Free computers and the internet Kindle books for 14 Apr 18

Powershell: The Ultimate Beginners Guide To Learn And Understand Powershell Programming Effectively

by Zach Webber

There are a lot of coding languages that you can learn how to work with, but if you would like to work with a language that goes well with the Windows operating system and helps you create your own programs, PowerShell is the best option that you can go with. This guidebook will spend the time to carefully go about explaining PowerShell and how you can get started on using this in no time.

There are a lot of different things that you can do when it comes to working with PowerShell. Some of the topics that we will take a look at inside this guidebook are:
-What is the PowerShell language
-How to work with your own commands
-How to work with outputs and pipelines inside of PowerShell
-The operations and the wildcards
-How to work with strings and quotes in PowerShell
-What the drives and providers are all about.
When you are ready to learn a new programming language, make sure to check out this guidebook and learn how to work with the PowerShell programming language.

Forget Big Brother We Tell DAD Everything

by D T Pollard

For decades there has been this fear of an imposed surveillance state imposed by the government. Ironically we find ourselves voluntarily self reporting our activities, not to a character like Big Brother in George Orwell’s book Nineteen Eighty Four, but to massive social networking websites that the author refers to as DAD. How did individuals that once fiercely guarded their privacy agree to self report their activities for the world to see? As it turns out, we tell DAD everything.

Deep Learning: 2 Manuscripts – Deep Learning With Keras And Convolutional Neural Networks In Python

by Frank Millstein

Deep Learning – 2 BOOK BUNDLE!!

Deep Learning with Keras

This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition and much more.

Furthermore, you will get familiar with recurrent neural networks like LSTM and GAN as you explore processing sequence data like time series, text, and audio.

The book will definitely be your best companion on this great deep learning journey with Keras introducing you to the basics you need to know in order to take next steps and learn more advanced deep neural networks.

Here Is a Preview of What You’ll Learn Hereâ?¦

  • The difference between deep learning and machine learning
  • Deep neural networks
  • Convolutional neural networks
  • Building deep learning models with Keras
  • Multi-layer perceptron network models
  • Activation functions
  • Handwritten recognition using MNIST
  • Solving multi-class classification problems
  • Recurrent neural networks and sequence classification
  • And much more…

Convolutional Neural Networks in Python

This book covers the basics behind Convolutional Neural Networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy to understand way. It is perfect for any beginner out there looking forward to learning more about this machine learning field.
This book is all about how to use convolutional neural networks for various image, object and other common classification problems in Python. Here, we also take a deeper look into various Keras layer used for building CNNs we take a look at different activation functions and much more, which will eventually lead you to creating highly accurate models able of performing great task results on various image classification, object classification and other problems.
Therefore, at the end of the book, you will have a better insight into this world, thus you will be more than prepared to deal with more complex and challenging tasks on your own.

Here Is a Preview of What You’ll Learn In This Bookâ?¦

  • Convolutional neural networks structure
  • How convolutional neural networks actually work
  • Convolutional neural networks applications
  • The importance of convolution operator
  • Different convolutional neural networks layers and their importance
  • Arrangement of spatial parameters
  • How and when to use stride and zero-padding
  • Method of parameter sharing
  • Matrix multiplication and its importance
  • Pooling and dense layers
  • Introducing non-linearity relu activation function
  • How to train your convolutional neural network models using backpropagation
  • How and why to apply dropout
  • CNN model training process
  • How to build a convolutional neural network
  • Generating predictions and calculating loss functions
  • How to train and evaluate your MNIST classifier
  • How to build a simple image classification CNN
  • And much, much more!

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