Deep Learning With TensorFlow

There is a lot to be said about Deep learning and there is a lot to be said about TensorFlow. Only after that can we understand the total concept of Deep Learning with TensorFlow.

Let’s start with “What is TensorFlow?”

TensorFlow is a Machine Learning Library which is developed by the Brain Team at Google. When it comes to Google TensorFlow this is what the official website has to say:

“TensorFlow is an open source end-to-end platform for machine learning”

Not only this, but TensorFlow also comprises of a comprehensible and flexible system of tools, community resources, and libraries. This combination of TensorFlows abilities allows developers to easily create and deploy Machine Learning applications. It also contains high-level Python API’ for building and training Deep Learning Models.

Now that we have a preliminary understanding of the Google TensorFlow, let's move on to Deep Learning.

Deep Learning is a part of the Machine Learning methods based on artificial neural networks. It is sometimes known as Deep Structured Learning or Hierarchical Learning. There are also three categories of Deep Learning:

Finally, now that we have a basic understanding of each of the concepts of Deep Learning and TensorFlow out of the way, we can move on the real deal: Deep Learning with TensorFlow.

Google TensorFlow, which is a library built for Deep Learning, enables developers to create complex ML applications with ease and accuracy. The main reason for such popularity of Deep Learning with TensorFlow is its applications and uses in anything ML related, be it: Images, Videos, Audion or Text. Any problem faced in the face of these categories can be tackled using the Tensorflow library.

Unlike its competition, Tensorflow was built with generally available processing power limits in mind. It can even run on a general household PC.

Such ease and application results in the increasing popularity of TensorFlow with Deep Learning.

Why Google TensorFlow?

TensorFlow holds superior as compared to other Libraries when it comes to:

  • Easy Model Building
  • Robust Machine Learning Production
  • Powerful Experimentation For Research

Let’s take a deeper look into each.

Easy Model Building

With the help of TensorFlow’s powerful and high-level APIs, developers can not only build but also train Machine Learning Models.

One of the most utilized API is Keras with eager extraction. Using Keras developers can immediate model iteration. It also allows for easy debugging.

Robust Machine Learning

It doesn’t matter which language you use, TensorFlow is able to easily train and deploy models:

  • In Cloud
  • On-prem
  • On-Device

Powerful Experimentation

Google TensorFlow offers an extremely flexible architecture. It allows the rapid development of new ideas and concepts. Coding state of the art models becomes as easy and fast as it gets.

Practical Example Of TensorFlow

One of the biggest and most prominent examples is the way Deep Learning with TensorFlow to enable mobile proof of purchases at Coca-Cola.

Explaining the whole situation which resulted in Coca-Cola using TensorFlow requires a 2000 word article of its own. Keeping the example under the scope of this blog, I will try and explain it as best as I can.

It all started with the loyalty program of Coca-Cola which started back in 2006 when mobile browsing was technically non-existent. A website was created where customers could enter their codes in order to gain rewards.

In 2016 Coca-Cola redeveloped its website for mobile users. Instead of just simply entering the code the users could take and upload it.

Coca-Cola was using off-the-shelf Optical Recognition Libraries (OCR) to analyze the image and read the code. But there was a problem. And not from the user’s side.

The problem was in how the codes were printed on the bottom of the bottle caps. Long story short OCRs were not doing great and Coca-Cola was looking for something that could actually work.

In the past, it was very difficult to develop deep neural networks but it changed when in 2015 Google released an open source library: Google TensorFlow. It was designed to simplify the development of deep neural networks.

You get where I’m going right?

Coca-Cola quickly got to work. This is what Coca-Cola has to say once they were done with the initial development of a neural network:

Any neural network is only as good as the data used to train it. We knew that we needed a large set of labeled product-code images to train a CNN that would achieve our performance goals. Our training set would be built in three phases:

  1. Pre-Launch simulated images
  2. Pre-Launch real-world images
  3. Images labeled by our user in production

And this exactly what they did, the results were more amazing then anyone could have ever imagined. With the help of Google TensorFlow, a decade long issue was resolved.

Advantage of Deep Learning with TensorFlow

There are 5 main advantages of using TensorFlow. I’ll discuss them briefly one by one.

No. 1: Graphs

Google TensorFlow has superior indigenous graph visualization capabilities when compared to some of the other major libraries such as Torch and Theano.

No. 2: Library Management

Since the library is backed by Google, it enjoys frequent updates, seamless performance, and quick updates.

No. 3: Debugging

Developers can even execute the subparts of the graph. This ability of TensorFlow gives it the upper hand as relative discrete data is introduced and retrieved onto an edge. The result is a better and improved debugging method.

No. 4: Scalability

Since this library was developed keeping the generally available computing power in mind, it can be executed on a gamut of hardware machines including even cellular devices.

No. 5: Pipelining

TensorFlow is without a doubt highly parallel. It has the ability to be used with various back-ends software like GPU, ASIC, and more.

Now, that we have reached the end I have something as a gift for you. If you are looking for a small project on which you can practice your TensorFlow expertise then here is a link to a small project: https://blog.carbonteq.com/practical-image-recognition-with-tensorflow/. Enjoy!