Convolutional Neural Networks Swift Tensorflow - Programming Ebook

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Sunday, February 7, 2021

Convolutional Neural Networks Swift Tensorflow

Convolutional Neural Networks Swift Tensorflow
Convolutional Neural Networks Swift Tensorflow




Book Details 
             TitleConvolutional Neural Networks Swift Tensorflow
      ISBN-10942878347
         Author: Brett Koonce
    Language: English
        SubjectSwift / Computers & Technology / Programming / Apple Programming
No. of pages: 254
         Format: PDF, EPUB

Convolutional Neural Networks with Swift for Tensorflow

Image Recognition and Dataset Categorization 


Introduction

In this book, we are going to learn convolutional neural networks by focusing on the specific problem of image recognition, using Swift for Tensorflow and a command-line Unix approach. If you are new to this field, then I would suggest you read the first few chapters and get a working system bootstrapped and then spend your time going through the basics with MNIST and CIFAR repeatedly, in particular familiarizing yourself with how neural networks work. If you feel comfortable with the core concepts already, then feel free to skip ahead to the middle where we explore some more powerful convolutional neural networks.

Why Swift

The short version is that I believe swift is a modern, open source, beginner- friendly language that has proven itself by solving real problems for
iOS developers daily. By integrating automatic differentiation into the programming language, a number of interesting compiler techniques to address the limitations of current machine learning software and hardware become possible in the long term. This is in my opinion where the world is headed, one way or another.

Why image recognition

Image recognition is one of the oldest, most well-understood uses of neural networks. As a result, we can introduce the basics and then build up to advanced state-of-the-art approaches in a logically consistent manner. With this foundation, you will be able to branch out to tackle

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InTroduCTIon

other image-related tasks (e.g., object detection and segmentation) easily. The deep learning techniques needed to build large-scale convolutional neural networks translate easily to reinforcement learning and generative adversarial networks (GANs), two important areas of modern research. In addition, I believe this foundation will make it easy to make the transition to time sequence models such as recurrent neural networks (RNNs) and long short-term memory (LSTM) once you have mastered CNNs.

Why CLI

Broadly speaking, this book is going to focus on a command-line interface (CLI)–based approach using both a local machine on your home network and virtual machines in the remote, Google Cloud. This is in my opinion the best approach because

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  • We can control costs very effectively. In the worst-
    case scenario, you can perform the majority of your work using a local machine built for under a thousand dollars, and your only remaining cost will be electricity and time.

  • We can scale easily from anywhere in the world.
    Using cloud instances full time can quickly become expensive, and so many people avoid learning cloud workflows. But using on-demand cloud-based resources periodically to augment your local workflow means you can learn the cloud in a very practical and efficient way. Eventually, you will be able to prototype and build solutions on your primary machine, then quickly scale them up in the cloud to parallelize computation and access more powerful hardware when needed or available.

• We can get the best of both worlds. While minimizing costs is certainly important, I have found that focusing on how much money you are spending tends to produce a mindset where you are afraid to try new things and experiment in general. Building your own machine puts you into the mindset of putting in more cycles to reduce your costs, which is in my opinion the key to success.

So, toward this end, we will utilize a command-line workflow with the following goals:

  • We will use a local terminal interface to log in to all of our machines, so that there is literally no difference between our approaches on the desktop and in the cloud.

  • We will utilize the same operating system and software locally and in the cloud so that we do not have to
    learn about differences between platforms. Then, by definition, any workflow you can do on your computer, you will be able to do in the cloud, and vice versa.

    Ultimately, by blurring the line between your personal computer and the cloud, my goal is for you to understand that there is fundamentally no difference between doing things locally or remotely. The real limiting factor then is your imagination, not resources.

    Doing things this way will be more work at first, I will admit. But once you have mastered this workflow, it will be much easier for you to scale
    in the future. If you are willing to put in the time now, this approach will make your skills much more flexible and powerful in the future. What you do with them is up to you. 


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