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Introduction To Deep Learning And Neural Networks With Python

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Introduction to Deep Learning and Neural Networks with PythonTM

Introduction to Deep Learning and Neural Networks with PythonTM Book
Author : Ahmed Fawzy Gad,Fatima Ezzahra Jarmouni
Publisher : Academic Press
Release : 2020-12-07
ISBN : 0323909345
Language : En, Es, Fr & De

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Book Description :

Introduction to Deep Learning and Neural Networks with PythonTM: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonTM code examples to clarify neural network calculations, by book’s end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonTM examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. Examines the practical side of deep learning and neural networks Provides a problem-based approach to building artificial neural networks using real data Describes PythonTM functions and features for neuroscientists Uses a careful tutorial approach to describe implementation of neural networks in PythonTM Features math and code examples (via companion website) with helpful instructions for easy implementation

Introduction to Deep Learning Using PyTorch

Introduction to Deep Learning Using PyTorch Book
Author : Goku Mohandas
Publisher : Unknown
Release : 2018
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

"This video will serve as an introduction to PyTorch, a dynamic, deep learning framework in Python. In this video, you will learn to create simple neural networks, which are the backbone of artificial intelligence. We will start with fundamental concepts of deep learning (including feed forward networks, back-propagation, loss functions, etc.) and then dive into using PyTorch tensors to easily create our networks. Finally, we will CUDA render our code in order to be GPU-compatible for even faster model training."--Resource description page.

Deep Learning with Python

Deep Learning with Python Book
Author : Chao Pan
Publisher : Createspace Independent Publishing Platform
Release : 2016-06-14
ISBN : 9781721250974
Language : En, Es, Fr & De

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Book Description :

***** BUY NOW (will soon return to 24.77 $) *****Are you thinking of learning deep Learning using Python? (For Beginners Only) If you are looking for a beginners guide to learn deep learning, in just a few hours, this book is for you. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses.To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach, which would lead to better mental representations.Step-by-Step Guide and Visual Illustrations and ExamplesThis book and the accompanying examples, you would be well suited to tackle problems, which pique your interests using machine learning and deep learning models. Book Objectives This book will help you: Have an appreciation for deep learning and an understanding of their fundamental principles. Have an elementary grasp of deep learning concepts and algorithms. Have achieved a technical background in deep learning and neural networks using Python. Target UsersThe book designed for a variety of target audiences. Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of machine learning. Seasoned professionals in the field of artificial intelligence and deep learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Introduction What is Artificial Intelligence, Machine Learning and Deep Learning? Mathematical Foundations of Deep Learning Understanding Machine Learning Models Evaluation of Machine Learning Models: Overfitting, Underfitting, Bias Variance Tradeoff Fully Connected Neural Networks Convolutional Neural Networks Recurrent Neural Networks Generative Adversarial Networks Deep Reinforcement Learning Introduction to Deep Neural Networks with Keras A First Look at Neural Networks in Keras Introduction to Pytorch The Pytorch Deep Learning Framework Your First Neural Network in Pytorch Deep Learning for Computer Vision Build a Convolutional Neural Network Deep Learning for Natural Language Processing Working with Sequential Data Build a Recurrent Neural Network Frequently Asked Questions Q: Is this book for me and do I need programming experience?A: if you want to smash Deep Learning from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Can I have a refund if this book doesn't fit for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email.***** MONEY BACK GUARANTEE BY AMAZON ***** Editorial Reviews"This is an excellent book, it is a very good introduction to deep learning and neural networks. The concepts and terminology are clearly explained. The book also points out several good locations on the internet where users can obtain more information. I was extremely happy with this book and I recommend it for all beginners" - Prof. Alain Simon, EDHEC Business School. Statistician and DataScientist.

Introduction to Deep Learning

Introduction to Deep Learning Book
Author : Eugene Charniak
Publisher : MIT Press
Release : 2019-02-19
ISBN : 0262351641
Language : En, Es, Fr & De

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Book Description :

A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.

Introduction to Deep Learning and Neural Networks with PythonT

Introduction to Deep Learning and Neural Networks with PythonT Book
Author : Ahmed Fawzy Gad,Fatima Ezzahra Jarmouni
Publisher : Academic Press
Release : 2020-12-10
ISBN : 0323909337
Language : En, Es, Fr & De

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Book Description :

Introduction to Deep Learning and Neural Networks with PythonT: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonT code examples to clarify neural network calculations, by book's end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonT examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. Examines the practical side of deep learning and neural networks Provides a problem-based approach to building artificial neural networks using real data Describes PythonT functions and features for neuroscientists Uses a careful tutorial approach to describe implementation of neural networks in PythonT Features math and code examples (via companion website) with helpful instructions for easy implementation

Practical Deep Learning with Python

Practical Deep Learning with Python Book
Author : Ron Kneusel
Publisher : No Starch Press
Release : 2021-01-28
ISBN : 1718500742
Language : En, Es, Fr & De

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Book Description :

"An introduction to machine learning and deep learning for beginners. Covers fundamental concepts before presenting classic machine learning models, neural networks, and modern convolutional neural networks. Includes hands-on Python experiments for each model"--

Introduction to Deep Learning Black White Version

Introduction to Deep Learning  Black White Version  Book
Author : Prof Dr Juergen Brauer
Publisher : Unknown
Release : 2018-08-19
ISBN : 9781725894051
Language : En, Es, Fr & De

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Book Description :

About the book: In Computer Sciences there is currently a gold rush mood due to a new field called "Deep Learning". But what is Deep Learning? This book is an introduction to Neural Networks and the most important Deep Learning model - the Convolutional Neural Network model including a description of tricks that can be used to train such models more quickly. We start with the biological role model: the Neuron. About 86.000.000.000 of these simple processing elements are in your brain! And they all work in parallel! We discuss how to model the operation of a biological neuron with technical neuron models and then consider the first simple single-layer network of technical neurons. We then introduce the Multi-Layer Perceptron (MLP) and the Convolutional Neural Network (CNN) model which uses the MLP at its end. At the end of the book we discuss promising new directions for the field of Deep Learning. A famous physicist once said: "What I cannot create, I do not understand". For this, the book is full of examples of how to program all models discussed in Python and TensorFlow - Today, the most important Deep Learning library. About the author: Prof. Dr.-Ing. Juergen Brauer is a professor for Sensor Data Processing and Programming at the University of Applied Sciences Kempten in Germany where he holds a "Deep Learning" and other machine learning related lectures for Computer Science and Advanced Driver Assistance Systems students. His personal experience tells him: "What I cannot program, I do not understand". Note: This is a black/white version of the original book version which uses colors in some figures. It allows for lower printing costs and therefore a lower final price. However, some details might be recognized better in the color version.

Deep Learning Fundamentals

Deep Learning Fundamentals Book
Author : Chao Pan
Publisher : Createspace Independent Publishing Platform
Release : 2016-06-15
ISBN : 9781721230884
Language : En, Es, Fr & De

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Book Description :

This book is the first part of the book deep learning with Python write by the same author. If you already purchased deep learning with Python by Chao Pan no need for this book. Are you thinking of learning deep Learning fundamentals, concepts and algorithms? (For Beginners) If you are looking for a complete beginners guide to learn deep learning with examples, in just a few hours, this book is for you. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses.To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples This book and the accompanying examples, you would be well suited to tackle problems, which pique your interests using machine learning and deep learning models. Instead of tough math formulas, this book contains several graphs and images. Book Objectives Have an appreciation for deep learning and an understanding of their fundamental principles. Have an elementary grasp of deep learning concepts and algorithms. Have achieved a technical background in deep learning and neural networks. Target Users The most suitable users would include: Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of machine learning. Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Introduction Teaching Approach What is Artificial Intelligence, Machine Learning and Deep Learning? Mathematical Foundations of Deep Learning Machine Learning Fundamentals Fully Connected Neural Networks Convolutional Neural Networks Recurrent Neural Networks Generative Adversarial Networks Deep Reinforcement Learning Introduction to Deep Neural Networks with Keras Sources & References Frequently Asked Questions Q: Is this book for me and do I need programming experience?A: if you want to smash deep learning from scratch, this book is for you. No programming experience is required. The present only the fundamentals concepts and algorithms of deep learning. It ll be a good introduction for beginners.Q: Can I loan this book to friends?A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days.Q: Does this book include everything I need to become a Machine Learning expert?A: Unfortunately, no. This book is designed for readers taking their first steps in Deep Learning and further learning will be required beyond this book to master all aspects.Q: Can I have a refund if this book is not fitted for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected]

Python Deep Learning Develop Your First Neural Network in Python Using Tensorflow Keras and Pytorch

Python Deep Learning  Develop Your First Neural Network in Python Using Tensorflow  Keras  and Pytorch Book
Author : Samuel Burns
Publisher : Step-By-Step Tutorial for Begi
Release : 2019-04-03
ISBN : 9781092562225
Language : En, Es, Fr & De

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Book Description :

Build your Own Neural Network today. Through easy-to-follow instruction and examples, you'll learn the fundamentals of Deep learning and build your very own Neural Network in Python using TensorFlow, Keras, PyTorch, and Theano. While you have the option of spending thousands of dollars on big and boring textbooks, we recommend getting the same pieces of information for a fraction of the cost. So Get Your Copy Now!! Why this book? Book ObjectivesThe following are the objectives of this book: To help you understand deep learning in detail To help you know how to get started with deep learning in Python by setting up the coding environment. To help you transition from a deep learning Beginner to a Professional. To help you learn how to develop a complete and functional artificial neural network model in Python on your own. Who this Book is for? The author targets the following groups of people: Anybody who is a complete beginner to deep learning with Python. Anybody in need of advancing their Python for deep learning skills. Professors, lecturers or tutors who are looking to find better ways to explain Deep Learning to their students in the simplest and easiest way. Students and academicians, especially those focusing on python programming, neural networks, machine learning, and deep learning. What do you need for this Book? You are required to have installed the following on your computer: Python 3.X. TensorFlow . Keras . PyTorch The Author guides you on how to install the rest of the Python libraries that are required for deep learning.The author will guide you on how to install and configure the rest. What is inside the book? What is Deep Learning? An Overview of Artificial Neural Networks. Exploring the Libraries. Installation and Setup. TensorFlow Basics. Deep Learning with TensorFlow. Keras Basics. PyTorch Basics. Creating Convolutional Neural Networks with PyTorch. Creating Recurrent Neural Networks with PyTorch. From the back cover. Deep learning is part of machine learning methods based on learning data representations. This book written by Samuel Burns provides an excellent introduction to deep learning methods for computer vision applications. The author does not focus on too much math since this guide is designed for developers who are beginners in the field of deep learning. The book has been grouped into chapters, with each chapter exploring a different feature of the deep learning libraries that can be used in Python programming language. Each chapter features a unique Neural Network architecture including Convolutional Neural Networks. After reading this book, you will be able to build your own Neural Networks using Tenserflow, Keras, and PyTorch. Moreover, the author has provided Python codes, each code performing a different task. Corresponding explanations have also been provided alongside each piece of code to help the reader understand the meaning of the various lines of the code. In addition to this, screenshots showing the output that each code should return have been given. The author has used a simple language to make it easy even for beginners to understand.

Introduction to Deep Learning

Introduction to Deep Learning Book
Author : Juergen Brauer
Publisher : Createspace Independent Publishing Platform
Release : 2018-08-03
ISBN : 9781724716415
Language : En, Es, Fr & De

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Book Description :

About the book: In Computer Sciences there is currently a gold rush mood due to a new field called "Deep Learning".But what is Deep Learning? This book is an introduction to Neural Networks and the most important Deep Learning model - the Convolutional Neural Network model including a description of tricks that can be used to train such models more quickly.We start with the biological role model: the Neuron. About 86.000.000.000 of these simple processing elements are in your brain! And they all work in parallel! We discuss how to model the operation of a biological neuron with technical neuron models and then consider the first simple single-layer network of technical neurons. We then introduce the Multi-Layer Perceptron (MLP) and the Convolutional Neural Network (CNN) model which uses the MLP at its end. At the end of the book we discuss promising new directions for the field of Deep Learning.A famous physicist once said: "What I cannot create, I do not understand". For this, the book is full of examples of how to program all models discussed in Python and TensorFlow - Today, the most important Deep Learning library.About the author: Prof. Dr.-Ing. Juergen Brauer is a professor for Sensor Data Processing and Programming at the University of Applied Sciences Kempten in Germany where he holds a "Deep Learning" and other machine learning related lectures for Computer Science and Advanced Driver Assistance Systems students.His personal experience tells him: "What I cannot program, I do not understand".

Introduction to Deep Learning

Introduction to Deep Learning Book
Author : Sandro Skansi
Publisher : Springer
Release : 2018-02-04
ISBN : 3319730045
Language : En, Es, Fr & De

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Book Description :

This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.

Learn Keras for Deep Neural Networks

Learn Keras for Deep Neural Networks Book
Author : Jojo Moolayil
Publisher : Apress
Release : 2018-12-07
ISBN : 1484242408
Language : En, Es, Fr & De

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Book Description :

Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras. What You’ll Learn Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks Who This Book Is For Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.

Deep Introduction to Machine Learning

Deep Introduction to Machine Learning Book
Author : Aaron Nelson Ph D
Publisher : Unknown
Release : 2021-01-05
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition

Deep Learning from the Basics

Deep Learning from the Basics Book
Author : Koki Saitoh
Publisher : Packt Publishing Ltd
Release : 2021-03-08
ISBN : 180020972X
Language : En, Es, Fr & De

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Book Description :

Discover ways to implement various deep learning algorithms by leveraging Python and other technologies Key Features Learn deep learning models through several activities Begin with simple machine learning problems, and finish by building a complex system of your own Teach your machines to see by mastering the technologies required for image recognition Book Description Deep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us. Deep Learning from the Basics begins with a fast-paced introduction to deep learning with Python, its definition, characteristics, and applications. You'll learn how to use the Python interpreter and the script files in your applications, and utilize NumPy and Matplotlib in your deep learning models. As you progress through the book, you'll discover backpropagation—an efficient way to calculate the gradients of weight parameters—and study multilayer perceptrons and their limitations, before, finally, implementing a three-layer neural network and calculating multidimensional arrays. By the end of the book, you'll have the knowledge to apply the relevant technologies in deep learning. What you will learn Use Python with minimum external sources to implement deep learning programs Study the various deep learning and neural network theories Learn how to determine learning coefficients and the initial values of weights Implement trends such as Batch Normalization, Dropout, and Adam Explore applications like automatic driving, image generation, and reinforcement learning Who this book is for Deep Learning from the Basics is designed for data scientists, data analysts, and developers who want to use deep learning techniques to develop efficient solutions. This book is ideal for those who want a deeper understanding as well as an overview of the technologies. Some working knowledge of Python is a must. Knowledge of NumPy and pandas will be beneficial, but not essential.

Deep Learning with Python

Deep Learning with Python Book
Author : Nikhil Ketkar
Publisher : Apress
Release : 2017-04-18
ISBN : 1484227662
Language : En, Es, Fr & De

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Book Description :

Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.

Mastering Deep Learning Fundamentals

Mastering Deep Learning Fundamentals Book
Author : Ai Publishing
Publisher : AI Publishing
Release : 2019-06-09
ISBN : 9781733042628
Language : En, Es, Fr & De

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Book Description :

** ONE HOUR FREE VIDEO COURSE IN DEEP LEARNING INCLUDED** **Get your copy now, the price will change soon**You are interested in deep learning, but don't know how to get startedLet us help youWho are the book for? Are a college student and want more than your university course offers Are you a student interested in a career in Data science? Are you a programmer who wants to make a career switch into data science and AI? Are you an engineer who wants to use new data science techniques at your current job? Are you an entrepreneur who dreams of a data science but do not yet know the basics? Are you a hobbyist who wants to build cool data science projects? Are you a data scientist practitioner and want to broaden your area of expertise? If the answer to any of the above questions is a YES, this book is for you.We have designed this book for beginners in mind and our goal is to prepare students with practical skills to solve real-world problems and to stand out in the job market.This book are not for shallow learners who simply want to copy-paste code. This book will require your time and commitment.Our book is different from other books?If you are searching for a step by step guide to learn deep learning and AI from scratch or are interested in the current updates of the AI world, our book is just the right one for you. This book paves beginners' road towards the path of deep learning concepts and algorithms without any traditional complexity of mathematical formulas.With the help of graphs and images, this books is the easiest to comprehend even by those who have no previous technological knowledge of machine learning. Moreover, our book, with its comprehensive content, prepares the readers for higher advanced courses.We strive to provide world-class data science and AI education at reasonable prices. To achieve that, we have put in a lot of planning and efforts to provide a rich learning experience for the students.What's Inside This Book? Part I: Fundamentals of Deep learning Fundamentals of Probability Fundamentals of Statistics Fundamentals of Linear Algebra Introduction to Machine Learning and Deep Learning Fundamentals of Machine Learning Fundamentals of Neural Networks and Deep Learning Deep Learning Parameters and Hyper-parameters Deep Neural Networks Layers Deep Learning Activation Functions Deep Learning Loss Functions Deep Learning Optimization Algorithms Convolutional Neural Network Recurrent Neural Networks LSTM Recursive Neural Networks Bonus Course Conclusion Part II: Deep Learning in Practice (In Jupyter notebooks) Python for Beginners Python Data Structures Python Function Object Oriented Programming in Python Best practices in Python and Zen of Python Installing Python Numpy, Pandas, Matplotlib and Scikit-learn Evaluating a model's performance Keras and Tensorflow Deep learning workstation: Jupyter Notebooks and Getting Binary Classification Building Deep Learning Model Convolutional Neural Networks in Keras Data Preparation Model Building Training and Testing Deep learning for text and sequences Brief introduction to Google Colab Data Preparation Data Wrangling and Analysis Recurrent Neural Network (RNN) ** MONEY BACK GUARANTEE BY AMAZON **If you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform or contact us (our email inside the book).

Deep Learning with Python

Deep Learning with Python Book
Author : Benjamin Smith
Publisher : Independently Published
Release : 2020-08-24
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Are you looking to take your machine learning knowledge further?Want to understand what deep learning is all about?Then you've picked the right book. With "Deep learning with Python: Simple and Effective Tips and Tricks to Learn Deep Learning with Python," you will learn the basics of deep learning, what it is and what it is used for. In particular, we focus on using the Python programming language over any other, given that it is the most popular programming language for data sciences. You will learn: -What deep learning is and how it works-Different deep learning methods-What its limitations and challenges are-Why we prefer to use Python-The best Python libraries for deep learning-Overviews of classification, regression, and clustering tasks-Algorithms and activation functions-What neural nets are-Recurrent neural networks and LSTMs-Convolutional neural networks and image processing-An introduction to deep reinforcement learning-And much more!You'll also get the chance to build a deep learning model using TensorFlow and Keras.Are you excited to get started?Then scroll up, hit that "Buy Now" button and get started on the next level in your data science journey.

TensorFlow 2 x in the Colaboratory Cloud

TensorFlow 2 x in the Colaboratory Cloud Book
Author : David Paper
Publisher : Apress
Release : 2021-02-04
ISBN : 9781484266489
Language : En, Es, Fr & De

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Book Description :

Use TensorFlow 2.x with Google's Colaboratory (Colab) product that offers a free cloud service for Python programmers. Colab is especially well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab’s default install of the most current TensorFlow 2.x along with Colab’s easy access to on-demand GPU hardware acceleration in the cloud for fast execution of deep learning models. This book offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything else—Python, TensorFlow 2.x, GPU support, and Jupyter Notebooks—is provided and ready to go from Colab. The book begins with an introduction to TensorFlow 2.x and the Google Colab cloud service. You will learn how to provision a workspace on Google Colab and build a simple neural network application. From there you will progress into TensorFlow datasets and building input pipelines in support of modeling and testing. You will find coverage of deep learning classification and regression, with clear code examples showing how to perform each of those functions. Advanced topics covered in the book include convolutional neural networks and recurrent neural networks. This book contains all the applied math and programming you need to master the content. Examples range from simple to relatively complex when necessary to ensure acquisition of appropriate deep learning concepts and constructs. Examples are carefully explained, concise, accurate, and complete to perfectly complement deep learning skill development. Care is taken to walk you through the foundational principles of deep learning through clear examples written in Python that you can try out and experiment with using Google Colab from the comfort of your own home or office. What You Will Learn Be familiar with the basic concepts and constructs of applied deep learning Create machine learning models with clean and reliable Python code Work with datasets common to deep learning applications Prepare data for TensorFlow consumption Take advantage of Google Colab’s built-in support for deep learning Execute deep learning experiments using a variety of neural network models Be able to mount Google Colab directly to your Google Drive account Visualize training versus test performance to see model fit Who This Book Is For Readers who want to learn the highly popular TensorFlow 2.x deep learning platform, those who wish to master deep learning fundamentals that are sometimes skipped over in the rush to be productive, and those looking to build competency with a modern cloud service tool such as Google Colab

Mastering Deep Learning Fundamentals with Python

Mastering Deep Learning Fundamentals with Python Book
Author : Richard Wilson
Publisher : Independently Published
Release : 2019-07-14
ISBN : 9781080537778
Language : En, Es, Fr & De

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Book Description :

★★Buy the Paperback Version of this Book and get the Kindle Book version for FREE ★★ Step into the fascinating world of data science.. You to participate in the revolution that brings artificial intelligence back to the heart of our society, thanks to data scientists. Data science consists in translating problems of any other nature into quantitative modeling problems, solved by processing algorithms. This book, designed for anyone wishing to learn Deep Learning. This book presents the main techniques: deep neural networks, able to model all kinds of data, convolution networks, able to classify images, segment them and discover the objects or people who are there, recurring networks, it contains sample code so that the reader can easily test and run the programs. On the program: Deep learning Neural Networks and Deep Learning Deep Learning Parameters and Hyper-parameters Deep Neural Networks Layers Deep Learning Activation Functions Convolutional Neural Network Python Data Structures Best practices in Python and Zen of Python Installing Python Python These are some of the topics covered in this book: fundamentals of deep learning fundamentals of probability fundamentals of statistics fundamentals of linear algebra introduction to machine learning and deep learning fundamentals of machine learning fundamentals of neural networks and deep learning deep learning parameters and hyper-parameters deep neural networks layers deep learning activation functions convolutional neural network Deep learning in practice (in jupyter notebooks) python data structures best practices in python and zen of python installing python The following are the objectives of this book: To help you understand deep learning in detail To help you know how to get started with deep learning in Python by setting up the coding environment. To help you transition from a deep learning Beginner to a Professional. To help you learn how to develop a complete and functional artificial neural network model in Python on your own. And more Get this book now to learn more about -- Deep learning in Python by setting up the coding environment.!

Convolutional Neural Networks In Python

Convolutional Neural Networks In Python Book
Author : Frank Millstein
Publisher : Frank Millstein
Release : 2020-07-06
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Convolutional Neural Networks in Python This book covers the basics behind Convolutional Neural Networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy to understand way. It is perfect for any beginner out there looking forward to learning more about this machine learning field. This book is all about how to use convolutional neural networks for various image, object and other common classification problems in Python. Here, we also take a deeper look into various Keras layer used for building CNNs we take a look at different activation functions and much more, which will eventually lead you to creating highly accurate models able of performing great task results on various image classification, object classification and other problems. Therefore, at the end of the book, you will have a better insight into this world, thus you will be more than prepared to deal with more complex and challenging tasks on your own. Here Is a Preview of What You’ll Learn In This Book… Convolutional neural networks structure How convolutional neural networks actually work Convolutional neural networks applications The importance of convolution operator Different convolutional neural networks layers and their importance Arrangement of spatial parameters How and when to use stride and zero-padding Method of parameter sharing Matrix multiplication and its importance Pooling and dense layers Introducing non-linearity relu activation function How to train your convolutional neural network models using backpropagation How and why to apply dropout CNN model training process How to build a convolutional neural network Generating predictions and calculating loss functions How to train and evaluate your MNIST classifier How to build a simple image classification CNN And much, much more! Get this book NOW and learn more about Convolutional Neural Networks in Python!