Are you looking to automate your trading strategy? Are you looking for a more efficient way of completing your financial analysis?
Python is the answer.
While looking to gain summarize our knowledge on the subject, we realized that there was a lot of information available in books and the internet. However, there seemed to be too much information. There were 500-page textbooks on the subject that had very little practical use. They were pretty useless for beginners just like a dictionary is useless for anyone trying to learn a language. All these books had tons of theory with no step-by-step guide.
There were a whole bunch of other blogs that had basic programming information with no relation to financial strategies.
With this in mind, this book starts you off with a step-by-step guide to install Python on your computer; and plot/visualize relevant financial data. Later in the book, you can build on your basic knowledge to learn more about advanced financial analysis and trading strategies to move forward. This book is what you've been looking for.
Learn proven patterns, techniques, and tricks to take full advantage of the Node.js platform. Master well-known design principles to create applications that are readable, extensible, and that can grow big.
Key Features
Learn how to create solid server-side applications by leveraging the full power of Node.js 14
Understand how Node.js works and learn how to take full advantage of its core components as well as the solutions offered by its ecosystem
Avoid common mistakes and use proven patterns to create production grade Node.js applications
Book Description
In this book, we will show you how to implement a series of best practices and design patterns to help you create efficient and robust Node.js applications with ease.
We kick off by exploring the basics of Node.js, analyzing its asynchronous event driven architecture and its fundamental design patterns. We then show you how to build asynchronous control flow patterns with callbacks, promises and async/await. Next, we dive into Node.js streams, unveiling their power and showing you how to use them at their full capacity. Following streams is an analysis of different creational, structural, and behavioral design patterns that take full advantage of JavaScript and Node.js. Lastly, the book dives into more advanced concepts such as Universal JavaScript, scalability and messaging patterns to help you build enterprise-grade distributed applications.
Throughout the book, you'll see Node.js in action with the help of several real-life examples leveraging technologies such as LevelDB, Redis, RabbitMQ, ZeroMQ, and many others. They will be used to demonstrate a pattern or technique, but they will also give you a great introduction to the Node.js ecosystem and its set of solutions.
What you will learn
Become comfortable with writing asynchronous code by leveraging callbacks, promises, and the async/await syntax
Leverage Node.js streams to create data-driven asynchronous processing pipelines
Implement well-known software design patterns to create production grade applications
Share code between Node.js and the browser and take advantage of full-stack JavaScript
Build and scale microservices and distributed systems powered by Node.js
Use Node.js in conjunction with other powerful technologies such as Redis, RabbitMQ, ZeroMQ, and LevelDB
Who this book is for
This book is for developers and software architects who have some prior basic knowledge of JavaScript and Node.js and now want to get the most out of these technologies in terms of productivity, design quality, and scalability. Software professionals with intermediate experience in Node.js and JavaScript will also find valuable the more advanced patterns and techniques presented in this book.
This book assumes that you have an intermediate understanding of web application development, databases, and software design principles.
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
Автор: Quick John M. Название: Learn to Implement Games with Code ISBN: 1498753388 ISBN-13(EAN): 9781498753388 Издательство: Taylor&Francis Рейтинг: Цена: 9186.00 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.
Описание: MATLAB can run Python code! Python for MATLAB Development shows you how to enhance MATLAB with Python solutions to a vast array of computational problems in science, engineering, optimization, statistics, finance, and simulation. It is three books in one: * A thorough Python tutorial that leverages your existing MATLAB knowledge with a comprehensive collection of MATLAB/Python equivalent expressions * A reference guide to setting up and managing a Python environment that integrates cleanly with MATLAB * A collection of recipes that demonstrate Python solutions invoked directly from MATLAB This book shows how to call Python functions to enhance MATLAB's capabilities. Specifically, you'll see how Python helps MATLAB: * Run faster with numba * Distribute work to a compute cluster with dask * Find symbolic solutions to integrals, derivatives, and series summations with SymPy * Overlay data on maps with Cartopy * Solve mixed-integer linear programming problems with PuLP * Interact with Redis via pyredis, PostgreSQL via psycopg2, and MongoDB via pymongo * Read and write file formats that are not natively understood by MATLAB, such as SQLite, YAML, and ini Who This Book Is For MATLAB developers who are new to Python and other developers with some prior experience with MATLAB, R, IDL, or Mathematica.
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.
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
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