Daily Dose for 2017.10.20

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C++ Windows Programming
Troubleshooting iOS
Machine Learning with TensorFlow
Practical Recommender Systems
Machine Learning in Action

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Books/Videos on Sale (or Free) Today

These deals are good for today only, so be sure to take advantage of the pricing before the offers expire.

Free C++ Windows Programming

C++ Windows Programming Free Packt eBook by Stefan Björnander (valid through 10/20 at 19:00 EST). This book covers C++.

Publisher’s Description

It is critical that modern developers have the right tools to build practical, user-friendly, and efficient applications in order to compete in today’s market. Through hands-on guidance, this book illustrates and demonstrates C++ best practices and the Small Windows object-oriented class library to ease your development of interactive Windows applications.

Begin with a focus on high level application development using Small Windows. Learn how to build four real-world applications which focus on the general problems faced when developing graphical applications. Get essential troubleshooting guidance on drawing, spreadsheet, and word processing applications. Finally finish up with a deep dive into the workings of the Small Windows class library, which will give you all the insights you need to build your own object-oriented class library in C++.

What You Will Learn

  • Develop advanced real-world applications in Windows
  • Design and implement a graphical object-oriented class library in C++
  • Get to grips with the workings of the integral aspects of the Win32 API, such as mouse input, drawing, cut-and-paste, file handling, and drop files
  • Identify general problems when developing graphical applications as well as specific problems regarding drawing, spreadsheet, and word processing applications
  • Implement classes, functions, and macros of the object-oriented class library developed in the book and how we implement its functionality by calling functions and macros in the Win32 API

About the Author

Stefan Björnander holds a master’s degree in computer science, and has worked with software development for many years. He has lectured on programming for the industry and universities. He has also authored Microsoft Visual C++ Windows Applications by Example for Packt Publishing, which gained great acclaim.

$9.99 Troubleshooting iOS Solving iPhone and iPad Problems

Troubleshooting iOS $9.99 Apress eBook by Paul McFedries. This book covers IOS, iPhone, iPad.

Publisher’s Description

  • Fix cellular and networking connections
  • Incorporate accessories effectively
  • Solve battery and charging issues
  • Cole up syncing and iCloud glitches

Understand and solve many different kinds of iPhone and iPad problems. This book covers both general troubleshooting techniques applicable in a wide variety of situations as well as specific fixes for topics such as networking, apps, photos, the battery, and syncing.

Glitches, hiccups, and crashes just aren’t supposed to happen with iOS, but alas, all too often they do. It is these non-obvious fixes, workarounds, and preventative measures that form the core of iOS Troubleshooting. With clear, straightforward prose, this book will take the reader through hundreds of iOS problems, explain the reasons for them, and provide easy to understand solutions to get the device (and you) back in business.

What you’ll learn:

  • Fix cellular and networking connections
  • Incorporate accessories effectively
  • Solve battery and charging issues
  • Clear up syncing and iCloud glitches

Who this book is for:

Any person who uses an iOS device.

About the Author

Paul McFedries is a full-time technical writer. Paul has been authoring computer books since 1991 and has more than 90 books to his credit. Paul’s books have sold more than four million copies worldwide. These books include iPhone Portable Genius, iPad Portable Genius, and Teach Yourself VISUALLY OS X El Capitan for Wiley, and Using iPhone, My Office for iPad, and Windows 10 In Depth for Que.

50% off Machine Learning with TensorFlow

Machine Learning with TensorFlow 50% off Manning’s eBook by Nishant Shukla. This book covers Machine Learning, Tensorflow, TensorBoards, Neural Network, Python, Supervised Learning, Unsupervised Learning, Reinforced Learning, Linear Regression, Polynomial Model, Regularization, Classification, K-Means Clustering, Audio Segmentation, Markov Model, Hidden Markov Model, Forward Algorithm, Viterbi Decode, Autoencoder, Convolutional Neural Network, Recurrent Neural Network.

Publisher’s Description

We’re living in a big data world. Being able to make near-real-time decisions becomes increasingly crucial. To succeed, we need machine learning systems that can turn massive amounts of data into valuable insights. But when you’re just starting out in the data science field, how do you get started creating machine learning applications? The answer is TensorFlow, a new open source machine learning library from Google that they use in their own successful products like Search, Maps, YouTube, Translate, and Photos. The TensorFlow library can take your high level designs and turn them into the low level mathematical operations required by machine learning algorithms.

About the technology

Machine Learning with TensorFlow teaches you machine learning algorithms and how to implement solutions with TensorFlow. You’ll start with an overview of machine learning concepts. Next, you’ll learn the essentials you’ll need to begin using TensorFlow before moving on to specific machine learning problems and solutions. With lots of diagrams, code examples, and exercises, this tutorial teaches you cutting-edge machine learning algorithms and techniques to solve them. Each chapter zooms into a prominent example of machine learning, such as classification, regression, anomaly detection, clustering, and neural networks. Cover them all to master the basics, or cater to your needs by skipping around. By the end of this book, you’ll be able to solve classification, clustering, regression, and prediction problems in the real world.

What’s inside

  • Formulating machine learning frameworks for real-world problems
  • Understanding machine learning problems
  • Solving problems with TensorFlow
  • Visualizing algorithms with TensorBoards
  • Using well-studied neural network architectures
  • Reusing provided code for your own applications

About the reader

This book is for programmers who have some experience with Python and linear algebra concepts like vectors and matrices. No experience with machine learning is necessary.

About the author

Nishant Shukla is a computer vision researcher at UCLA, focusing on machine learning techniques with robotics. He has been a developer for Microsoft, Facebook, and Foursquare, and a machine learning engineer for SpaceX, as well as the author of the Haskell Data Analysis Cookbook.

50% off Practical Recommender Systems

Practical Recommender Systems 50% off Manning’s eBook by Kim Falk. This book covers Recommender Systems.

Publisher’s Description

Practical Recommender Systems goes behind the curtain to show you how recommender systems work and, more importantly, how to create and apply them for your site. After you’ve covered the basics of how recommender systems work, you’ll discover how to collect user data and produce personalized recommendations. Next, you’ll learn how and where to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, this hands-on guide covers scaling problems and other issues you may encounter as your site grows.

About the technology

Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Using behavioral and demographic data, these systems make predictions about what users will be most interested in at a particular time, resulting in high-quality, ordered, personalized suggestions. Recommender systems are practically a necessity for keeping your site content current, useful, and interesting to your visitors.

What’s inside

  • Practical introduction to recommender system algorithms
  • Collaborative and content-based filtering
  • Creating individual recommendations from visitor data
  • Real-world examples of recommender systems

About the reader

This book assumes you’re comfortable reading code in Python and have some experience with databases.

About the author

Kim Falk is a Data Scientist at Adform, where he is working on recommender systems. He has experience in providing recommendations for large entertainment companies and working with big data solutions.

50% off Machine Learning in Action

Machine Learning in Action 50% off Manning’s eBook by Peter Harrington. This book covers Machine Learning, Python, NumPy, K-Nearest Neighbors, Decision Trees, Matplotlib, Naive Bayes, Logistic Regression, Vector Machines, SMO Algorithm, AdaBoost, Linear Regression, Tree-Based Regression, Tkinter, Unsupervised Learning, K-Means Clustering, Apriori Algorithm, FP-Growth, Principal Component Analysis, Singular Value Decomposition, Big Data, MapReduce, Hadoop, mrjob, Pegasos Algorithm.

Publisher’s Description

Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You’ll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

About the book

A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.

Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you’ll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You’ll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.

Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.

What’s inside

  • A no-nonsense introduction
  • Examples showing common ML tasks
  • Everyday data analysis
  • Implementing classic algorithms like Apriori and Adaboos

About the reader

Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.

About the author

Peter Harrington is a professional developer and data scientist. He holds five US patents and his work has been published in numerous academic journals.

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