Eder Santana's Deep Learning with Python
Seiten
2017
Packt Publishing Limited (Verlag)
978-1-78728-046-5 (ISBN)
Packt Publishing Limited (Verlag)
978-1-78728-046-5 (ISBN)
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Takes you from basic calculus knowledge to understanding back-propagation and its application for training in neural networks for deep learning and understanding automatic differentiation. This book provides training in convolutional, recurrent neural networks and focuses on supervised learning and integration into your product offerings.
This book takes you from basic calculus knowledge to understanding back-propagation and its application for training in neural networks for deep learning and understanding automatic differentiation
About This Book
* Covers the latest concepts in Python deep learning
*Introduction to Tensorflow
*Full of examples of solving complicated tasks
Who This Book Is For
To get the most from this book, you should already have a good grasp of Python and it will help, though is not necessary, to have a basic understanding of some deep learning concepts.
What You Will Learn
* Get the lowdown on backpropagation
*Perceive and understand automatic differentiation with Theano
*Explore the powerful mechanism of seamless CPU and GPU usage with Theano
*Apply convolutional neural networks for image analysis
*Discover the methods of image classification and harness object recognition using deep learning
*Get to know recurrent neural networks for the textual sentimental analysis model
In Detail
Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python.
This book takes you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understanding automatic differentiation. Through the course, we will provide a thorough training in convolutional, recurrent neural networks and focus on supervised learning and integration into your product offerings such as search, image recognition, and object processing. We will also examine the performance of the sentimental analysis model and will conclude with an introduction to Tensorflow.
By the end of this book, you will be able to confidently start working with deep learning right away.
This book takes you from basic calculus knowledge to understanding back-propagation and its application for training in neural networks for deep learning and understanding automatic differentiation
About This Book
* Covers the latest concepts in Python deep learning
*Introduction to Tensorflow
*Full of examples of solving complicated tasks
Who This Book Is For
To get the most from this book, you should already have a good grasp of Python and it will help, though is not necessary, to have a basic understanding of some deep learning concepts.
What You Will Learn
* Get the lowdown on backpropagation
*Perceive and understand automatic differentiation with Theano
*Explore the powerful mechanism of seamless CPU and GPU usage with Theano
*Apply convolutional neural networks for image analysis
*Discover the methods of image classification and harness object recognition using deep learning
*Get to know recurrent neural networks for the textual sentimental analysis model
In Detail
Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python.
This book takes you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understanding automatic differentiation. Through the course, we will provide a thorough training in convolutional, recurrent neural networks and focus on supervised learning and integration into your product offerings such as search, image recognition, and object processing. We will also examine the performance of the sentimental analysis model and will conclude with an introduction to Tensorflow.
By the end of this book, you will be able to confidently start working with deep learning right away.
Eder Santana is a PhD candidate on Electrical and Computer Engineering. His thesis topic is on Deep and Recurrent neural networks. After working for 3 years with Kernel Machines (SVMs, Information Theoretic Learning, and so on), Eder moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks. Now, Eder contributes to Keras: Deep Learning Library for Python. Besides deep learning, he also likes data visualization and teaching machine learning, either on online forums or as a teacher assistant.
| Erscheint lt. Verlag | 31.7.2017 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| ISBN-10 | 1-78728-046-2 / 1787280462 |
| ISBN-13 | 978-1-78728-046-5 / 9781787280465 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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