Whether you’re studying machine learning or are an AI enthusiast, you must’ve heard of TensorFlow. It’s among the most popular solutions for machine learning and deep learning professionals and has become an industry staple.
This means if you want to pursue a career in the field of AI and ML, you must be well-acquainted with this technology. If you’re wondering about questions such as what TensorFlow is and how it works, you’ve come to the right place as the following article will give you a detailed overview of this technology.
What is TensorFlow?
TensorFlow is an open-source library for deep learning. The people at the Google Brain Team had initially created it to perform large computations. It wasn’t created particularly for deep learning. However, they soon realized that TensorFlow was beneficial for deep learning implementations, and since then, they have made it an open-source solution.
TensorFlow bundles multiple machine learning and deep learning algorithms and models. It allows you to use Python for machine learning and offers a front-end API to build applications. You can use C++ with TensorFlow to execute those applications and enjoy high performance.
With TensorFlow, you can easily train and run deep neural networks for various ML applications. These include word embeddings, handwritten digit classification, recurrent neural networks, image recognition, natural language processing, and partial differential equation simulations.
Along with such versatile applications, TensorFlow also lets you perform production prediction at scale as you can use the same models for training.
It accepts tensors, which are multi-dimensional arrays of higher dimensions. They are quite helpful in managing and utilizing large quantities of data.
What are the Components of TensorFlow?
To understand what is TensorFlow, you should first be familiar with the components of this technology:
The most important component in TensorFlow is called a tensor. It is a matrix or vector of multiple dimensions that represent all data types. All the values in a tensor have identical data types with a partially or completely known shape. The shape of data refers to the dimensionality of the array or matrix. All the TensorFlow computations use tensors. They are the building blocks for the software.
A tensor can originate from computation as a result or as the input data for the same. All the operations in TensorFlow take place in a graph. In TensorFlow, a graph is a set of successive computations.
Every operation in TensorFlow is called an op node, and they are interlinked to each other. A graph outlines the connections between the various nodes and the ops. Keep in mind that it doesn’t show the values. Every edge of a node is the tensor. In other words, an edge of a node allows you to populate it with data.
2. Graph framework
Operations in Tensorflow use a graph framework. The graph would collect and describe the different computations taking place during the training. It offers various benefits.
The graphs in Tensorflow make it possible to use the software on multiple GPUs or CPUs. It also allows you to use the software on a mobile operating system. Its portability enables you to preserve the computations for later use. You can save a graph so you can run it in the future, making your tasks much more manageable.
Computations in graphs take place by connecting tensors. Every tensor has an edge and a node. The node carries the operation and generates an endpoint output. The edge explains the input-output relationship between the nodes.
How Does it Work?
You can build data flow graphs by using TensorFlow. A data flow graph is a structure that explains how data moves through a series of processing nodes or a graph. Every node in a graph stands for a mathematical operation.
TensorFlow gives you all of this information to the programming through the Python language. Python is easy to learn and use language. Moreover, it’s pretty easy to explain how you can high-level abstractions together through Python. In Python, the nodes and tensors of TensorFlow are Python objects, and all the TensorFlow applications are Python applications.
However, you don’t perform the actual mathematical operations in Python. The transformation libraries available in TensorFlow are high-performance C++ binaries. Python simply directs the traffic between those pieces and gives you high-level programming abstractions so you can connect them.
Because you can run TensorFlow applications on any target such as Android or iOS devices, local machines, clusters in the cloud, etc., you can run the resulting models on different devices too.
The recent version of TensorFlow, called TensorFlow 2.0, has changed how you can use this technology substantially. It introduced the Keras API, which makes it much simpler to use TensorFlow and offers support for TensorFlow Lite that allows you to deploy models on a larger spectrum of platforms.
The only catch is you’ll have to rewrite the code rewritten for the previous TensorFlow version.
Benefits of using TensorFlow
TensorFlow is among the most popular machine learning and deep learning technologies. The main reason behind its widespread popularity is the various advantages it offers to businesses. The following are the primary benefits of using TensorFlow:
TensorFlow is an open-source solution. This means it’s free to use, which has enhanced its accessibility substantially as companies don’t have to invest much to start using TensorFlow.
2. Use of Graph Computation
Graph computation allows you to visualize a neural network’s construction through the Tensorboard. Through the visualization, you can examine the graph and generate the required insights.
TensorFlow is compatible with various devices. Moreover, the introduction of TensorFlow lite has made it much more flexible as it has become compatible with more devices. You can use TensorFlow from anywhere as long as you have a compatible device (laptop, PC, cloud, etc.).
TensorFlow has many APIs to build at scale deep learning architectures. Moreover, it’s a Google product, giving it access to Google’s vast resources. TensorFlow can integrate easily with many AI and ML technologies, making it highly versatile. You can use TensorFlow for various deep learning applications due to its multiple features.
Learn more about TensorFlow and other AI topics
There are many applications of TensorFlow. Understanding how it operates and how you can use it in deep learning are advanced concepts. Moreover, you must also know the fundamentals of artificial intelligence and machine learning to use this software correctly.
Hence, the most efficient way to learn TensorFlow and its relevant concepts is by taking a machine learning course. Taking such a course will give you access to a detailed curriculum and learn from experts.
upGrad offers the Executive PG Programme in Machine Learning and AI with IIIT-B to help you significantly in learning and understanding TensorFlow.
It’s a 12-month course and requires you to have a bachelor’s degree with 50% marks with mathematics or statistical background and one year of professional work experience in programming or analytics. The program offers 40+ live sessions and 25+ expert sessions to streamline your learning experience.
During the course, you’ll be working on 14 assignments and projects that will help you test your knowledge of AI, ML, and other related subjects. You’ll get peer-to-peer networking opportunities during the program. upGrad has a learner base in over 85 countries. Through this platform, you can network globally and accelerate your career growth significantly.
Along with these advantages, you’ll also receive career coaching, one on one industry mentorship, and just-in-time interviews so you can pursue a promising career in this field.
TensorFlow is a popular AI technology, and if you’re interested in becoming an AI or ML professional, you must be familiar with this software.
TensorFlow uses tensors and allows you to perform graph computations. If you’re interested in learning more about TensorFlow, we recommend checking out the course we have shared above.