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Hands-On Transfer Learning with Python
Hands-On Transfer Learning with Python

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

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Profile Icon Nitin Panwar Profile Icon Sarkar Profile Icon Raghav Bali Profile Icon Tamoghna Ghosh
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€18.99 per month
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (3 Ratings)
Paperback Aug 2018 438 pages 1st Edition
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€20.98 €29.99
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Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Nitin Panwar Profile Icon Sarkar Profile Icon Raghav Bali Profile Icon Tamoghna Ghosh
Arrow right icon
€18.99 per month
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (3 Ratings)
Paperback Aug 2018 438 pages 1st Edition
eBook
€20.98 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€20.98 €29.99
Paperback
€36.99
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Renews at €18.99p/m

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Hands-On Transfer Learning with Python

Deep Learning Essentials

This chapter provides a whirlwind tour of deep learning essentials, starting from the very basics of what deep learning really means, and then moving on to other essential concepts and terminology around neural networks. The reader will be given an overview of the basic building blocks of neural networks, and how deep neural networks are trained. Concepts surrounding model training, including activation functions, loss functions, backpropagation, and hyperparameter-tuning strategies will be covered. These foundational concepts will be of great help for both beginners and experienced data scientists who are venturing into deep neural network models. Special focus has been given to how to set up a robust cloud-based deep learning environment with GPU support, along with tips for setting up an in-house deep learning environment. This should be very useful...

What is deep learning?

In machine learning (ML), we try to automatically discover rules for mapping input data to a desired output. In this process, it's very important to create appropriate representations of data. For example, if we want to create an algorithm to classify an email as spam/ham, we need to represent the email data numerically. One simple representation could be a binary vector where each component depicts the presence or absence of a word from a predefined vocabulary of words. Also, these representations are task-dependent, that is, representations may vary according to the final task that we desire our ML algorithm to perform.

In the preceding email example, instead of identifying spam/ham if we want to detect sentiment in the email, a more useful representation of the data could be binary vectors where the predefined vocabulary consists of words with positive...

Deep learning frameworks

One of the primary reasons for the widespread popularity and adoption of deep learning is the Python deep learning ecosystem, which consists of easy-to-use open source deep learning frameworks. However, the deep learning landscape is rapidly changing, considering how new frameworks keep getting launched and older ones reach the end of their life. Deep learning enthusiasts might know that Theano was the first and most popular deep learning framework, created by MILA (https://mila.quebec/), which was headed by Yoshua Bengio. Unfortunately, it was recently announced that further development and support for Theano is ending after the launch of its latest version (1.0) in 2017. Hence, it is of paramount importance to understand what frameworks are out there that can be leveraged to implement and solve deep learning. Another point to remember here is that several...

Setting up a cloud-based deep learning environment with GPU support

Deep learning works quite well on a standard single PC setup with a CPU. However, once your datasets start increasing in size and your model architectures start getting more complex, you need to start thinking about investing in a robust deep learning environment. The major expectations being the system can build and train models efficiently, take less time to train models, and is fault tolerant. Most deep learning computations are essentially millions of matrix operations (data is represented as matrices) and enable fast computation in parallel; GPUs have been proven to work really well in this aspect. You can consider setting up a robust cloud-based deep learning environment or even an in-house environment. Let's look at how we can set up a robust cloud-based deep learning environment in this section.

The...

Setting up a robust, on-premise deep learning environment with GPU support

Often users or organizations may not want to leverage cloud services, especially if their data is sensitive, and so focus on building an on-premise deep learning environment. The major focus here should be to invest in the right type of hardware to enable maximum performance and leverage the right GPU for building deep learning models. With regards to hardware, special emphasis goes to the following:

  • Processor: You can invest in an i5 or an i7 Intel CPU, or maybe an Intel Xeon if you are looking to spoil yourself!
  • RAM: Invest in at least 32 GB of DDR4 or better RAM for your memory.
  • Disk: A 1 TB hard disk is excellent, and also you can invest in a minimum of 128 GB or 256 GB of SSD for fast data access!
  • GPU: Perhaps the most important component for deep learning. Invest in a NVIDIA GPU, anything above a...

Neural network basics

Let's try to familiarize ourselves with some of the basic concepts behind neural networks that make all deep learning models tick!

A simple linear neuron

A linear neuron is the most basic building block of a deep neural network. It can be schematically represented as shown in the following figure. Here, represents the input vector and wis are the weights of the neuron. Given a training set consisting a of set of input, target value pairs, a linear neuron tries to learn a linear transformation that can map the input vectors to the corresponding target value. Basically, a linear neuron approximates the input output relationship by a linear function :

Schematic representation of a simple linear neuron...

Summary

We covered a lot of ground in this chapter as regards the fundamentals of deep learning. We really commend your efforts on getting this far! The idea of this chapter was to introduce you to the core concepts and terminology pertaining to the domain of deep learning. We started with a brief introduction of deep learning and then looked at the popular frameworks in today's deep learning landscape. Detailed step-by-step guides have also been included for setting up your own deep learning environments to develop and train large-scale deep learning models on GPUs.

Finally, we covered essential concepts around neural networks including linear and non-linear neurons, data representation, chain rule, loss functions, multilayer networks, and SGD. The challenges of learning in neural networks were also covered, including popular caveats surrounding local minima and exploding...

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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date : Aug 31, 2018
Length: 438 pages
Edition : 1st
Language : English
ISBN-13 : 9781788831307
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Product Details

Publication date : Aug 31, 2018
Length: 438 pages
Edition : 1st
Language : English
ISBN-13 : 9781788831307
Vendor :
Google
Category :
Languages :
Concepts :
Tools :

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Table of Contents

13 Chapters
Machine Learning Fundamentals Chevron down icon Chevron up icon
Deep Learning Essentials Chevron down icon Chevron up icon
Understanding Deep Learning Architectures Chevron down icon Chevron up icon
Transfer Learning Fundamentals Chevron down icon Chevron up icon
Unleashing the Power of Transfer Learning Chevron down icon Chevron up icon
Image Recognition and Classification Chevron down icon Chevron up icon
Text Document Categorization Chevron down icon Chevron up icon
Audio Event Identification and Classification Chevron down icon Chevron up icon
DeepDream Chevron down icon Chevron up icon
Style Transfer Chevron down icon Chevron up icon
Automated Image Caption Generator Chevron down icon Chevron up icon
Image Colorization Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(3 Ratings)
5 star 66.7%
4 star 0%
3 star 0%
2 star 33.3%
1 star 0%
Indra May 13, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I've read his books and worked directly with Dipanjan, he's a great person to learn from. Highly recommend!
Amazon Verified review Amazon
Nehrmegar May 13, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book for data scientists who want to know what is transfer learning and how to apply it in order to improve the performance of their models.
Amazon Verified review Amazon
Amazon Customer Sep 07, 2019
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
You will most of the contents on internet
Amazon Verified review Amazon
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