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Real World OCaml: Functional Programming for the Masses, Second Edition: Real World OCaml: Functional Programming for the Masses, Second Edition
: Anil Madhavapeddy, Yaron Minsky
: Cambridge University Press
: 2022
: 514
:
: pdf (true)
: 10.2 MB

This fast-moving tutorial introduces you to OCaml, an industrial-strength programming language designed for expressiveness, safety, and speed. Through the book's many examples, you'll quickly learn how OCaml stands out as a tool for writing fast, succinct, and readable systems code using functional programming. Real World OCaml takes you through the concepts of the language at a brisk pace, and then helps you explore the tools and techniques that make OCaml an effective and practical tool. You'll also delve deep into the details of the compiler toolchain and OCaml's simple and efficient runtime system. This second edition brings the book up to date with almost a decade of improvements in the OCaml language and ecosystem, with new chapters covering testing, GADTs, and platform tooling. This title is also available as open access on Cambridge Core, thanks to the support of Tarides. Their generous contribution will bring more people to OCaml. We wrote this book because we believe in the importance of programming languages, and that OCaml in particular is an important language to learn. Both of us have been using OCaml in our academic and professional lives for over 20 years, and in that time we've come to see it as a powerful tool for building complex software systems.
: Ingvar16 22-09-2022, 18:03 | : 0 |
Modern Statistics: A Computer-Based Approach with Python: Modern Statistics: A Computer-Based Approach with Python
: Ron S. Kenett, Shelemyahu Zacks, Peter Gedeck
: Springer
: 2022
: 452
:
: pdf (true)
: 10.4 MB

This innovative textbook presents material for a course on modern statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others.
: Ingvar16 22-09-2022, 16:39 | : 0 |

: BDM. C++ & Python Complete Manual
:
: Papercut Limited
: 2022
: pdf
: 148
: 75 mb
:

Python and C++, two of the most powerful and used programming languages in the world. Between them, theyre used in everything from the latest PlayStation game to controlling the system on-board the International Space Station. This book will help you get started with both programming languages, leading you to advanced coding techniques and opening your mind to a new level of computing.
: na5ballov 22-09-2022, 16:13 | : 0 |
: Visual # 2012
: ..
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: 2013
: 480
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: 53,98
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: gol8425 22-09-2022, 10:44 | : 0 |

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: 2011
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: 400
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: gol8425 22-09-2022, 09:57 | : 0 |
Go Fundamentals: Gopher Guides (Rough Cuts): Go Fundamentals: Gopher Guides (Rough Cuts)
: Mark Bates, Cory LaNou
: Addison-Wesley Professional
: 2022
: 135
:
: epub, mobi
: 10.2 MB

Start Writing Production-Ready Go Code Fast--No Go Experience Necessary! Thousands of developers and teams want to start taking advantage of Go, the powerful language used in projects ranging from Kubernetes to Docker and Vault. Go Fundamentals is specifically designed to get you up-to-speed fast, to leverage your existing knowledge of other languages, and to help you avoid common mistakes made by Go newcomers. Based on author Mark Bates's and Cory LaNou's pioneering Gopher Guides training curricula, this guide will allow you to quickly understand and use Go syntax, core features, and idioms. Reflecting Go through version 1.18--which includes Go's exciting new support for generics--this guide prepares you to write robust, reliable, well-performing production code right from the outset.
: Ingvar16 22-09-2022, 01:09 | : 0 |
C++ Software Design: Design Principles and Patterns for High-Quality Software (Final): C++ Software Design: Design Principles and Patterns for High-Quality Software (Final)
: Klaus Iglberger
: OReilly Media, Inc.
: 2022
: 435
:
: epub (true), mobi
: 10.8 MB

Good software design is essential for the success of your project, but designing software is hard to do. You need to have a deep understanding of the consequences of design decisions and a good overview of available design alternatives. With this book, experienced C++ developers will get a thorough, practical, and unparalleled overview of software design with this modern language. C++ trainer and consultant Klaus Iglberger explains how you can manage dependencies and abstractions, improve changeability and extensibility of software entities, and apply and implement modern design patterns to help you take advantage of today's possibilities.
: Ingvar16 22-09-2022, 00:35 | : 0 |
: Practical Ansible: Configuration Management from Start to Finish, 2nd Edition
: VincentSesto
: Apress
: 2022
: ePUB, PDF
: 334
: 10 Mb
: English

Get ready to go from the basics of using Ansible to becoming proficient at implementing configuration management in your projects. This book begins with the basics of Ansible, providing you with details on how to install and configure your environment while working with different Ansible modules from the command line. Next, it introduces you to working with Ansible tasks and organizing configuration code into playbooks.
Youll then learn how to extend playbooks further, using roles and templates within the configuration code. Author Vincent Sesto then extends your knowledge further by covering custom Ansible modules using Python and Linux shell scripts and demonstrating how you can start to keep your secret values encrypted and secure using Ansible Vault. Youll also develop Ansible roles with the use of Ansible Galaxy to reuse existing roles that others have created. This updated edition reflects changes added in the latest version of Ansible (2.9). It also includes an expanded chapter on testing Ansible using Molecule and managing large server environments using applications like Ansible Tower.
: vitvikvas 21-09-2022, 20:27 | : 0 |
: MATLAB Deep Learning Toolbox Getting Started Guide
: Mark Hudson Beale, Martin T. Hagan, Howard B. Demuth
: The MathWorks, Inc
: 2022
: True PDF
: 132
: 10 Mb
: English

Deep Learning Toolbox provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. The Experiment Manager app helps you manage multiple deep learning experiments, keep track of training parameters, analyze results, and compare code from different experiments. You can visualize layer activations and graphically monitor training progress.
You can import networks and layer graphics from TensorFlow 2, TensorFlow-Keras, and PyTorch, the ONNX (Open Neural Network Exchange) model format, and Caffe. You can also export Deep Learning Toolbox networks and layer graphs to TensorFlow 2 and the ONNX model format. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
: vitvikvas 21-09-2022, 16:38 | : 0 |
: MATLAB Deep Learning Toolbox Users Guide (R2022b)
: Beale M.H., Hagan M.T., Demuth H.B.
: The MathWorks, Inc.
: 2022
: PDF
: 4452
: 94,7 Mb
: English

Deep Learning (DL) is a branch of Machine Learning (ML) that teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to learn information directly from data without relying on a predetermined equation as a model. Deep Learning is especially suited for image recognition, which is important for solving problems such as facial recognition, motion detection, and many advanced driver assistance technologies such as autonomous driving, lane detection, pedestrian detection, and autonomous parking.
Deep Learning Toolbox provides simple MATLAB commands for creating and interconnecting the layers of a deep neural network. Examples and pretrained networks make it easy to use MATLAB for deep learning, even without knowledge of advanced computer vision algorithms or neural networks.
: vitvikvas 21-09-2022, 16:16 | : 0 |

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