Artificial intelligence, machine learning, and deep learning

this book is intended to be a fast-paced introduction to various core features of machine learning and deep learning, with code samples that are included in a university course. Index : page 305-319.

Deep learning with TensorFlow and Keras : build and deploy supervised, unsupe…

Edition

Third Edition

ISBN/ISSN

9781803232911

Collation

698 p. ; 24 cm.

Series Title

-

Call Number

005.131 Kap d

Deep learning with Python. Third Edition

The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX!Deep Learning with Python, Third Edition puts the power of deep learning in your hands.This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX

Natural language processing with PyTorch : build intelligent language applica…

Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you're a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMa…

Ultimatics : jurnal teknik informatika, volume 17, nomor 1, June 2025

This journal talks about Analysis and Design of Algorithm, Software Engineering, System and Network Security, Ubiquitous and Mobile Computing, Artificial Intelligence and Machine Learning, Algorithm Theory, World Wide Web, Cryptography, as well as other topics in the field of Informatics.

Cyber Security and Machine Learning : With Python and PyTorch

My goal with this book is to cover AI for cyber security, and AI assurance. I use the term AI although I mainly mean machine learning. AI for cyber security refers to the use of ML algorithms to provide defense for information systems.

Scaling up machine learning : Parallel and distributed approaches

This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

Machine learning : hands-on for developers and technical professionals

Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within cer…

Hands-on large language models : language understanding and generation

With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today. Index : page 393-403

Deep Learning with Python. Second Edition

The book Deep Learning with Python (2nd ed.) is about machine learning, neural networks, and programming with Python

Introduction to machine learning. Third Edition

Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners …

Hands-on machine learning with Scikit-Learn, Keras and TensorFlow : concepts,…

This book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Index : page 811-834.

Applied text analysis with Python ; enabling language-aware data pruducts wit…

The programming landscape of natural language processing has changed dramatically in the past few years. Machine learning approaches now require mature tools like Python's scikit-learn to apply models to text at scale. This practical guide shows programmers and data scientists who have an intermediate-level understanding of Python and a basic understanding of machine learning and natural langua…

Why machines learn : the elegant math behind modern AI

Edition

-

ISBN/ISSN

9780593185742

Collation

464 p. ; 24 cm.

Series Title

-

Call Number

006.3 Ana w

Supremacy : AI, ChatGPT, and the race that will change the world

Edition

-

ISBN/ISSN

9781250337740

Collation

320 p. ; 22 cm.

Series Title

-

Call Number

006.3 Ols s

Machine learning : tingkat dasar dan lanjut

Perusahaan-perusahaan raksasa berbasis IT telah menunjukkan berbagai kesuksesan dalam penggunaan artificial intelligence, khususnya machine learning. Kesuksesan mereka tentu saja tidak instan dalam sekejap. Mereka telah bertahun-tahun membangun sistem itu menggunakan berbagai teknik machine learning, mulai dari teknik tingkat dasar yang simpel, lalu secara terus menerus dikembangkan, hingga saa…

Big data analytics with hadoop 3 : build highly effective analytics solutions…

Edition

-

ISBN/ISSN

9781788628846

Collation

465 p. ; 24 cm.

Series Title

-

Call Number

004.6782 All b

Practical big data analytics : hands-on techniques to implement enterprise an…

Get command of your organizational Big Data using the power of data science and analytics Key Features A perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisions Work with the best tools such as Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform massive online analyses Get expert tips on statistical inference, ma…

Learning Bayesian models with R : become an expert in Bayesian machine learni…

Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems About This Book * Understand the principles of Bayesian Inference with less mathematical equations * Learn state-of-the art Machine Learning methods * Familiarize yourself with the recent advances in Deep Learning and Big Data frameworks with this step-by-step guide Who This Book I…

Unsupervised Learning with R

Work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured dataAbout This Book- Unlock and discover how to tackle clusters of raw data through practical examples in R- Explore your data and create your own models from scratch- Analyze the main aspects of unsupervised learning with this comprehensive, practical step-by-step guideWho This Book…