This book is dedicated to all the machine learning and deep learning enthusiasts, data scientists, researchers, and even students who want to perform more accurate, fast machine learning operations with TensorFlow. Those with basic knowledge of programming (Python and C/C++) and math concepts who want to be introduced to the topics of machine learning will find this book useful.What You Will Learn
Google's TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks.
This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you'll gain familiarity with the framework and perform the mathematical operations required for data analysis. As you progress further, you'll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical examples.
By the end of this book, you'll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application.Style and approach
Get quickly up and running with TensorFlow using this fast-paced guide. You will get to know everything that can be done with TensorFlow and we'll show you how to implement it in your environment. The examples in the book are from the core of the computation industry—something you can connect to and will find familiar.
Giancarlo Zaccone has more than 10 years of experience managing research projects in both the scientific and industrial domains. He worked as researcher at the C.N.R, the National Research Council, where he was involved in projects related to parallel numerical computing and scientific visualization. Currently, he is a senior software engineer at a consulting company developing and maintaining software systems for space and defence applications. Giancarlo holds a master's degree in physics from the Federico II of Naples and a 2nd level postgraduate master course in scientific computing from La Sapienza of Rome. He has already been a Packt author for the following book: Python Parallel Programming Cookbook. You can contact him at https://it.linkedin.com/in/giancarlozaccone
Python Parallel Programming Cookbook is intended for software developers who are well versed with Python and want to use parallel programming techniques to write powerful and efficient code. This book will help you master the basics and the advanced of parallel computing.What You Will LearnSynchronize multiple threads and processes to manage parallel tasksImplement message passing communication between processes to build parallel applicationsProgram your own GPU cards to address complex problemsManage computing entities to execute distributed computational tasksWrite efficient programs by adopting the event-driven programming modelExplore the cloud technology with DJango and Google App EngineApply parallel programming techniques that can lead to performance improvementsIn Detail
Parallel programming techniques are required for a developer to get the best use of all the computational resources available today and to build efficient software systems. From multi-core to GPU systems up to the distributed architectures, the high computation of programs throughout requires the use of programming tools and software libraries. Because of this, it is becoming increasingly important to know what the parallel programming techniques are. Python is commonly used as even non-experts can easily deal with its concepts.
This book will teach you parallel programming techniques using examples in Python and will help you explore the many ways in which you can write code that allows more than one process to happen at once. Starting with introducing you to the world of parallel computing, it moves on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool.
Next you will be taught about process-based parallelism where you will synchronize processes using message passing along with learning about the performance of MPI Python Modules. You will then go on to learn the asynchronous parallel programming model using the Python asyncio module along with handling exceptions. Moving on, you will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker.
You will also understand the StarCluster framework, Pycsp, Scoop, and Disco modules in Python. Further on, you will learn GPU programming with Python using the PyCUDA module along with evaluating performance limitations. Next you will get acquainted with the cloud computing concepts in Python, using Google App Engine (GAE), and building your first application with GAE. Lastly, you will learn about grid computing concepts in Python and using PyGlobus toolkit, GFTP and GASS COPY to transfer files, and service monitoring in PyGlobus.Style and approach
A step-by-step guide to parallel programming using Python, with recipes accompanied by one or more programming examples. It is a practically oriented book and has all the necessary underlying parallel computing concepts.
This book is ideal for data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. Intermediate and advanced machine learning implementers who need a quick guide they can easily navigate will find it useful.What You Will LearnBecome familiar with the basics of the TensorFlow machine learning libraryGet to know Linear Regression techniques with TensorFlowLearn SVMs with hands-on recipesImplement neural networks and improve predictionsApply NLP and sentiment analysis to your dataMaster CNN and RNN through practical recipesTake TensorFlow into productionIn Detail
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow.
This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.
Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.Style and approach
This book takes a recipe-based approach where every topic is explicated with the help of a real-world example.
Python programmers and data scientists - put your skills to the test with this practical guide dedicated to real-world machine learning that makes a real impact.What You Will LearnExplore and use Python's impressive machine learning ecosystemSuccessfully evaluate and apply the most effective models to problemsLearn the fundamentals of NLP - and put them into practiceVisualize data for maximum impact and clarityDeploy machine learning models using third party APIsGet to grips with feature engineeringIn Detail
Machine Learning is transforming the way we understand and interact with the world around us. But how much do you really understand it? How confident are you interacting with the tools and models that drive it?
Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through the development of some awesome machine learning applications and algorithms with real-world examples that demonstrate how to put concepts into practice.
You'll learn how to use cluster techniques to discover bargain air fares, and apply linear regression to find yourself a cheap apartment – and much more. Everything you learn is backed by a real-world example, whether its data manipulation or statistical modelling.
That way you're never left floundering in theory – you'll be simply collecting and analyzing data in a way that makes a real impact.Style and approach
Packed with real-world projects, this book takes you beyond the theory to demonstrate how to apply machine learning techniques to real problems.
This book is for data analysts, data scientists, and researchers who want to increase the speed and efficiency of their machine learning activities and results. Anyone looking for a fresh guide to complex numerical computations with TensorFlow will find this an extremely helpful resource. This book is also for developers who want to implement TensorFlow in production in various scenarios. Some experience with C++ and Python is expected.What You Will LearnLoad, interact, dissect, process, and save complex datasetsSolve classification and regression problems using state of the art techniques Predict the outcome of a simple time series using Linear Regression modelingUse a Logistic Regression scheme to predict the future result of a time seriesClassify images using deep neural network schemesTag a set of images and detect features using a deep neural network, including a Convolutional Neural Network (CNN) layerResolve character recognition problems using the Recurrent Neural Network (RNN) modelIn Detail
This book of projects highlights how TensorFlow can be used in different scenarios - this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with Tensors. Simply pick a project that is in line with your environment and get stacks of information on how to implement TensorFlow in production.Style and approach
This book is a practical guide to implementing TensorFlow in production. It explores various scenarios in which you could use TensorFlow and shows you how to use it in the context of real world projects. This will not only give you an upper hand in the field, but shows the potential for innovative uses of TensorFlow in your environment. This guide opens the door to second generation machine learning and numerical computation – a must-have for your bookshelf!
Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks.
This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries.
Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way.
You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.What you will learn Apply deep machine intelligence and GPU computing with TensorFlow Access public datasets and use TensorFlow to load, process, and transform the data Discover how to use the high-level TensorFlow API to build more powerful applications Use deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applicationsWho this book is for
The book is for people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.