent systems with easeKey FeaturesEnter the world of AI with the help of solid concepts and real-world use casesExplore AI components to build real-world automated intelligenceBecome well versed with machine learning and deep learning conceptsBook DescriptionVirtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI.

Get into the world of smart data security using machine learning algorithms and Python librariesKey FeaturesLearn machine learning algorithms and cybersecurity fundamentalsAutomate your daily workflow by applying use cases to many facets of securityImplement smart machine learning solutions to detect various cybersecurity problemsBook DescriptionCyber threats today are one of the costliest losses that an organization can face.

Concepts, tools, and techniques to explore deep learning architectures and methodologiesKey FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook DescriptionDeep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data.

Publisher's Note: This edition from 2018 is outdated!

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystemKey FeaturesBuild deep learning models with transfer learning principles in Pythonimplement transfer learning to solve real-world research problemsPerform complex operations such as image captioning neural style transferBook DescriptionTransfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.

Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with PythonKey FeaturesUnderstand how to obtain financial data via Quandl or internal systemsAutomate commercial banking using artificial intelligence and Python programsImplement various artificial intelligence models to make personal banking easyBook DescriptionRemodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI).

Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulatorKey FeaturesExplore the OpenAI Gym toolkit and interface to use over 700 learning tasksImplement agents to solve simple to complex AI problemsStudy learning environments and discover how to create your ownBook DescriptionMany real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken.

Leverage the full potential of SAS to get unique, actionable insights from your dataKey FeaturesBuild enterprise-class data solutions using SAS and become well-versed in SAS programmingWork with different data structures, and run SQL queries to manipulate your dataExplore essential concepts and techniques with practical examples to confidently pass the SAS certification examBook DescriptionSAS is one of the leading enterprise tools in the world today when it comes to data management and analysis.

Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and FlutterKey FeaturesWork through projects covering mobile vision, style transfer, speech processing, and multimedia processingCover interesting deep learning solutions for mobileBuild your confidence in training models, performance tuning, memory optimization, and neural network deployment through every projectBook DescriptionDeep learning is rapidly becoming the most popular topic in the mobile app industry.

Learn how to use AI and blockchain to build decentralized intelligent applications (DIApps) that overcome real-world challengesKey FeaturesUnderstand the fundamental concepts for converging artificial intelligence and blockchainApply your learnings to build apps using machine learning with Ethereum, IPFS, and MoiBitGet well-versed with the AI-blockchain ecosystem to develop your own DIAppsBook DescriptionAI and blockchain are two emerging technologies catalyzing the pace of enterprise innovation.

Leverage the power of deep learning and Keras to develop smarter and more efficient data modelsKey FeaturesUnderstand different neural networks and their implementation using KerasExplore recipes for training and fine-tuning your neural network modelsPut your deep learning knowledge to practice with real-world use-cases, tips, and tricksBook DescriptionKeras has quickly emerged as a popular deep learning library.

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.

Grasp the fundamentals of Artificial Intelligence and build your own intelligent systems with easeKey FeaturesEnter the world of AI with the help of solid concepts and real-world use casesExplore AI components to build real-world automated intelligenceBecome well versed with machine learning and deep learning conceptsBook DescriptionVirtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI.

Leverage the power of machine learning and Swift programming to build intelligent iOS applications with easeKey FeaturesImplement effective machine learning solutions for your iOS applicationsUse Swift and Core ML to build and deploy popular machine learning modelsDevelop neural networks for natural language processing and computer visionBook DescriptionMachine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot.

Create AI applications in Python and lay the foundations for your career in data scienceKey FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail with interesting examplesMaster core AI concepts with engaging activitiesBook DescriptionMachine learning and neural networks are pillars on which you can build intelligent applications.

Build, train, and deploy intelligent applications using Java librariesKey FeaturesLeverage the power of Java libraries to build smart applicationsBuild and train deep learning models for implementing artificial intelligenceLearn various algorithms to automate complex tasksBook DescriptionArtificial intelligence (AI) is increasingly in demand as well as relevant in the modern world, where everything is driven by technology and data.

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystemKey FeaturesBuild deep learning models with transfer learning principles in Pythonimplement transfer learning to solve real-world research problemsPerform complex operations such as image captioning neural style transferBook DescriptionTransfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.

Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.

OpenAI's GPT-3, ChatGPT, GPT-4 and Hugging Face transformers for language tasks in one book.

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.

Learn to build powerful machine learning models quickly and deploy large-scale predictive applicationsKey Features[*]Design, engineer and deploy scalable machine learning solutions with the power of Python[*]Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework[*]Build state-of-the-art models and develop personalized recommendations to perform machine learning at scaleBook DescriptionLarge Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle.

Electrical, communication, transportation, computer, and neural networks are special kinds of nets.

This book is a follow-up to the IChemE symposium on "e;Neural Networks and Other Learning Technologies"e;, held at Imperial College, UK, in May 1999.

Learn to build powerful machine learning models quickly and deploy large-scale predictive applicationsKey Features[*]Design, engineer and deploy scalable machine learning solutions with the power of Python[*]Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework[*]Build state-of-the-art models and develop personalized recommendations to perform machine learning at scaleBook DescriptionLarge Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle.