Implement blockchain from scratch covering all the details with Racket, a general-purpose Lisp. You'll start by exploring what a blockchain is, so you have a solid foundation for the rest of the book. You'll then be ready to learn Racket before starting on your blockchain implementation. Once you have a working blockchain, you'll move onto extending it. The book's appendices provide supporting resources to help you in your blockchain projects.
The recommended approach for the book is to follow along and write the code as it's being explained instead of reading passively. This way you will get the most out of it. All of the source code is available for free download from GitHub.
What You Will Learn
Discover the Racket programming language and how to use it
Implement a blockchain from scratch using Lisp
Implement smart contracts and peer-to-peer support
Learn how to use macros to employ more general abstractions
Who This Book Is For Novices that have at least some experience with programming, as well as some basic working experience with computers. The book also assumes some experience with high school mathematics, such as functions.
If you have always been fascinated by programming for Artificial Intelligence, but it always seemed to you like something too hard to learn, you are in the right place, looking at the right book.
You surely are well aware of how important Artificial Intelligence is these days.
Just think about your everyday life. Every time you buy a product on Amazon, follow a new profile on Instagram, listen to a song on Spotify, or reserve a room on Booking, these platforms are learning something out of your behavior.
Also, it is commonly known that Machine Learning, Neural Networks, and the mastering of their most important language, Python, offer nowadays a lot of chances in work and business.
Either if you want to start up your own enterprise, apply your knowledge to your current business, or find a job at the greatest and most innovative companies, Computer Programming: Python will prepare the ground for your future success.
And that's the reason why we put together three of the most successful Russel R. Russo books as Learn Python Programming, Neural Networks for Beginners and Machine Learning with Python.
With Computer Programming: Python you will:
Discover why Python is the best language for Machine Learning
Find the smartest way to approach Machine Learning and interact with Python and Neural Networks
Get tips and tricks for a smooth and painless journey into artificial intelligence and prevent you from getting lost in coding
Debunk the most common myths about Machine Learning
Code your first application
Understand the elements of Python you will actually need
Easily find your path among Python data, statements, classes and objects
See how algorithms and Machine Learning will help you making predictions
Learn the smartest way to approach Neural Network Programming
Discover why algorithms are your friends
Learn about The "three Vs" of Big Data (plus two new Vs)
Spot the three most common problems with Neural Networks and how to overcome them
Описание: Chapter 1: Getting Started with Python 3 and Jupyter NotebookChapter Goal: Introduce the reader to the basics of Python Programming language, philosophy, and installation. We will also learn how to install it on various platforms. This chapter also introduces the readers to Python programming with Jupyter Notebook. In the end, we will also have a brief overview of the constituent libraries of sciPy stack.No of pages - 30Sub -Topics1. Introduction to the Python programming language2. History of Python3. Python enhancement proposals (PEPs)4. Philosophy of Python5. Real life applications of Python6. Installing Python on various platforms (Windows and Debian Linux Flavors)7. Python modes (Interactive and Script)8. Pip (pip installs python)9. Introduction to the scientific Python ecosystem10. Overview of Jupyter Notebook11. Installation of Jupyter Notebook12. Running code in Jupyter Notebook Chapter 2: Getting Started with NumPyChapter Goal: Get started with NumPy Ndarrays and the basics of NumPy library. The chapter covers the instructions for installation and basic usage of NumPy.No of pages: 10Sub - Topics: 1. Introduction to NumPy2. Install NumPy with pip33. Indexing and Slicing of ndarrays4. Properties of ndarrays5. Constants in NumPy6. Datatypes in datatypes Chapter 3: Introduction to Data VisualizationChapter goal - In this chapter, we will discuss the various ndarray creation routines available in NumPy. We will also get started with Visualizations with Matplotlib. We will learn how to visualize the various numerical ranges with Matplotlib.No of pages: 15Sub - Topics: 1. Ones and zeros2. Matrices3. Introduction to Matplotlib4. Running Matplotlib programs in Jupyter Notebook and the script mode5. Numerical ranges and visualizations Chapter 4: Introduction to Pandas Chapter goal - Get started with Pandas data structuresNo of pages: 10Sub - Topics: 1. Install Pandas2. What is Pandas3. Introduction to series4. Introduction to dataframesa) Plain Text Fileb) CSVc) Handling excel filed) NumPy file formate) NumPy CSV file readingf) Matplotlib Cbookg) Read CSVh) Read Exceli) Read JSONj) Picklek) Pandas and webl) Read SQLm) Clipboard Chapter 5: Introduction to Machine Learning with Scikit-LearnChapter goal - Get acquainted with machine learning basics and scikit-Learn libraryNo of pages: 101. What is machine learning, offline and online processes2. Supervised/unsupervised methods3. Overview of scikit learn library, APIs4. Dataset loading, generated datasets Chapter 6: Preparing Data for Machine LearningChapter Goal: Clean, vectorize and transform dataNo of Pages: 151. Type of data variables2. Vectorization3. Normalization4. Processing text and images Chapter 7: Supervised Learning Methods - 1Chapter Goal: Learn and implement classification and regression algorithmsNo of Pages: 301. Regression and classification, multiclass, multilabel classification2. K-nearest neighbors3. Linear regression, understanding parameters4. Logistic regression5. Decision trees Chapter 8: Tuning Supervised L
Become a skilled C++ programmer by embracing object-oriented programming and exploring language complexities, design patterns, and smart programming techniques with this detailed hands-on guide covering examples compliant with C++20
Key Features:
Apply object-oriented design concepts in C++ using language features and sound programming techniques
Unlock sophisticated programming solutions with nuances to become an efficient programmer
Explore design patterns as proven solutions for writing scalable and maintainable software in C++
Book Description:
While object-oriented software design helps you write more easily maintainable code, companies choose C++ as an OO language for its speed. Object-oriented programming (OOP) in C++ is not automatic - understanding OO concepts and how they map to C++ language features as well as OOP techniques is crucial. You must also know how to distinguish your code by utilizing well-tested, creative solutions, which can be found in popular design patterns. This book will help you to harness OOP in C++ for writing better code.
Starting with the essential C++ features that serve as building blocks for the main chapters, this book explains fundamental object-oriented concepts and shows you how to implement them in C++. With the help of practical code examples and diagrams, you'll find out how and why things work. The book's coverage furthers your C++ repertoire by including templates, exceptions, operator overloading, STL, and OO component testing. You'll also discover popular design patterns with in-depth examples and how to use them as effective programming solutions to recurring OOP problems.
By the end of this book, you'll be able to employ essential and advanced OOP concepts confidently to create enduring and robust software.
What You Will Learn:
Quickly learn the building blocks needed to develop a base for essential OOP features in C++
Implement OO designs using both C++ language features and proven programming techniques
Understand how well-designed, encapsulated code helps make more easily maintainable software
Write robust C++ code that can handle programming exceptions
Design extensible and generic code using templates
Apply operator overloading, utilize STL, and perform OO component testing
Examine popular design patterns to provide creative solutions for typical OO problems
Who this book is for:
Whether you are a professional programmer or an adept college student looking to use C++ as an OOP language, this book will help you create robust and easily maintainable code. Programmers who want to master the implementation of OO designs through both C++ language features and refined implementation techniques will find the book useful. This OOP book assumes prior programming experience; however, if you have no prior C++ or basic C++ experience, the early chapters will help you learn the core building blocks that set the foundation for the many OOP sections, advanced features, and design patterns.
Описание: Discover modern best practices and develop the skills that you need to get started with GUI programming in Tkinter by building real-world, productive, and fun applications such as a text editor, drum machine, game of chess, media player, drawing application, chat application, screen saver, port scanner, and much more!
Описание: Chapter 1: Optimization and neural networks Subtopics: How to read the book Introduction to the book Chapter 2: Hands-on with One Single NeuronSubtopics: Overview of optimization A definition of learning Constrained vs. unconstrained optimization Absolute and local minima Optimization algorithms with focus on Gradient Descent Variations of Gradient Descent (mini-batch and stochastic) How to choose the right mini-batch size Chapter 3: Feed Forward Neural NetworksSubtopics: A short introduction to matrix algebra Activation functions (identity, sigmoid, tanh, swish, etc.) Implementation of one neuron in Keras Linear regression with one neuron Logistic regression with one neuron Chapter 4: RegularizationSubtopics: Matrix formalism Softmax activation function Overfitting and bias-variance discussion How to implement a fully conneted network with Keras Multi-class classification with the Zalando dataset in Keras Gradient descent variation in practice with a real dataset Weight initialization How to compare the complexity of neural networks How to estimate memory used by neural networks in Keras Chapter 5: Advanced OptimizersSubtopics: An introduction to regularization l_p norm l_2 regularization Weight decay when using regularization Dropout Early Stopping Chapter 6Chapter Title: Hyper-Parameter tuningSubtopics: Exponentially weighted averages Momentum RMSProp Adam Comparison of optimizers Chapter 7Chapter Title: Convolutional Neural NetworksSubtopics: Introduction to Hyper-parameter tuning Black box optimization Grid Search Random Search Coarse to fine optimization Sampling on logarithmic scale Bayesian optimisation Chapter 8Chapter Title: Brief Introduction to Recurrent Neural NetworksSubtopics: Theory of convolution Pooling and padding Building blocks of a CNN Implementation of a CNN with Keras Introduction to recurrent neural networks Implementation of a RNN with Keras Chapter 9: AutoencodersSubtopics: Feed Forward Autoencoders Loss function in autoencoders Reconstruction error Application of autoencoders: dimensionality reduction Application of autoencoders: Classification with latent features Curse of dimensionality Denoising autoencoders Autoencoders with CNN Chapter 10: Metric AnalysisSubtopics: Human level performance and Bayes error Bias Metric analysis diagram Training set overfitting How to split your dataset Unbalanced dataset: what can happen K-fold cross validation Manual metric analysis: an example Chapter 11 Chapter Title: General Adversarial Networks (GANs)Subtopics: Introduction to GANs The building blocks of GANs An example of implementation of GANs in Keras APPENDIX 1: Introduction to KerasSubtopics: Sequential model Keras Layers Funct
If you have always been fascinated by programming for Artificial Intelligence, but it always seemed to you like something too hard to learn, you are in the right place, looking at the right book.
You surely are well aware of how important Artificial Intelligence is these days.
Just think about your everyday life. Every time you buy a product on Amazon, follow a new profile on Instagram, listen to a song on Spotify, or reserve a room on Booking, these platforms are learning something out of your behavior.
Also, it is commonly known that Machine Learning, Neural Networks, and the mastering of their most important language, Python, offer nowadays a lot of chances in work and business.
Either if you want to start up your own enterprise, apply your knowledge to your current business, or find a job at the greatest and most innovative companies, Computer Programming: Python will prepare the ground for your future success.
And that's the reason why we put together three of the most successful Russel R. Russo books as Learn Python Programming, Neural Networks for Beginners and Machine Learning with Python.
With Computer Programming: Python you will:
Discover why Python is the best language for Machine Learning
Find the smartest way to approach Machine Learning and interact with Python and Neural Networks
Get tips and tricks for a smooth and painless journey into artificial intelligence and prevent you from getting lost in coding
Debunk the most common myths about Machine Learning
Code your first application
Understand the elements of Python you will actually need
Easily find your path among Python data, statements, classes and objects
See how algorithms and Machine Learning will help you making predictions
Learn the smartest way to approach Neural Network Programming
Discover why algorithms are your friends
Learn about The "three Vs" of Big Data (plus two new Vs)
Spot the three most common problems with Neural Networks and how to overcome them
Автор: Quick John M. Название: Learn to Implement Games with Code ISBN: 1498753388 ISBN-13(EAN): 9781498753388 Издательство: Taylor&Francis Рейтинг: Цена: 8726.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Game development is one of the most rewarding crafts of modern times. Not only is making games a wonderful lifelong hobby, but employment opportunities exist at many levels. Learn to Implement Games with Code guides you through the development process as you put together a release-ready game. It is written in a friendly and conversational tone, which is suitable for a wide audience of aspiring game developers, such as yourself. You will gain practical, hands-on experience with implementing game components using code. Gradually, you will build a complete game that you can be proud of. After finishing this book, you will be prepared to start making games of your very own design.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
