Внимание! Заказы на школьную литературу принимаются исключительно через сайт и комплектуются до 5 рабочих дней!

Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras

Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras
Hands-On+Transfer+Learning+with+Python%3A+Implement+advanced+deep+learning+and+neural+network+models+using+TensorFlow+and+Keras - фото 1
2 247 грн
89107
ISBN
978-1788831307
Издательство
Packt Publishing
Год
2018
Страниц
438
Формат
70х100 1/16 (170х240 мм)
Обложка 
Мягкая
Тип бумаги 
Офсет
Язык
Английский
Срок поставки
25-30 дней
  • По ХарьковуДоставка курьером - 100 грн
    Бесплатно - от 2000 грн
  • По УкраинеБесплатно - от 2000 грн
    Новая Почта - от 40 грн
    Укрпочта - от 25 грн
  • Международная доставкаУкрпочта...
Подробнее о доставке

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.

About the Author

Dipanjan Sarkar is a Data Scientist at Intel, the world's largest silicon company, on a mission to make the world more connected and productive. He primarily works on data science, advanced analytics, business intelligence, application development and building large-scale intelligent systems. Besides this, he is also a formal course instructor around the areas of data science and artificial intelligence. Dipanjan holds a master of technology degree in Information Technology with specializations in Data Science and Software Engineering from the International Institute of Information Technology, Bangalore. He is also an avid supporter of self-learning, especially Massive Open Online Courses and also holds a Data Science Specialization from Johns Hopkins University on Coursera besides multiple technical certifications around the areas of machine learning and data science.

Raghav Bali is a Data Scientist at Optum, a United Health Group Company. He is part of the Data Science group where his work is enabling United Health Group develop data driven solutions to transform healthcare sector. He primarily works on data science, analytics and development of scalable machine learning based solutions. In his previous role at Intel as a Data Scientist, his work involved research and development of enterprise solutions in the infrastructure domain leveraging cutting edge techniques from machine learning, deep learning and transfer learning. He has also worked in domains such as ERP and finance with some of the leading organizations of the world. Raghav has a master's degree (gold medalist) in Information Technology from International Institute of Information Technology, Bangalore.

Tamoghna Ghosh is a data scientist at Intel Corporation. He has overall 10+ years of experience in analytics, algorithms, data visualization & software development. He received his master's degree in Computer Science from the Indian Statistical Institute,Kolkata , with a focus on pattern recognition and information retrieval. He also holds a master degree in Mathematics from University of Calcutta with specialization in Functional Analysis & Mathematical Modelling/Dynamical systems.

Tamoghna started his career at Microsoft Research India as a Research assistant and worked on designing some novel approach to crypt-analysis of block ciphers. Tamoghna's interests include algorithms, data science, artificial intelligence and deep learning. In his free time he likes reading books and travelling.

Table of Contents

  1. Machine Learning Fundamentals
  2. Deep Learning Essentials
  3. Understanding Deep Learning Architectures
  4. Transfer Learning Fundamentals
  5. Unleash the Power of Transfer Learning
  6. Image Recognition and Classification
  7. Text Document Categorization
  8. Audio Identification and Categorization
  9. Deep Dream
  10. Neural Style Transfer
  11. Automated Image Caption Generator
  12. Image Colorization

Товар входит в категории

Вы можете купить книгу с доставкой курьером новой поштой укрпочтой Кривой Рог, Львов, Полтава, Житомир, Черкассы, Харьков, Чернигов, Винница, Тернополь, Киев, Луцк, Ровно, Хмельницкий, Херсон, Кировоград, Николаев, Днепропетровск, Ужгород, Запорожье, Суммы, Черновцы, Одесса, Ивано-франковск, другие города Украины. только в нашем магазине низкие цены, возможен торг, прямые поступления от издательства,книги под заказ, печать книг на заказ, компьютерные книги на английском языке.

Ви можете купити придбати книгу з доставкою кур'єром нова пошта Укрпошта Кривий Ріг, Львів, Полтава, Житомир, Харків, Чернігів, Вінниця, Тернопіль, Київ, Луцьк, Рівне, Хмельницький, Херсон, Кіровоград, Миколаїв, Дніпропетровськ, Ужгород , Запоріжжя, Суми, Чернівці, Черкаси, Одеса, Івано-франківськ, інші міста України. тільки в нашому магазині низькі ціни, можливий торг, прямі надходження від видавництва, книги під замовлення, друк книг на замовлення, комп'ютерні книги англійською мовою.