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Python: Beginner's Guide to Artificial Intelligence: Build applications to intelligently interact with the world around you using Python
Python: Beginner's Guide to Artificial Intelligence: Build applications to intelligently interact with the world around you using Python

Develop real-world applications powered by the latest advances in intelligent systems

Key Features

  • Gain real-world contextualization using deep learning problems concerning research and application
  • Get to know the best practices to improve and optimize your machine...
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Graphics in this book are printed in black and white.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data....

The Neural Simulation Language: A System for Brain Modeling
The Neural Simulation Language: A System for Brain Modeling
The Neural Simulation Language (NSL), developed by Alfredo Weitzenfeld, Michael Arbib, and Amanda Alexander, provides a simulation environment for modular brain modeling. NSL is an object-oriented language offering object-oriented protocols applicable to all levels of neural simulation. One of NSL's main strengths is that it allows for realistic...
An Introduction to 3D Computer Vision Techniques and Algorithms
An Introduction to 3D Computer Vision Techniques and Algorithms

Computer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. However using algorithms, it is...

Meta-Learning in Computational Intelligence (Studies in Computational Intelligence)
Meta-Learning in Computational Intelligence (Studies in Computational Intelligence)

In the early days of pattern recognition and statistical data analysis life was rather simple: datasets were relatively small, collected from well-designed experiments, analyzed using a few methods that had good theoretical background. Explosive growth of the use of computers led to the creation of huge amounts of data of all kinds,...

Stem Cells and Cancer Stem Cells, Vol. 1: Therapeutic Applications in Disease and Injury
Stem Cells and Cancer Stem Cells, Vol. 1: Therapeutic Applications in Disease and Injury

Difference between tissue specific stem cells and embryonic stem cells is explained. The advantages of the latter are included. The application of human pluripotent stem cells, mesenchymal stem cells, and hematopoietic stem cells in cancer therapy and tissue/organ regeneration is detailed. Role of neural cancer stem cells in brain tumors,...

Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Nowadays data accumulate at an alarming speed in various storage devices,
and so does valuable information. However, it is difficult to understand information
hidden in data without the aid of data analysis techniques, which
has provoked extensive interest in developing a field separate from machine
learning. This new field is
...
Advances in Data Mining and Modeling: Hong Kong 27 - 28 June 2002
Advances in Data Mining and Modeling: Hong Kong 27 - 28 June 2002

Data mining and data modeling are hot topics and are under fast development. Because of its wide applications and rich research contents, a lot of practitioners and academics are attracted to work on these areas. In the view of promoting the communications and collaborations among the practitioners and researchers in Hong Kong, a two-day...

Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras
Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

Build, scale, and deploy deep neural network models using the star libraries in Python

Key Features

  • Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras
  • Build, deploy, and scale end-to-end deep neural network models in a production...
Computational Intelligence: Engineering of Hybrid Systems
Computational Intelligence: Engineering of Hybrid Systems

Hybrid Intelligent Systems has become an important research topic in computer science and a key application field in science and engineering. This book offers a gentle introduction to the engineering aspects of hybrid intelligent systems, also emphasizing the interrelation with the main intelligent technologies such as genetic algorithms –...

Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning)
Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning)

We have been very pleased, beyond our expectations, with the reception of the first edition of this book. Bioinformatics, however, continues to evolve very rapidly, hence the need for a new edition. In the past three years, fullgenome sequencing has blossomed with the completion of the sequence of the fly and the first draft of the...

R Deep Learning Projects: Master the techniques to design and develop neural network models in R
R Deep Learning Projects: Master the techniques to design and develop neural network models in R

5 real-world projects to help you master deep learning concepts

Key Features

  • Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more
  • Get to grips with R's impressive range of...
unlimited object storage image
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