Step-by-step guide to learn and solve complex computational problems with Nature Inspired algorithms.

Key Features

Artificial Neural Networks

Deep Learning models using Keras

Quantum Computers and Programming

Genetic Algorithms, CNN and RNNs

Swarm Intelligence Systems

Reinforcement Learning using OpenAI

Artificial Life

DNA computing

Fractals

Description
Natural Computing is the field of research inspired by nature, that allows the development of new algorithms to solve complex problems, leads to the synthesis of natural models, and may result in the design of new computing systems. This book exactly aims to educate you with practical examples on topics of importance associated with research field of Natural computing.

The initial few chapters will quickly walk you through Neural Networks while describing deep learning architectures such as CNN, RNN and AutoEncoders using Keras. As you progress further, youll gain understanding to develop genetic algorithm to solve traveling saleman problem, implement swarm intelligence techniques using the SwarmPackagePy and Cellular Automata techniques such as Game of Life, Langton's ant, etc.

The latter half of the book will introduce you to the world of Fractals such as such as the Cantor Set and the Mandelbrot Set, develop a quantum program with the QiSkit tool that runs on a real quantum computing platform, namely the IBM Q Machine and a Python simulation of the Adleman experiment that showed for the first time the possibility of performing computations at the molecular level.

What You Will Learn

Mastering Artificial Neural Networks

Developing Artificial Intelligence systems

Resolving complex problems with Genetic Programming and Swarm intelligence algorithms

Programming Quantum Computers

Exploring the mathematical world of fractals

Simulating complex systems by Cellular Automata

Understanding the basics of DNA computation

Who This Book Is For
This book is for all science enthusiasts, in particular who want to understand what are the links between computer sciences and natural systems. Interested readers should have good skills in math and python programming along with some basic knowledge of physics and biology. . Although, some knowledge of the topics covered in the book will be helpful, it is not essential to have worked with the tools covered in the book.

Table of Contents

Neural Networks

Deep Learning

Genetic Programming

Swarm Intelligence

Cellular Automata

Fractals

Quantum Computing

DNA Computing

About the Author
Giancarlo Zaccone has over ten years of experience in managing research projects in scientific and industrial areas.
He is a Software and Systems Engineer Consultant at European Space Agency (ESTEC).
Giancarlo holds a masters degree in Physics and an advanced masters degree in Scientific Computing at La Sapienza of Rome.

His LinkedIn Profile: https://www.linkedin.com/in/giancarlozaccone/

 

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Spis treści

Natural Computing with Python eBook -- spis treści

• Cover
• Natural Computing with Python
• Copyright
• About the Author
• Preface
• acknowledgement
• Errata
• Table of Contents
• 1. Neural Networks
• Introduction
• Structure
• Perceptron
• Developing logic gates by perceptron
• Activation functions
• Linear and non-linear models
• Step function
• Sigmoid function
• ReLU function
• Sigmoid neuron
• How neural networks learn
• Neural network architecture
• Supervised learning
• Gradient descent
• MLP Python implementation
• Feedforward step
• Backpropagation
• TensorFlow
• Installation
• Flow graph
• Placeholders
• Logistic regression
• MNIST dataset
• Flow graph definition
• Training
• Evaluation
• Conclusion
• Sitography
• Python
• Neural networks
• Machine learning
• TensorFlow
• 2. Deep Learning
• Structure
• What is deep learning?
• Keras deep learning framework
• Keras tutorial
• Convolutional Neural Networks (CNNs)
• Convolution layers
• Pooling layers
• ReLU layers
• Fully connected layers
• Upsampling layers
• Loss layers
• CNN implementation
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM)
• Sentiment Analysis for IMDB movie review
• Autoencoders
• Why copy input to output?
• Use of autoencoders
• Developing autoencoders
• Reinforcement learning
• Application areas
• Elements of reinforcement learning
• Q-learning
• Solving the CartPole problem
• Conclusion
• Sitography
• Deep learning
• CNNs
• RNNs
• Autoencoders
• Reinforcement learning
• Keras
• 3. Genetic Algorithms and Programming
• Structure
• Evolution and algorithms
• Optimization problems
• Basic terminology
• Genetic algorithms
• Population
• Fitness
• Genetic operators
• Python implementation
• Travelling salesman problem (TSP)
• Genetic programming
• Terminal set and function set
• Genetic operations
• Symbolic regression problem using gplearn .110
• Conclusion
• Sitography
• Genetic algorithms
• Genetic programming
• Python frameworks
• 4. Swarm Intelligence
• Introduction
• Structure
• Mechanisms underlying collective behavior
• Pheromones
• Stigmergy
• Stigmergy and collective behaviour
• Ant colony optimisation (ACO)
• ACO implementation
• Particle swarm optimization
• PSO implementation
• SwarmPackagePy framework
• Requirements
• Installation
• Artificial Bee Algorithm
• Method invocation
• Example
• Conclusion
• Sitography
• Swarm intelligence
• TSP problem
• Particle swarm optimization
• Ant Colony Optimization
• SwarmPackagePy frameworks
• 5. Cellular Automata
• Introduction
• Structure
• Background history
• Automata
• Turing machines
• Cellular automata
• SierpiÅ„ski triangle
• Game of Life
• Langtons ant
• Wolframs cellular automata
• Implementation
• CellPyLib
• Rule 110
• Reversibility and entropy
• Sitography
• Cellular automata
• Turing machines
• Game of Life
• Langtons ant
• Wolfram automata
• 6. Fractals
• Introduction
• Structure
• What are fractals?
• Self-similarity
• Fine structure
• Fractional dimensions
• Recursion
• Python and recursion
• Fractal dimension
• Cantor set
• Sierpinskis fractals
• Complex numbers
• Python and complex numbers
• Mandelbrot set
• Fractals and nature
• LS-Systems
• Conclusion
• Sitography
• Fractals
• Mandelbrot
• Fractals and nature
• 7. Quantum Computing
• Introduction
• Structure
• Quantum computers
• Qubits
• Quantum gates
• Quantum programming
• Qiskit
• Programming workflow
• Building a quantum circuit
• Executing the quantum model
• QASM backend
• Quantum circuits
• Quantum gates
• X gate
• H gate
• Running Qiskit on IBM Q devices
• Create a free IBM Q account to get an API token
• Running on IBM Q devices
• Applications of quantum computing
• Conclusion
• Sitography
• Quantum mechanics
• Quantum computing
• Quantum programming
• Quantum computers
• Python frameworks
• 8. DNA Computing
• Introduction
• Structure
• The idea behind DNA computing
• DNA fundamentals
• Basics of DNA computing
• How to manipulate DNA
• Phases of DNA algorithms
• Adleman model for DNA computing
• Adlemans biological approach
• Python simulation of Adlemans experiment
• Conclusion
• Sitography
• DNA computing
• Adlemans experiment
• Python frameworks
• Index