The Deep Learning with Keras Workshop: Learn how to define and train neural network models with just a few lines of code

Matthew Moocarme

Mahla Abdolahnejad

Ritesh Bhagwat

€29.99

4.6 (8 Ratings)

Paperback Jul 2020 496 pages 1st Edition

Matthew Moocarme

Mahla Abdolahnejad

Ritesh Bhagwat

€29.99

4.6 (8 Ratings)

Paperback Jul 2020 496 pages 1st Edition

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

View table of contents

Preview Book

Download Code

Key benefits

Get to grips with various model evaluation metrics, including sensitivity, specificity, and AUC scores
Explore advanced concepts such as sequential memory and sequential modeling
Reinforce your skills with real-world development, screencasts, and knowledge checks

Description

New experiences can be intimidating, but not this one! This beginner’s guide to deep learning is here to help you explore deep learning from scratch with Keras, and be on your way to training your first ever neural networks. What sets Keras apart from other deep learning frameworks is its simplicity. With over two hundred thousand users, Keras has a stronger adoption in industry and the research community than any other deep learning framework. The Deep Learning with Keras Workshop starts by introducing you to the fundamental concepts of machine learning using the scikit-learn package. After learning how to perform the linear transformations that are necessary for building neural networks, you'll build your first neural network with the Keras library. As you advance, you'll learn how to build multi-layer neural networks and recognize when your model is underfitting or overfitting to the training data. With the help of practical exercises, you’ll learn to use cross-validation techniques to evaluate your models and then choose the optimal hyperparameters to fine-tune their performance. Finally, you’ll explore recurrent neural networks and learn how to train them to predict values in sequential data. By the end of this book, you'll have developed the skills you need to confidently train your own neural network models.

Who is this book for?

If you know the basics of data science and machine learning and want to get started with advanced machine learning technologies like artificial neural networks and deep learning, then this is the book for you. To grasp the concepts explained in this deep learning book more effectively, prior experience in Python programming and some familiarity with statistics and logistic regression are a must.

What you will learn

Gain insights into the fundamentals of neural networks
Understand the limitations of machine learning and how it differs from deep learning
Build image classifiers with convolutional neural networks
Evaluate, tweak, and improve your models with techniques such as cross-validation
Create prediction models to detect data patterns and make predictions
Improve model accuracy with L1, L2, and dropout regularization

Estimated delivery fee Deliver to Malta

Premium delivery 7 - 10 business days

€32.95

(Includes tracking information)

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

€32.99

€29.99

€29.99

Total € 92.97

FAQs

What is the delivery time and cost of print book?

Shipping Details

USA:

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.

Unfortunately, due to several restrictions, we are unable to ship to the following countries:

Afghanistan
American Samoa
Belarus
Brunei Darussalam
Central African Republic
The Democratic Republic of Congo
Eritrea
Guinea-bissau
Iran
Lebanon
Libiya Arab Jamahriya
Somalia
Sudan
Russian Federation
Syrian Arab Republic
Ukraine
Venezuela

What is custom duty/charge?

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order?

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges?

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.

How can I cancel my order?

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy?

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged?

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use?

You can pay with the following card types:

Visa Debit
Visa Credit
MasterCard
PayPal

What is the delivery time and cost of print books?

Shipping Details

USA:

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Unfortunately, due to several restrictions, we are unable to ship to the following countries:

Afghanistan
American Samoa
Belarus
Brunei Darussalam
Central African Republic
The Democratic Republic of Congo
Eritrea
Guinea-bissau
Iran
Lebanon
Libiya Arab Jamahriya
Somalia
Sudan
Russian Federation
Syrian Arab Republic
Ukraine
Venezuela

Filter reviews by

All

Packt verified reviews

Amazon verified reviews

Darwin Cubi Jan 27, 2021

En mi caso me interesó este libro pues la mayoría del tiempo trabajo con R y he estado poco a poco aprendiendo python, con principal intereses en el aprendizaje profundo. No he tenido la oportunidad de trabajar con keras o tensorFlow y tengo que decir que me sentí algo intimidado cuando mire los ejemplos que se encuentran que la web del desarrollador. Este libro en apariencia es extenso con sus 495 páginas y 9 capítulos pero se debe a que tiene varias ilustraciones, bloques de código junto con el detalle de las funciones. Desde el primer capítulo hasta el último se definen y aclaran conceptos de manera muy sencilla sin entrar en detalles matemáticos, si ya tienes cierta experiencia podrías saltarte el capítulo 1. Lo genial del libro es que hace una perfecta sincronía entre mostrar los pasos que se deben seguir para la elaboración de un modelo y cómo usar las bibliotecas keras y tensorflow. Además el libro está estructurado de tal manera que todo el desarrollo y aprendizaje de un capitulo se usa para el capítulo siguiente.Todo el código que se muestra en el libro ya se encuentra en github junto con el archivo requirements.txt que facilita mucho la instalación de las bibliotecas necesarias. Recomiendo mucho este libro a personas que tengan poca o nada de experiencia en el desarrollo de modelos ya que hará que su primer contacto sea muy fácil y muy enriquecedor en conceptos y buenas prácticas al momento de crear un modelo de aprendizaje profundo. No lo recomendaría para personas con experiencia que buscan el concepto matemático o que desean profundizar en la optimización o uso de los hyperparametros de un modelo.In my case I was interested in this book as most of the time I work with R and have been slowly learning python, with main interests in deep learning. I have not had the opportunity to work with keras or tensorFlow and I have to say that I felt a bit intimidated when I looked at the examples found on the developer's website. This book in appearance is lengthy with its 495 pages and 9 chapters but that is because it has several illustrations, code blocks along with the detail of the functions. From the first chapter to the last it defines and clarifies concepts in a very simple way without going into mathematical details, if you already have some experience you could skip chapter 1. The great thing about the book is that it makes a perfect synchrony between showing the steps that must be followed for the development of a model and how to use the keras and tensorflow libraries. Also the book is structured in such a way that all the development and learning from one chapter is used for the next chapter.All the code shown in the book is already on github along with the requirements.txt file which makes it much easier to install the necessary libraries. I highly recommend this book to people with little or no experience in model development as it will make your first contact very easy and very enriching in concepts and best practices when creating a deep learning model. I would not recommend it for experienced people who are looking for the mathematical concept or who want to go deeper into the optimization or use of the hyperparameters of a model.

Amazon Verified review

Sree Feb 11, 2021

One of the best books on Keras!

Andrew Dec 13, 2020

This is a great book if you want to learn how to use Keras for deep learning! The layout is clear and easily understandable, with lots of practical applications with code examples. The book is well organized into chapters that make it a practical resource. Practical aspects such as data preprocessing, model evaluation and advantages/disadvantages of deep learning are also covered, which make this a good reference for both how deep learning models work as well as how they can be best applied. Highly recommended for anyone looking to start using Keras for their machine learning projects!

Siim Tolk Feb 28, 2021

The authors do not assume any prior knowledge from the reader except for some experience with Python and Linear Algebra. However, the chapters manage to avoid lengthy introductory theory and cover the necessary basics quickly which means that you’ll see how to actually use the packages on example problems in no time. The book relies heavily on examples, which keeps the pace high, and offers explanations on what is going on behind the scenes along the way.All in all, I think it is an excellent resource for people new to the field.Best read with a Jupyter notebook running on aside.(FYI, I was sent a copy of the book by the authors for an unbiased review )

Martin Alonso Jan 11, 2021

I’ve always found neural networks and deep learning fascinating. The ease with which they can be used to solve any machine learning problem are mind-blowing, though their computational power varies with the number of neurons you decide to use, which can be either a hindrance or a boon to the model.The advantage of having open-source platforms, such as Python, Jupyter, and the several libraries and modules available is there is always a software developer who has already tried to solve the problem you are working on. Thus, the likelihood that there is a Python library capable of implementing machine learning or deep learning models was already pretty high. Enter Tensorflow and Keras.Both these libraries work together, and they are very easy to implement straight out of the box – once you’ve installed the necessary dependencies that enable their use. And though the tutorials on their website are straightforward, there is very little documentation on how to apply neural networks beyond what is offered on the website, which is simple regression and image classification problems.The Deep Learning with Keras Workshop aims to correct this.It is a very easy to follow guide which strives to lay the groundwork behind how neural networks work and how these could be applied using Keras. It does so by assuming that the user isn’t wanting in statistical analysis or how modeling works, but by first explaining the proper way the data should be assessed, establishing a baseline using both the data itself and a simple model, and from there building up on how neural networks work.When it comes to how models are built, evaluated, and tuned, it is very easy to find tutorials that cover all this; albeit these can be very brief, not explaining the intricacies of the model, while also being very crowded when it comes to explaining how each part of the model-building process comes together. I thoroughly enjoyed that, in this book, while each of these parts is studied, they are each dedicated a separate chapter, allowing the reader to take their time to explained how concepts such as hyperparameter tuning or cross-validation are used to boost the model’s prediction capabilities. By not cramming model building, tuning, evaluation, and cross-validation into every single chapter, but rather presenting each one individually and using the previous as a foundation to build on the next, the authors have done an extremely good job building a book that genuinely teaches not only how Keras works, but how the process of building any model works.Turning to the user examples, each chapter builds on the previous one, allowing the user to build more robust models, but not all chapters use the same examples. I found it refreshing that the book works with several real-world problems and data sets, showing the user how neural networks and Keras can be applied to several problems. And, though not all of them have high accuracy rates or low-test errors, which is not a downside given this is how most real-world problems work, it helps the user by showing the applications of neural networks beyond simple regressions and image classification.The one true downside to this book is that it does not go into detail how each parameter affects the model. Why is it important to have more (or less) epochs or batches? What is the difference between an “adam” and “sgd” optimizer? What is the difference between using Dense(1, activation=’sigmoid’) and Activation(‘sigmoid’)? Nevertheless, going into these details could make the book harder to understand and could cause the user to lose interest, so perhaps adding some minor details, or pointing to further material that can go in depth into those subjects, akin to what is done with the Keras and Tensorflow package websites, would be helpful.Overall, I find that my experience with Keras and neural networks has improved upon reading this book, and though the use of such is computationally demanding, I find that I can now try to use small deep learning models as part of my model evaluation and comparison when exploring new data sets in a more comfortable manner.

The Deep Learning with Keras Workshop: Learn how to define and train neural network models with just a few lines of code

What do you get with Print?