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Artificial Intelligence By Example: Develop machine intelligence from scratch using real artificial intelligence use cases
Artificial Intelligence By Example: Develop machine intelligence from scratch using real artificial intelligence use cases

Be an adaptive thinker that leads the way to Artificial Intelligence

Key Features

  • AI-based examples to guide you in designing and implementing machine intelligence
  • Develop your own method for future AI solutions
  • Acquire advanced AI, machine learning, and...
Markov Chains: Models, Algorithms and Applications (International Series in Operations Research & Management Science)
Markov Chains: Models, Algorithms and Applications (International Series in Operations Research & Management Science)
Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order...
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms...
Hidden Markov Models for Time Series: An Introduction Using R (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
Hidden Markov Models for Time Series: An Introduction Using R (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Reveals How HMMs Can Be Used as General-Purpose Time Series Models

Implements all methods in R
Hidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide range of time series types, from continuous-valued, circular, and
...

Super-Recursive Algorithms (Monographs in Computer Science)
Super-Recursive Algorithms (Monographs in Computer Science)
This book introduces the new realm of superrecursive algorithms and the development of mathematical models for them. Although many still believe that only recursive algorithms exist and that only some of them are realizable, there are many situations in which people actually work with superrecursive algorithms....
Data Analytics for Engineering and Construction  Project Risk Management (Risk, Systems and Decisions)
Data Analytics for Engineering and Construction Project Risk Management (Risk, Systems and Decisions)

This book provides a step-by-step guidance on how to implement analytical methods in project risk management. The text focuses on engineering design and construction projects and as such is suitable for graduate students in engineering, construction, or project management, as well as practitioners aiming to develop, improve,...

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition
Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries

Key Features

  • Build a strong foundation in neural networks and deep learning with Python libraries
  • Explore advanced deep learning techniques and their applications...
Simulation-Based Algorithms for Markov Decision Processes (Communications and Control Engineering)
Simulation-Based Algorithms for Markov Decision Processes (Communications and Control Engineering)
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences.  Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical...
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms

Key Features

  • Explore statistics and complex mathematics for data-intensive applications
  • Discover new developments in EM algorithm, PCA, and bayesian...
Applied Statistics: Theory and Problem Solutions with R
Applied Statistics: Theory and Problem Solutions with R

Instructs readers on how to use methods of statistics and experimental design with R software 

Applied statistics covers both the theory and the application of modern statistical and mathematical modelling techniques to applied problems in industry, public services, commerce, and research. It proceeds...

The Art of Modeling in Science and Engineering with Mathematica
The Art of Modeling in Science and Engineering with Mathematica
Modeling is practiced in engineering and all physical sciences. Many specialized texts exist - written at a high level - that cover this subject. However, students and even professionals often experience difficulties in setting up and solving even the simplest of models. This can be attributed to three difficulties: the proper choice of model,...
Deep Learning Essentials: Your hands-on guide to the fundamentals of deep learning and neural network modeling
Deep Learning Essentials: Your hands-on guide to the fundamentals of deep learning and neural network modeling

Get to grips with the essentials of deep learning by leveraging the power of Python

Key Features

  • Your one-stop solution to get started with the essentials of deep learning and neural network modeling
  • Train different kinds of neural networks to tackle various problems in...
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