Download or read book Hands On Neuroevolution with Python written by Iaroslav Omelianenko and published by Packt Publishing Ltd. This book was released on 2019-12-24 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Increase the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolution Key FeaturesImplement neuroevolution algorithms to improve the performance of neural network architecturesUnderstand evolutionary algorithms and neuroevolution methods with real-world examplesLearn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulationsBook Description Neuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes. This book will give you comprehensive insights into essential neuroevolution concepts and equip you with the skills you need to apply neuroevolution-based algorithms to solve practical, real-world problems. You'll start with learning the key neuroevolution concepts and methods by writing code with Python. You'll also get hands-on experience with popular Python libraries and cover examples of classical reinforcement learning, path planning for autonomous agents, and developing agents to autonomously play Atari games. Next, you'll learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. Later, you'll understand how to apply neuroevolution strategies to existing neural network designs to improve training and inference performance. Finally, you'll gain clear insights into the topology of neural networks and how neuroevolution allows you to develop complex networks, starting with simple ones. By the end of this book, you will not only have explored existing neuroevolution-based algorithms, but also have the skills you need to apply them in your research and work assignments. What you will learnDiscover the most popular neuroevolution algorithms – NEAT, HyperNEAT, and ES-HyperNEATExplore how to implement neuroevolution-based algorithms in PythonGet up to speed with advanced visualization tools to examine evolved neural network graphsUnderstand how to examine the results of experiments and analyze algorithm performanceDelve into neuroevolution techniques to improve the performance of existing methodsApply deep neuroevolution to develop agents for playing Atari gamesWho this book is for This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking to implement neuroevolution algorithms from scratch. Working knowledge of the Python programming language and basic knowledge of deep learning and neural networks are mandatory.

Download or read book Hands On Genetic Algorithms with Python written by Eyal Wirsansky and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPy Key Features Explore the ins and outs of genetic algorithms with this fast-paced guide Implement tasks such as feature selection, search optimization, and cluster analysis using Python Solve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applications Book DescriptionGenetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence. After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications. By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains.What you will learn Understand how to use state-of-the-art Python tools to create genetic algorithm-based applications Use genetic algorithms to optimize functions and solve planning and scheduling problems Enhance the performance of machine learning models and optimize deep learning network architecture Apply genetic algorithms to reinforcement learning tasks using OpenAI Gym Explore how images can be reconstructed using a set of semi-transparent shapes Discover other bio-inspired techniques, such as genetic programming and particle swarm optimization Who this book is for This book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book.

Download or read book Hands On Reinforcement Learning for Games written by Micheal Lanham and published by Packt Publishing Ltd. This book was released on 2020-01-03 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key FeaturesGet to grips with the different reinforcement and DRL algorithms for game developmentLearn how to implement components such as artificial agents, map and level generation, and audio generationGain insights into cutting-edge RL research and understand how it is similar to artificial general researchBook Description With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python. Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games. By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications. What you will learnUnderstand how deep learning can be integrated into an RL agentExplore basic to advanced algorithms commonly used in game developmentBuild agents that can learn and solve problems in all types of environmentsTrain a Deep Q-Network (DQN) agent to solve the CartPole balancing problemDevelop game AI agents by understanding the mechanism behind complex AIIntegrate all the concepts learned into new projects or gaming agentsWho this book is for If you’re a game developer looking to implement AI techniques to build next-generation games from scratch, this book is for you. Machine learning and deep learning practitioners, and RL researchers who want to understand how to use self-learning agents in the game domain will also find this book useful. Knowledge of game development and Python programming experience are required.

Download or read book Hands On Python Deep Learning for the Web written by Anubhav Singh and published by Packt Publishing Ltd. This book was released on 2020-05-15 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use the power of deep learning with Python to build and deploy intelligent web applications Key FeaturesCreate next-generation intelligent web applications using Python libraries such as Flask and DjangoImplement deep learning algorithms and techniques for performing smart web automationIntegrate neural network architectures to create powerful full-stack web applicationsBook Description When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you'll cover the latest tools and technological practices that are being used to implement deep learning in web development using Python. Starting with the fundamentals of machine learning, you'll focus on DL and the basics of neural networks, including common variants such as convolutional neural networks (CNNs). You'll learn how to integrate them into websites with the frontends of different standard web tech stacks. The book then helps you gain practical experience of developing a deep learning-enabled web app using Python libraries such as Django and Flask by creating RESTful APIs for custom models. Later, you'll explore how to set up a cloud environment for deep learning-based web deployments on Google Cloud and Amazon Web Services (AWS). Next, you'll learn how to use Microsoft's intelligent Emotion API, which can detect a person's emotions through a picture of their face. You'll also get to grips with deploying real-world websites, in addition to learning how to secure websites using reCAPTCHA and Cloudflare. Finally, you'll use NLP to integrate a voice UX through Dialogflow on your web pages. By the end of this book, you'll have learned how to deploy intelligent web apps and websites with the help of effective tools and practices. What you will learnExplore deep learning models and implement them in your browserDesign a smart web-based client using Django and FlaskWork with different Python-based APIs for performing deep learning tasksImplement popular neural network models with TensorFlow.jsDesign and build deep web services on the cloud using deep learningGet familiar with the standard workflow of taking deep learning models into productionWho this book is for This deep learning book is for data scientists, machine learning practitioners, and deep learning engineers who are looking to perform deep learning techniques and methodologies on the web. You will also find this book useful if you’re a web developer who wants to implement smart techniques in the browser to make it more interactive. Working knowledge of the Python programming language and basic machine learning techniques will be beneficial.

Download or read book Handbook of Neuroevolution Through Erlang written by Gene I. Sher and published by Springer Science & Business Media. This book was released on 2012-11-06 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Neuroevolution Through Erlang presents both the theory behind, and the methodology of, developing a neuroevolutionary-based computational intelligence system using Erlang. With a foreword written by Joe Armstrong, this handbook offers an extensive tutorial for creating a state of the art Topology and Weight Evolving Artificial Neural Network (TWEANN) platform. In a step-by-step format, the reader is guided from a single simulated neuron to a complete system. By following these steps, the reader will be able to use novel technology to build a TWEANN system, which can be applied to Artificial Life simulation, and Forex trading. Because of Erlang’s architecture, it perfectly matches that of evolutionary and neurocomptational systems. As a programming language, it is a concurrent, message passing paradigm which allows the developers to make full use of the multi-core & multi-cpu systems. Handbook of Neuroevolution Through Erlang explains how to leverage Erlang’s features in the field of machine learning, and the system’s real world applications, ranging from algorithmic financial trading to artificial life and robotics.

Download or read book Neuroevolution written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-21 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Neuroevolution Neuroevolution, sometimes spelled neuro-evolution, is a form of artificial intelligence that generates artificial neural networks (ANN), parameters, and rules through the application of evolutionary algorithms. Neuroevolution is also spelled neuro-evolution. The most popular applications for this technique are found in evolutionary robotics, artificial life, and general game playing. The primary advantage is that neuroevolution may be applied to a wider variety of problems than supervised learning methods, which need a curriculum of accurate input-output pairings to function properly. Neuroevolution, on the other hand, needs nothing more than a measurement of how well a network performs at a given job. For instance, the result of a game can be easily measured even if the necessary strategies are not provided in the form of labeled examples. Neuroevolution is frequently utilized as a component of the reinforcement learning paradigm. It can be contrasted with conventional deep learning approaches, which make use of gradient descent on a neural network that possesses a fixed topology. Neuroevolution is frequently utilized as a component of the reinforcement learning paradigm. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Neuroevolution Chapter 2: Artificial neural network Chapter 3: Evolutionary algorithm Chapter 4: Genetic representation Chapter 5: Effective fitness Chapter 6: Neuroevolution of augmenting topologies Chapter 7: Recurrent neural network Chapter 8: Compositional pattern-producing network Chapter 9: HyperNEAT Chapter 10: Evolving intelligent system (II) Answering the public top questions about neuroevolution. (III) Real world examples for the usage of neuroevolution in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of neuroevolution. What Is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

Download or read book Proceedings of Ninth International Congress on Information and Communication Technology written by Xin-She Yang and published by Springer Nature. This book was released on with total page 647 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Nature of Code written by Daniel Shiffman and published by No Starch Press. This book was released on 2024-09-03 with total page 642 pages. Available in PDF, EPUB and Kindle. Book excerpt: All aboard The Coding Train! This beginner-friendly creative coding tutorial is designed to grow your skills in a fun, hands-on way as you build simulations of real-world phenomena with “The Coding Train” YouTube star Daniel Shiffman. What if you could re-create the awe-inspiring flocking patterns of birds or the hypnotic dance of fireflies—with code? For over a decade, The Nature of Code has empowered countless readers to do just that, bridging the gap between creative expression and programming. This innovative guide by Daniel Shiffman, creator of the beloved Coding Train, welcomes budding and seasoned programmers alike into a world where code meets playful creativity. This JavaScript-based edition of Shiffman’s groundbreaking work gently unfolds the mysteries of the natural world, turning complex topics like genetic algorithms, physics-based simulations, and neural networks into accessible and visually stunning creations. Embark on this extraordinary adventure with projects involving: A physics engine: Simulate the push and pull of gravitational attraction. Flocking birds: Choreograph the mesmerizing dance of a flock. Branching trees: Grow lifelike and organic tree structures. Neural networks: Craft intelligent systems that learn and adapt. Cellular automata: Uncover the magic of self-organizing patterns. Evolutionary algorithms: Play witness to natural selection in your code. Shiffman’s work has transformed thousands of curious minds into creators, breaking down barriers between science, art, and technology, and inviting readers to see code not just as a tool for tasks but as a canvas for boundless creativity. Whether you’re deciphering the elegant patterns of natural phenomena or crafting your own digital ecosystems, Shiffman’s guidance is sure to inform and inspire. The Nature of Code is not just about coding; it’s about looking at the natural world in a new way and letting its wonders inspire your next creation. Dive in and discover the joy of turning code into art—all while mastering coding fundamentals along the way. NOTE: All examples are written with p5.js, a JavaScript library for creative coding, and are available on the book's website.

Download or read book Information and Communication Technologies in Education Research and Industrial Applications written by Grigoris Antoniou and published by Springer Nature. This book was released on with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Download or read book Hands On Transfer Learning with Python written by Dipanjan Sarkar and published by Packt Publishing Ltd. This book was released on 2018-08-31 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex operations such as image captioning neural style transfer Book Description Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLP Who this book is for Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.

Download or read book Deep Learning Step by Step with Python written by N. Lewis and published by . This book was released on 2016-07-26 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finally! Deep Neural Networks Simplified with Python Deep Learning Step by Step with Python takes you on a gentle, fun and unhurried journey to building your own deep neural network models in Python. Using plain English, it offers an intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available to the data scientist for deep neural networks using Python. NO EXPERIENCE REQUIRED This book is designed to be accessible - I'm assuming you never did like linear algebra, don't want to see things derived, dislike complicated computer code, and you're here because you want to see deep neural networks explained in plain English, and try them out for yourself. It is so straightforward and easy to follow even your ten year old nephew (who dislikes math) can understand it! THIS BOOK IS FOR YOU IF YOU WANT: Explanations rather than mathematical derivation Real world applications that make sense. Illustrations to deepen your understanding. Worked examples in Python you can easily follow and immediately implement. Ideas you can actually use and try on your own data. QUICK AND EASY: Bestselling Data Scientist Dr. N.D Lewis shows you the shortcut up the steep steps to the very top. It's easier than you think. Through a simple to follow process you will learn how to build deep neural network models with Python. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful data science applications. YOU'LL LEARN HOW TO: Unleash the power of Deep Neural Networks for effective forecasting. Develop hands on solutions for binary classification. Design successful applications for multi-class problems. Master techniques for efficient model construction. Fine tune deep networks to boost, accelerate, and transform predictive performance. Build Deep Learning Models Faster! Everything you need to get started is contained within this book. Deep Learning Step by Step with Python is your very own hands on practical, tactical, easy to follow guide to mastery Buy this book today your next big breakthrough using deep neural networks is only a page away!

Download or read book Hands On Genetic Algorithms with Python written by Eyal Wirsansky and published by Packt Publishing Ltd. This book was released on 2024-07-12 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the ever-growing world of genetic algorithms to build and enhance AI applications involving search, optimization, machine learning, deep learning, NLP, and XAI using Python libraries Key Features Learn how to implement genetic algorithms using Python libraries DEAP, scikit-learn, and NumPy Take advantage of cloud computing technology to increase the performance of your solutions Discover bio-inspired algorithms such as particle swarm optimization (PSO) and NEAT Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by Eyal Wirsansky, a senior data scientist and AI researcher with over 25 years of experience and a research background in genetic algorithms and neural networks, Hands-On Genetic Algorithms with Python offers expert insights and practical knowledge to master genetic algorithms. After an introduction to genetic algorithms and their principles of operation, you’ll find out how they differ from traditional algorithms and the types of problems they can solve, followed by applying them to search and optimization tasks such as planning, scheduling, gaming, and analytics. As you progress, you’ll delve into explainable AI and apply genetic algorithms to AI to improve machine learning and deep learning models, as well as tackle reinforcement learning and NLP tasks. This updated second edition further expands on applying genetic algorithms to NLP and XAI and speeding up genetic algorithms with concurrency and cloud computing. You’ll also get to grips with the NEAT algorithm. The book concludes with an image reconstruction project and other related technologies for future applications. By the end of this book, you’ll have gained hands-on experience in applying genetic algorithms across a variety of fields, with emphasis on artificial intelligence with Python.What you will learn Use genetic algorithms to solve planning, scheduling, gaming, and analytics problems Create reinforcement learning, NLP, and explainable AI applications Enhance the performance of ML models and optimize deep learning architecture Deploy genetic algorithms using client-server architectures, enhancing scalability and computational efficiency Explore how images can be reconstructed using a set of semi-transparent shapes Delve into topics like elitism, niching, and multiplicity in genetic solutions to enhance optimization strategies and solution diversity Who this book is for If you’re a data scientist, software developer, AI enthusiast who wants to break into the world of genetic algorithms and apply them to real-world, intelligent applications as quickly as possible, this book is for you. Working knowledge of the Python programming language is required to get started with this book.

Download or read book Neural Networks with Python written by Mei Wong and published by GitforGits. This book was released on 2023-11-02 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Neural Networks with Python" serves as an introductory guide for those taking their first steps into neural network development with Python. It's tailored to assist beginners in understanding the foundational elements of neural networks and to provide them with the confidence to delve deeper into this intriguing area of machine learning. In this book, readers will embark on a learning journey, starting from the very basics of Python programming, progressing through essential concepts, and gradually building up to more complex neural network architectures. The book simplifies the learning process by using relatable examples and datasets, making the concepts accessible to everyone. You will be introduced to various neural network architectures such as Feedforward, Convolutional, and Recurrent Neural Networks, among others. Each type is explained in a clear and concise manner, with practical examples to illustrate their applications. The book emphasizes the real-world applications and practical aspects of neural network development, rather than just theoretical knowledge. Readers will also find guidance on how to troubleshoot and refine their neural network models. The goal is to equip you with a solid understanding of how to create efficient and effective neural networks, while also being mindful of the common challenges that may arise. By the end of your journey with this book, you will have a foundational understanding of neural networks within the Python ecosystem and be prepared to apply this knowledge to real-world scenarios. "Neural Networks with Python" aims to be your stepping stone into the vast world of machine learning, empowering you to build upon this knowledge and explore more advanced topics in the future. Key Learnings Master Python for machine learning, from setup to complex models. Gain flexibility with diverse neural network architectures for various problems. Hands-on experience in building, training, and fine-tuning neural networks. Learn strategic approaches for troubleshooting and optimizing neural models. Grasp advanced topics like autoencoders, capsule networks, and attention mechanisms. Acquire skills in crucial data preprocessing and augmentation techniques. Understand and apply optimization techniques and hyperparameter tuning. Implement an end-to-end machine learning project, from data to deployment. Table of Content Python, TensorFlow, and your First Neural Network Deep Dive into Feedforward Networks Convolutional Networks for Visual Tasks Recurrent Networks for Sequence Data Data Generation with GANs Transformers for Complex Tasks Autoencoders for Data Compression and Generation Capsule Networks

Download or read book Python Machine Learning written by James Deep and published by . This book was released on 2019-11-08 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you are ready to know the link between Python Programming and Machine Learning, then keep reading. The concept of Artificial Intelligence is regarded by many as the way of the future. It covers vast areas of study and complements almost every aspect of human life. The evolution of intelligent machines has been on the rise as experts try to outsmart each other in innovation. Many business models, health organizations, government units, and many more, have adopted one or two practices that incorporate automation into their daily activities. As part of Artificial Intelligence, Machine Learning has seen significant applications across significant sectors of the economies of the world. To date, almost every aspect of our life has elements of Machine Learning. From the phones we use to the retail stores we do our shopping at, the areas covered by Machine Learning applications are drastically increasing by the day. This technique has helped the developers of software to create high-tech apps that predict changes in the market, sort large amounts of data, and offer solutions to major real-world problems. As much the trends are evident on the ground, the theoretical perspective remains an isolated area to many. Scaling through Machine Learning requires some knowledge of programming. In general terms, you need a platform to gain an understanding of the topic and hone your skills in the same. Python Machine Learning covers the concept of Machine Learning, in a detailed but well elaborate language of presentation. The topic may not be simple but is very worthwhile as long as you understand the fundamental concepts that underlie Machine Learning. Inside this book you will find Types of ML Use of Python in Machine Learning Essential Libraries for ML Regression Analysis Decision Trees The Perceptron Random Forest Algorithms K-Nearest Neighbors (KNN) ...and many more amazing and interesting topics! This book takes readers on a knowledge trip through solved examples, tips, tricks, and visualized content. It will not only create an appetite for more but also give readers what they need to know about Machine Learning, all these in a small volume for easy reading. Want to know more? Scroll to the top of the page and click the "buy now" button!

Download or read book Deep Learning with Python written by Nikhil Ketkar and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Hands on Data Science for Biologists Using Python written by Yasha Hasija and published by CRC Press. This book was released on 2021-04-08 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hands-on Data Science for Biologists using Python has been conceptualized to address the massive data handling needs of modern-day biologists. With the advent of high throughput technologies and consequent availability of omics data, biological science has become a data-intensive field. This hands-on textbook has been written with the inception of easing data analysis by providing an interactive, problem-based instructional approach in Python programming language. The book starts with an introduction to Python and steadily delves into scrupulous techniques of data handling, preprocessing, and visualization. The book concludes with machine learning algorithms and their applications in biological data science. Each topic has an intuitive explanation of concepts and is accompanied with biological examples. Features of this book: The book contains standard templates for data analysis using Python, suitable for beginners as well as advanced learners. This book shows working implementations of data handling and machine learning algorithms using real-life biological datasets and problems, such as gene expression analysis; disease prediction; image recognition; SNP association with phenotypes and diseases. Considering the importance of visualization for data interpretation, especially in biological systems, there is a dedicated chapter for the ease of data visualization and plotting. Every chapter is designed to be interactive and is accompanied with Jupyter notebook to prompt readers to practice in their local systems. Other avant-garde component of the book is the inclusion of a machine learning project, wherein various machine learning algorithms are applied for the identification of genes associated with age-related disorders. A systematic understanding of data analysis steps has always been an important element for biological research. This book is a readily accessible resource that can be used as a handbook for data analysis, as well as a platter of standard code templates for building models.