Work through practical recipes to learn how to solve complex machine learning and deep learning problems using PythonKey FeaturesGet up and running with artificial intelligence in no time using hands-on problem-solving recipesExplore popular Python libraries and tools to build AI solutions for images, text, sounds, and imagesImplement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much moreBook DescriptionArtificial intelligence (AI) plays an integral role in automating problem-solving.
Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learningKey FeaturesDevelop and deploy deep reinforcement learning-based solutions to production pipelines, products, and servicesExplore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic methodCustomize and build RL-based applications for performing real-world tasksBook DescriptionWith deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions.
Take a comprehensive and step-by-step approach to understanding machine learningKey FeaturesDiscover how to apply the scikit-learn uniform API in all types of machine learning modelsUnderstand the difference between supervised and unsupervised learning modelsReinforce your understanding of machine learning concepts by working on real-world examplesBook DescriptionMachine learning algorithms are an integral part of almost all modern applications.
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.
Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing.
Towards Neuromorphic Machine Intelligence: Spike-Based Representation, Learning, and Applications provides readers with in-depth understanding of Spiking Neural Networks (SNNs), which is a burgeoning research branch of Artificial Neural Networks (ANNs), AI, and Machine Learning that sits at the heart of the integration between Computer Science and Neural Engineering.
The book aims to highlight the potential of deep learning (DL)-enabled methods in intelligent fault diagnosis (IFD), along with their benefits and contributions.
This book uncovers the stakes and possibilities of handling pandemic diseases with the help of Computational Intelligence, using cases and applications from the current Covid-19 pandemic.
An intense, psychologically charged domestic drama, The Return is a brilliant and haunting exploration of the insecurities that lie at the heart of human relationships.
The book aims to highlight the potential of deep learning (DL)-enabled methods in intelligent fault diagnosis (IFD), along with their benefits and contributions.
The story follows an unnamed narrator who visits a mental institution in southern France (more accurately, a "Maison de Sante") known for a revolutionary new method of treating mental illnesses called the "system of soothing".
Unlock the transformative potential of deep learning in your professional and academic endeavors with Deep Learning in Engineering, Energy and Finance: Principals and Applications.
An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithmsKey FeaturesCovers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithmLearn how to implement algorithms with code by following examples with line-by-line explanationsExplore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrationsBook DescriptionWith significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit.
Bring magic to your mobile apps using TensorFlow Lite and Core MLKey FeaturesExplore machine learning using classification, analytics, and detection tasks.
This book aims to provide a comprehensive overview of the applications of Artificial Intelligence (AI) in the area of Cybersecurity and Digital Forensics.
Neural network models, in addition to being of intrinsic theoretical interest, have also proved to be a useful framework in which issues in theoretical biology can be put into perspective.
These proceedings present the state of the art in Spanish research on pattern recognition, image processing, speech recognition, and artificial neural networks and applications to medicine, geology, control etc.
This volume contains the proceedings of the seventh Italian Workshop on Neural Nets WIRN VIETRI '95, organized by the International Institute for Advanced Scientific Studies 'E R Caianiello' (IIASS) and Societa Italiana Reti Neuroniche (SIREN).
As perception stands for the acquisition of a real world representation by interaction with an environment, learning is the modification of this internal representation.
