8 reasons developers are excited about TensorFlow

More and more developers are turning toward TensorFlow for their machine learning projects. The question is, what is TensorFlow? In layman’s terms, the TensorFlow library from Google and GitHub offers high-level abstraction for prototyping and writing error-free algorithms from scratch. But it’s not as easy as it sounds. Implementing end-to-end workflows in a particular framework is hard work, and TensorFlow is no exception.

Many of the elements from TensorFlow have been introduced to the deep learning scene for the first time, which means developers are still in the process of learning the best techniques to take advantage of these features. To make things simpler, we’ve included a list of the eight things you should know about TensorFlow:

The machine learning framework is incredibly powerful

If you’re drowning in data or struggling to grasp the next stage in AI, like neural networks and deep learning, then TensorFlow is a must. This machine learning framework has numerous applications, from screening for diabetic retinopathy to ward off blindness to discovering new planets. In fact, it can be used to alert authorities about illegal deforestation going on in a particular area. Think of it as the foundation level for Google Cloud Vision and AlphaGo.

Also, being an open source ML framework, TensorFlow can be downloaded for free on the Internet. What’s more, you can begin working on this framework immediately.

TensorFlow facilitates community engagement

Normally, anything related to machine learning is viewed in terms of its tech benchmarks, features, and capabilities. However, ask any qualified programmer, and he/she will tell you how difficult it is to write code that is usable by humans, compared to code written for machines to compile and execute. This is where TensorFlow shines.

Almost everyone in the ML community has become acutely aware of this framework. Many are open to the idea of trying it. It’s only a matter of time before enough people understand TensorFlow and start making helpful products. It promotes problem-solving through a collective approach.

For example, many developers have expressed interest in deep learning upon coming in contact with TensorFlow. Recently, Google DeepMind announced the transition from Torch to the TensorFlow framework, thereby signaling the launch of new reinforcement learning models in the coming years. For the first time, the community is looking forward to a future where openness is embraced alongside helpful modules and clean APIs. Also, a more helpful attitude is being noticed on the Internet.

You decide the approach

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In the early stages, TensorFlow was intimidating, to say the least. Developers found it incredibly difficult — impossible even — to code properly. But all this changed with the introduction of TensorFlow Eager. Now, the eager execution enables you to interact with the framework as a Python programmer. So, you get all the associated advantages of writing the code line-by-line rather than waiting around while you create huge graphs. No wonder more and more developers are slowly gravitating towards TensorFlow eager execution now.

Supports the creation of neural networks line-by-line

Let’s get something straight right off the beat – when you combine TensorFlow with Keras API, you are left with one outcome – simple and efficient neural network construction. The thing is, Keras is more or less concerned with simple prototyping and user-friendliness. This was something that was sorely missing in the old versions of TensorFlow. But over time, these issues decreased, culminating in tf.keras. This combination has proven a boon for developers who prefer to build the neural networks layer by layer and adopt an object-oriented approach.

For example, you now possess the power to create an advanced yet sequential neural network with all the basic bells and bells such as dropout using only a handful of code lines.

It involves a lot more than python

If you’ve paid attention to the development of TensorFlow, then you might have heard some of the complaints directed at the framework. One of the most common ones involves the heightened focus of TensorFlow on Python. But the truth is a lot more than that. Now, TensorFlow isn’t just for Pythonistas. The framework supports multiple languages, ranging from Swift to R to JavaScript.

The best part is, if you’ve made some headway into TensorFlow, you’re not required to begin anew from scratch. So, no longer do you have to deal with a new blank page and zero example code for machine learning. Thanks to TensorFlow, you’re able to adopt a better and more efficient model of using someone else’s code and customizing it as per your needs, thereby facilitating software engineering.

Complete all your work in the browser

Thanks to TensorFlow.js, developers now have the unique opportunity to train and execute models directly in the browser. Getting the hang of the process is quite easy, and you will also find lots of unique demos on the subject to speed things along. But be sure to follow the release schedule carefully.

It’s quite challenging to maintain a good open source project with this level of complexity. That’s why you need to track the activities of the maintainers to keep on doing whatever you’re working on uninterrupted. TensorFlow has one of the best strategies for integrating new tests and features first so that early adopters get a taste of what’s to come before documentation.

Experienced a new lite build for smaller devices

If you’re at your wits’ end trying to work on your clunker of a desktop, then it’s time to switch to TensorFlow Lite. This version offers model execution to a host of devices, including Internet of Things and mobile. What’s more, you enjoy a three times more powerful boost to the inference speedup compared to the speed of the actual TensorFlow framework. Thus, it’s now possible to get ML done on your phone or Raspberry Pi.

Take a look at the new, improved data pipelines

If you’ve always wanted to work with TensorFlow but found it difficult to proceed beyond a certain point, then it’s time you check out the tf.data namespace. This allows you to perform input processing in TensorFlow more efficiently and expressively. Why? Because tf.data provides access to data pipelines that are easy to use, flexible, fast, and above all, synchronized with the right training.

The TensorFlow open tool has made waves in the IT industry, and with good reason. Machine learning has never been this easier, and the more you understand the framework, the easier you will find it.

Featured image: Flickr / O’Reilly Internal