Kirjahaku

This book is an exploration of Keras, a deep learning library developed in Python. The author begins by helping you understand more details about the keras library to help you understand where and how you can use it. The various Keras layers have been discussed; hence you will know what you can use them for. Keras comes with various optimizers that you can use to optimize your deep learning models. These have been discussed in detail. The cost functions provided by the library have also been explored in detail. The various metrics used to measure the performance of models developed in Keras have been discussed. Keras can be used for performing basic classification tasks. You will learn this in details. It is also a good library for image classification tasks. The author has explored this. Text classification is a common task today in data processing. The author helps you know how to perform this task with the Keras library. Advanced Keras features such as checkpoints and callbacks have also been discussed.

This is a guide to Android application developers on how to use Android Studio 6 to develop their apps. The first part is a guide on how to use the Code Editor in Android 6. The Code Editor provides programmers with an environment in which they can write their code, and perform some editing to it. The various features and functionalities provided by the Code Editor are discussed. Auto completion, which helps you to avoid too much typing, is also discussed, and you will learn how to enjoy using this feature. You will also learn how to split the Code Editor window to get various panels. Code folding is explored in detail. The various parts which form the code editor in Android Studio 6 are examined, along with the Designer Tool in Android Studio, which helps the user to create the user interface part for their Android apps. You will learn how to use in its various modes, that is, the Text and the Design modes. The use of Java Code in Android Studio 6 to create the user interface is explained in detail.

This book is an exploration of Ruby on Rails 5. It begins by guiding you on how to setup the Rails development environment on your local computer in various platforms. You are then guided on how to test the various parts of your Rails 5 app. Test-Driven Development is also explored in detail. The MVC (Model-View-Controller) architecture for apps is discussed. You will learn how to implement a Rails 5 app using such architecture for ease of development, maintenance, and organization. Each of the modern web apps is in need of an authentication system. This helps us to regulate how the resources of your app are accessed. This book guides you on how to create an authentication system with Rails. It is also good for us to implement a mechanism for activating accounts via email. This also allows us to know whether a user has provided a valid email address or not. This book guides you on how to implement such a system in Rails 5. You will learn how to deploy a Rails 5 app with Heroku.

This book is a guide for you on how to program in C++, Java and Python. These three programming languages are in high demand in the market, calling for you to learn how to program them. The book begins guiding you on how to program in C++. It starts by guiding you on how to setup your environment for programming in C++. You are also guided on how to declare variables for reserving memory locations. Basic programming features such as decision making and looping have been discussed. You will know how to write programs capable of looping and making decisions in C++. Arithmetic operators have been explored. The book also guides you on how to program in Java. You are guided on how to setup your own environment for programming in Java. You will also know how to write your Java programs, either in a text editor such as Notepad, or in Netbeans. The process of compiling and running Java programs via the command prompt has also been discussed in detail. You will also know how to create loops in Java, as well as writing programs capable of making decisions. Arithmetic operators supported in Java have also been discussed in detail. The book also guides you on how to program in Python. It begins by helping you to setup your local environment for Python programming.

This book is an exploration of an artificial neural network. It has been created to suit even the complete beginners to artificial neural networks. The first part of the book is an overview of artificial neural networks so as to help the reader understand what they are. You will also learn the relationship between the neurons which make up the human brain and the artificial neurons. Artificial neural networks embrace the concept of learning which is common in human beings. This book guides you to understand how learning takes place in artificial neural networks. The back-propagation algorithm, which is used for training artificial neural networks, is discussed. The book also guides you through the architecture of an artificial neural network. The various types of artificial neural networks based on their architecture are also discussed. The book guides you on the necessary steps for one to build a neural network. The perception, which is a type of an artificial neural network, is explored, and you will explore how to implement one programmatically.

This book is written to help those in need of familiarizing themselves with the basics as well as advanced aspects of statistics. After reading it, you will be able to know the meaning of the various terms associated with statistics. Significance testing is one of the important subjects in statistics as it helps us prove the credibility of statements based on data. This has been discussed in this book. The author guides you on how to calculate statistical measures including the arithmetic mean, adjusted R-squared, arithmetic variance, arithmetic median, arithmetic mode and others. Various types of data have been used in calculations including individual series data, discrete series data and continuous series data. Point estimation has been explored in detail. The author has also discussed skewness, kurtosis, binomial distribution, linear regression and several other topics in statistics.

This book is an exploration of deep learning in Python using TensorFlow. The author guides you on how to create machine learning models using TensorFlow. You will know the initial steps of getting started with TensorFlow in Python. This involves installing TensorFlow and writing your first code. TensorFlow works using the concept of graphs. The author helps you know how expressions are represented into graphs in TensorFlow. Deep learning in Python with TensorFlow simply involves the creation of neural network models. The author helps you understand how to create neural network models with TensorFlow. You are guided on how to train such models with data of various types. Examples of such data include images and text. The process of loading your own data into TensorFlow for training neural network models has also been discussed. You will also know how to use the inbuilt data for training your neural network models.

This book is a guide for you on probability theory. It is a good book for students and practitioners in fields such as finance, engineering, science, technology and others. The book guides on how to approach probability in the right way. Numerous examples have been given, both theoretical and mathematical with a high degree of accuracy. If you have wished to know how to model random and uncertain events, this is the right book for you. The author guides you on how to tackle probabilistic problems using various forms of probability distributions. Probabilities are normally combined using rules. The author has helped you understand how to apply these rules to model your problems. The author has approached the subject in an easy way and by use of real world examples. Numerous stories have been given to help you know how the various distributions are connected and the kind of problems where each distribution should be applied. The author finally helps you know the areas in which probability is applied today. You will also know the various ways you can use probability in your day-to-day activities for your own benefit. It is the best book to help you know how to make better decisions when dealing with random and uncertain events. If you are a student, grab a copy of this book and know how to tackle probability-related problems.

This book is a guide for you on how to use Pandas and Numpy in Python programming language for data analysis. The author begins by helping you familiarize yourself with the basics of data science, Numpy and Pandas. You are guided on how to work with Numpy arrays and how to manipulate them. The various operations that you can perform on your data via the Pandas library have been discussed. You will also know how to create various data structures in Pandas for data storage. Data from the environment is dirty. The process of cleaning such data has been discussed. This involves handling outliers, missing values etc. The author guides you on how to work with data in various types of storage formats. Examples include MS Excel, CSV files, JSON, etc. You are also guided on how to calculate various measures for your data. The process of visualizing data has been explored in detail.