03424nam 2200397 450 991080990870332120190509070315.01-78953-696-0(CKB)4100000007878189(MiAaPQ)EBC5744479(CaSebORM)9781789530759(PPN)236074830(EXLCZ)99410000000787818920190423d2019 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierTensorflow 2. 0 quick start guide get up to speed with the newly introduced features of tensorflow 2.0 /Tony Holdroyd1st editionBirmingham, England ;Mumbai :Packt,2019.1 online resource (185 pages)1-78953-075-X Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks. Key Features Train your own models for effective prediction, using high-level Keras API Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks Get acquainted with some new practices introduced in TensorFlow 2.0 Alpha Book Description TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks. After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains. By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques. What you will learn Use tf.Keras for fast prototyping, building, and training deep learning neural network models Easily convert your TensorFlow 1.12 applications to TensorFlow 2.0-compatible files Use TensorFlow to tackle traditional supervised and unsupervised machine learning applications Understand image recognition techniques using TensorFlow Perform neural style transfer for image hybridization using a neural network Code a recurrent neural network in TensorFlow to perform text-style generation Who this book is for Data scientists, machine learning developers, and deep learning enthusiasts looking to quickly get started with TensorFlow 2 will find this book useful. Some Python programming experience with version 3.6 or later, along with a familiarity with Jupyter notebooks will be an added advantage. Exposure to machine learning and neural network techniques would also be helpful.Machine learningStatistical methodsMachine learningStatistical methods.006.31Holdroyd Tony1653529MiAaPQMiAaPQMiAaPQBOOK9910809908703321Tensorflow 2. 0 quick start guide4004875UNINA