TensorFlow – A Quick Guide to the Powerful Deep Learning Framework

If you are intrigued by the idea of Artificial Intelligence (AI) then you probably know about the revolutionary applications of this field, including image processing, object detection and speech recognition. These applications are all part of a subfield of AI known as Deep Learning. Programmers can create these revolutionary systems by implementing concepts of Deep Learning using AI libraries and frameworks, one of them being TensorFlow.

In this article, you’ll get a quick journey into the TensorFlow Deep Learning framework, its workings, features, applications, and how you can implement it in your AI systems.

Deep Learning

Deep Learning (DL) is a subset of Machine Learning, which is a larger subset of AI and data science. DL makes use of algorithm structures derived from human brain functionality. Such algorithms are called Neural Networks (NNs) and they comprise of neurons that make up layers. A typical NN has an input, output, and a lot of hidden layers.

Data is passed through these layers and the NN learns the features of the given data.

What is TensorFlow?

TensorFlow is an open-source Deep Learning framework developed by Google. This math-intensive framework is based on dataflow and differentiable programming and is used to build and train Neural Networks using various tools, libraries, and community resources. As of now, TensorFlow is the leading platform for creating Deep Learning models and Neural Networks.

TensorFlow handles data in the form of multi-dimensional arrays of higher dimensions called tensors, tensors are a useful solution for handling large amounts of data. The framework works based on data flow graphs that have nodes and edges. As the execution mechanism is in the form of graphs, it is much easier to execute TensorFlow code in a distributed manner across a cluster of computers while using Graphical Processing Units (GPUs). It also allows you to construct a flowchart of operations that can be performed on your inputs.

Key Features

• Built to run on multiple CPUs or GPUs and even mobile operating systems.
• Supports several programming languages including Python, C++, and Java.
• Incorporates different APIs to build and scale deep learning architectures like CNN or RNN.
• Uses intuitive high-level APIs like Keras with eager execution.
• Immediate model iteration and easy debugging.
• Supports deployment on the cloud, on-premises, in the browser, or on-device.
• Built-in data loading and handling API.
• Allows for powerful research experimentation.
• Strong and supportive online open-source community.

Applications

There are numerous applications of the Deep Learning library, a small number of which are given as follows:

• Artificial Intelligence applications: chatbots and virtual assistants.
• Computer Vision applications: models for image recognition, object detection and classification.
• Speech Processing applications: systems for analyzing human voice and speech patterns.
• Image Processing applications: models for performing transformation techniques on images.
• Natural Language Processing applications: text-based recognition and sentiment analysis models.

Acquiring TensorFlow

As already stated, TensorFlow is open-source and free to use. Follow the steps below to acquire the framework.

Step 1

For this step, download and install the bootstrap version of pip called ‘get-pip.py’ unless you already have it installed. You can download it here.

Step 2

Open your Integrated Development Environment for Python, Java, C++, or any other programming language used and supported by TensorFlow. You can view the list here.

Now change your directory to the one containing the get-pip.py file and type in the command: py get-pip.py

Step 3

Once the installation is complete, simply type in the command: pip install –upgrade tensorflow to begin installing TensorFlow using pip.

And that’s it. You now have TensorFlow installed and ready to use!

Using Tensorflow

To use the framework, simply import the library using the following command:

You can now use the ‘ tf ‘ command to access different modules of the library. The following is an example of importing AI models from TensorFlow.

And that’s it! Now you should be able to implement TensorFlow in your AI programs with ease.

Conclusion

TensorFlow has truly revolutionized the way we create AI systems and has powerful real-world applications. From building and training ML models to deployment, TensorFlow offers robust resources to create ML projects.

I hope this quick walkthrough helps you bring your ideas to life easily. Let us know your thoughts about this leading framework in the comment section below.