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.
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.
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.
The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks.
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.
Neural Nets and Chaotic Carriers develops rational principles for the design of associative memories, with a view to applying these principles to models with irregularly oscillatory operation so evident in biological neural systems, and necessitated by the meaninglessness of absolute signal levels.
Written in accessible language without mathematical formulas, this short book provides an overview of the wide and varied applications of artificial intelligence (AI) across the spectrum of physical sciences.
This book contains twenty-two original contributions that provide a comprehensive overview of computational approaches to understanding a single neuron structure.
Written in accessible language without mathematical formulas, this short book provides an overview of the wide and varied applications of artificial intelligence (AI) across the spectrum of physical sciences.
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.
Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines.
Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing.
This book provides an in-depth overview of artificial intelligence and deep learning approaches with case studies to solve problems associated with biometric security such as authentication, indexing, template protection, spoofing attack detection, ROI detection, gender classification etc.
With so much artificial intelligence (AI) in the headlines, it is no surprise that businesses are scrambling to exploit this exciting and transformative technology.
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework.
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.
A compact guide to mastering TensorFlow 2, covering essential APIs, datasets, and practical applications for efficient machine learning and deep learning projects.
Focusing on how visual information is represented, stored and extracted in the human brain, this book uses cognitive neural modeling in order to show how visual information is represented and memorized in the brain.
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.
Image Processing and Pattern Recognition covers major applications in the field, including optical character recognition, speech classification, medical imaging, paper currency recognition, classification reliability techniques, and sensor technology.
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.
Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guideKey FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook DescriptionVarious intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome.
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.
Interactive media are a human-machine interface that allows people to connect with each other by making them active participants in the media they consume through text, graphics, audio and video.
After many years during which it languished in relative obscurity, in remote classrooms of computer science departments and in small prototype projects for tech companies, artificial intelligence (AI) is now a searingly hot topic across the media.
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.
Design and use machine learning models for music generation using Magenta and make them interact with existing music creation toolsKey FeaturesLearn how machine learning, deep learning, and reinforcement learning are used in music generationGenerate new content by manipulating the source data using Magenta utilities, and train machine learning models with itExplore various Magenta projects such as Magenta Studio, MusicVAE, and NSynthBook DescriptionThe importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation.
State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks.
This volume covers practical and effective implementation techniques, including recurrent methods, Boltzmann machines, constructive learning with methods for the reduction of complexity in neural network systems, modular systems, associative memory, neural network design based on the concept of the Inductive Logic Unit, and a comprehensive treatment of implementations in the area of data classification.
Learn the ropes of supervised machine learning with R by studying popular real-world use-cases, and understand how it drives object detection in driver less cars, customer churn, and loan default prediction.
Your one-stop guide to learning and implementing artificial neural networks with Keras effectivelyKey FeaturesDesign and create neural network architectures on different domains using KerasIntegrate neural network models in your applications using this highly practical guideGet ready for the future of neural networks through transfer learning and predicting multi network modelsBook DescriptionNeural networks are used to solve a wide range of problems in different areas of AI and deep learning.
Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed.
Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problemsIncludes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChainKey FeaturesThis second edition delves deeper into key machine learning topics, CI/CD, and system designExplore core MLOps practices, such as model management and performance monitoringBuild end-to-end examples of deployable ML microservices and pipelines using AWS and open-source toolsBook DescriptionThe Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems.
