What is TensorFlow? The Popular Machine Learning Library Explained

What is TensorFlow? 

Well, TensorFlow is a popular machine learning library developed by Google. As you know, machine learning is a complex stream. Training and implementing machine learning algorithms and models is difficult than it seems. But Google’s TensorFlow made it easy. By using this collecting data, training models and predicting future outcomes become easy. TensorFlow is an open-source library used for large numerical computation. Its main application includes large-scale or complex machine learning. 

TensorFlow bundles both machine learning and deep learning models and algorithms. It makes them useful by bringing under a common platform. It also provides a great front-end API for application development. TensorFlow used high-performance C++ for the execution of those applications. TensorFlow can train and run the deep neural network models. These models may be of image analysis, face detection, recurrent neural networks, and more. It also supports production prediction with the use of training models. 

Working of TensorFlow

TensorFlow software library enables developers to create dataflow graphs. Data flow graphs are the structures that show how data moves through the graph of nodes. Each node is a mathematical operation, and the connection between them is a tensor. Also called the multi-dimensional array of data. Python eases these features through itself. It is easy to learn the language, and it has good ways to express high-level abstraction. Tensors and nodes in TensorFlow are python objects. Its uses are python applications. 

Yet the math operations are not performed by the python. It has many specific libraries to do that. These are high-performance C++ binaries. The job of the python is to manage the traffic between the works. TensorFlow is compatible and can run on many devices. To name a few a local machine, cloud, iOS, and Android devices. If you want to use GCP, then you can run TensorFlow on Google’s TensorFlow processing unit. Usually, it is fast compared to others.The TensorFlow models can get deployed in any device for some predictions. Google released the TensorFlow 2.0 version in 2019 in October. This revamped library is better based on customer feedback. 

The best part is the introduction of Keras API to ease the work of model training. TensorFlow 2.0 supports various platforms to deploy a model. You should change code when you are using a 2.0 version. Sometime it will be slight changes and sometimes it will be major changes. 

Benefits of TensorFlow for Machine Learning

The best benefit of TensorFlow for machine learning is Abstraction. TensorFlow won’t look for small details like algorithms, output ideas and more. TensorFlow allows the developer to concentrate on the overall logic of the application. It takes care of the back-end operations strongly. It offers additional features for developers to dig deeper into TensorFlow apps. The eager execution helps you to analyze each graph individually. The Tensorboard envision enables you to inspect the way of graphs. This inspection is in terms of the collective and web-based dashboard.

TensorFlow offers many advantages to being a product of Google. It was not only developed for common usage, but it offers some best things. With TensorFlow, you can train and deploy the model in a way that is easier and faster. The above mentioned TPU silicon suits best for this purpose. With TensorFlow some details of execution, the training of the model becomes difficult. Even though you have the same data, you cannot get the same result when using systems. But the TensorFlow team is on the mark and minimizes all the negatives. 

TensorFlow Vs Rest of the Frameworks

TensorFlow competes with a bunch of ML frameworks. Among them, PyTorch, CNTK, and MXNet are the major and popular ones. These frameworks also address the same conditions, but they fell just short.

We have briefly explained the PyTorch, Microsoft Cognitive Toolkit, and MXNet frameworks below. 

1. PyTorch

PyTorch is a python product. It also works similarly to TensorFlow. It offers highly collective model development, where you can design as you move. PyTorch is a good choice for faster development. But TensorFlow wins the battle with complex projects and development time. 

2. Microsoft Cognitive Toolkit (CNTK)

It is Microsoft’s cognitive toolkit, like TensorFlow, it is the graph to describe the data flow. CNTK focuses more on deep neural networks. It includes a wide range of API like the python, C++, Java, and #C. However, CNTK is more complex to learn, and deploy the models. 

3. Apache MXNet

Amazon uses MXNet as a deep learning framework in its AWS platform. It can scale across multiple GPUs and machines. It also supports various language APIs like the python, Java, C++, #C, R, JavaScript, Julia, and Perl. However, the basic APIs of MXNet is not as convenient as TensorFlow.