Building Neural Networks

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Artificial Intelligence With Python

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Author : Prateek Joshi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-01-27

Artificial Intelligence With Python written by Prateek Joshi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-27 with Computers categories.

Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.

Building Neural Networks From Scratch With Python

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Author : L D Knowings
language : en
Publisher:
Release Date : 2024-02-12

Building Neural Networks From Scratch With Python written by L D Knowings and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-12 with Computers categories.

Ready to throw your hat into the AI and machine learning ring? Get started right here, right now! Are you sick of these machine-learning guides that don't really teach you anything? Do you already know Python, but you're looking to expand your horizons and skills with the language? Do you want to dive into the amazing world of neural networks, but it just seems like it's... not for you? Artificial intelligence is progressing at a fantastic rate-every day, a new innovation hits the net, providing more and more opportunities for the advancement of society. In your everyday life, your job, and even in your passion projects, learning how to code a neural network can be game-changing. But it just seems... complicated. How do you learn everything that goes into such a complex topic without wanting to tear your own hair out? Well, it just got easier. Machine learning and neural networking don't have to be complicated-with the right resources, you can successfully code your very own neural network from scratch, minimal experience needed! In this all-encompassing guide to coding neural networks in Python, you'll uncover everything you need to go from zero to hero-transforming how you code and the scope of your knowledge right before your eyes. Here's just a portion of what you will discover in this guide: ● A comprehensive look at what a neural network is - including why you would use one and the benefits of including them in your repertoire ● All that pesky math dissuading you? Get right to the meat and potatoes of coding without all of those confusing equations getting you down ● Become a debugging master with these tips for handling code problems, maximizing your efficiency as a coder, and testing the data within your code ● Technological advancements galore! Learn how to keep up with all the latest trends in tech-and why doing so is important to you ● What in the world are layers and gradients? Detailed explanations of complex topics that will demystify neural networks, once and for all ● Dealing with underfitting, overfitting, and other oversights that many other resources overlook ● Several beginner-friendly neural network projects to put your newfound knowledge to the test And much more. Imagine a world where machine learning is more accessible, where neural networks and other complex topics are available to people just like you-people with a passion. Allowing for such technological advancements is going to truly change our world. It might seem hard, and you might be concerned based on other resources you've browsed-but this isn't an opportunity you can pass up on! By the end of this book, you'll have mastered neural networks confidently!

Deep Learning With Pytorch

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Author : Luca Pietro Giovanni Antiga
language : en
Publisher: Simon and Schuster
Release Date : 2020-07-01

Deep Learning With Pytorch written by Luca Pietro Giovanni Antiga and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-01 with Computers categories.

“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production

Neural Network Design

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Author : Martin T. Hagan
language : en
Publisher:
Release Date : 2003

Neural Network Design written by Martin T. Hagan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Neural networks (Computer science) categories.

Deep Learning For Natural Language Processing

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Author : Palash Goyal
language : en
Publisher: Apress
Release Date : 2018-06-26

Deep Learning For Natural Language Processing written by Palash Goyal and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-26 with Computers categories.

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.

Python Deep Learning Projects

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Author : Matthew Lamons
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-10-31

Python Deep Learning Projects written by Matthew Lamons and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Computers categories.

Insightful projects to master deep learning and neural network architectures using Python and Keras Key FeaturesExplore deep learning across computer vision, natural language processing (NLP), and image processingDiscover best practices for the training of deep neural networks and their deploymentAccess popular deep learning models as well as widely used neural network architecturesBook Description Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way What you will learnSet up a deep learning development environment on Amazon Web Services (AWS)Apply GPU-powered instances as well as the deep learning AMIImplement seq-to-seq networks for modeling natural language processing (NLP)Develop an end-to-end speech recognition systemBuild a system for pixel-wise semantic labeling of an imageCreate a system that generates images and their regionsWho this book is for Python Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects. It is assumed that you have sound knowledge of Python programming

Multivariate Statistical Machine Learning Methods For Genomic Prediction

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Author : Osval Antonio Montesinos López
language : en
Publisher: Springer Nature
Release Date : 2022-02-14

Multivariate Statistical Machine Learning Methods For Genomic Prediction written by Osval Antonio Montesinos López and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-14 with Technology & Engineering categories.

This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Neurocomputing

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Author : Francoise Fogelman Soulie
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Neurocomputing written by Francoise Fogelman Soulie and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.

This volume contains the collected papers of the NATO Conference on Neurocomputing, held in Les Arcs in February 1989. For many of us, this conference was reminiscent of another NATO Conference, in 1985, on Disordered Systems [1], which was the first conference on neural nets to be held in France. To some of the participants that conference opened, in a way, the field of neurocomputing (somewhat exotic at that time!) and also allowed for many future fruitful contacts. Since then, the field of neurocomputing has very much evolved and its audience has increased so widely that meetings in the US have often gathered more than 2000 participants. However, the NATO workshops have a distinct atmosphere of free discussions and time for exchange, and so, in 1988, we decided to go for another session. This was an ~casion for me and some of the early birds of the 1985 conference to realize how much, and how little too, the field had matured.

Exploring Neural Networks With C

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Author : Ryszard Tadeusiewicz
language : en
Publisher: CRC Press
Release Date : 2014-09-02

Exploring Neural Networks With C written by Ryszard Tadeusiewicz and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-02 with Computers categories.

The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations—making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical. Exploring Neural Networks with C# presents the important properties of neural networks—while keeping the complex mathematics to a minimum. Explaining how to build and use neural networks, it presents complicated information about neural networks structure, functioning, and learning in a manner that is easy to understand. Taking a "learn by doing" approach, the book is filled with illustrations to guide you through the mystery of neural networks. Examples of experiments are provided in the text to encourage individual research. Online access to C# programs is also provided to help you discover the properties of neural networks. Following the procedures and using the programs included with the book will allow you to learn how to work with neural networks and evaluate your progress. You can download the programs as both executable applications and C# source code from http://home.agh.edu.pl/~tad//index.php?page=programy&lang=en

Developers Guide For Building Own Neural Network Library

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Author : Venish Patidar
language : en
Publisher: Venish Patidar
Release Date : 2022-10-01

Developers Guide For Building Own Neural Network Library written by Venish Patidar and has been published by Venish Patidar this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-01 with Computers categories.

This book contains everything that a curious mind seeks more. The book sets the seeker on the mathematical journey, which starts with the biological neuron and a network and ends by creating your own. The book will gently introduce the concept of imitation and the roots of neural networks. Firstly, it will introduce the working of a biological neuron, followed by the analogies with the artificial neurons. After an overview of those, the book will shift the gears from biology to mathematics. We will find our way of creating the neural network library with the help of mathematics and developing the code alongside the section.