Ritesh kumar (441 résultats)

Etat : New. pp. 32. Ritesh Kumar Behera (illustrateur).

Etat : New.

Etat : New. pp. 32. Ritesh Kumar Behera (illustrateur).

Etat : New. pp. 32. Ritesh Kumar Behera (illustrateur).

Paperback. Etat : New.

Etat : New. pp. 194.

Etat : New.

Paperback. Etat : Brand New. 27 pages. 9.00x6.00x0.07 inches. In Stock. Kumar, Shubhendra (illustrateur).

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Etat : New. pp. 194.

Etat : New. pp. 194.

Etat : New. pp. 194.

Etat : New. pp. 194.

Etat : New. pp. 194.

Soft cover. Etat : New. ISBN:9788119626694,32pp. Ritesh Kumar Behera (illustrateur).

Etat : New.

Etat : New.

Paperback. Etat : Good. Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python Key Features Get up and running with artificial intelligence in no time using hands-on problem-solving recipes Explore popular Python libraries and tools to build AI solutions for images, text, sounds, and images Implement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much more Book DescriptionArtificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you?ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you?ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.What you will learn Implement data preprocessing steps and optimize model hyperparameters Delve into representational learning with adversarial autoencoders Use active learning, recommenders, knowledge embedding, and SAT solvers Get to grips with probabilistic modeling with TensorFlow probability Run object detection, text-to-speech conversion, and text and music generation Apply swarm algorithms, multi-agent systems, and graph networks Go from proof of concept to production by deploying models as microservices Understand how to use modern AI in practice Who this book is forThis AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You?ll also find this book useful if you?re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.…

Etat : New.

Soft cover. Etat : New. Concepts of Machine Learning with Practical Approaches. KEY FEATURES Includes real-scenario examples to explain the working of Machine Learning algorithms. Includes graphical and statistical representation to simplify modeling Machine Learning and Neural Networks. Full of Python codes, numerous exercises, and model question papers for data science students. DESCRIPTION The book offers the readers the fundamental concepts of Machine Learning techniques in a user-friendly language. The book aims to give in-depth knowledge of the different Machine Learning (ML) algorithms and the practical implementation of the various ML approaches. This book covers different Supervised Machine Learning algorithms such as Linear Regression Model, Naïve Bayes classifier Decision Tree, K-nearest neighbor, Logistic Regression, Support Vector Machine, Random forest algorithms, Unsupervised Machine Learning algorithms such as k-means clustering, Hierarchical Clustering, Probabilistic clustering, Association rule mining, Apriori Algorithm, f-p growth algorithm, Gaussian mixture model and Reinforcement Learning algorithm such as Markov Decision Process (MDP), Bellman equations, policy evaluation using Monte Carlo, Policy iteration and Value iteration, Q-Learning, State-Action-Reward-State-Action (SARSA). It also includes various feature extraction and feature selection techniques, the Recommender System, and a brief overview of Deep Learning. By the end of this book, the reader can understand Machine Learning concepts and easily implement various ML algorithms to real-world problems.…

Etat : New.

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Etat : New. In English.

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Etat : As New. Unread book in perfect condition.

Paperback. Etat : New.

Etat : New.