Ideally, What Exactly Does It Mean to Have TensorFlow, Keras or PyTorch Use?
By: Kurt F.
Tags: TensorFlow, Keras, Machine Learning, Data Science, Pytorch

Three Important Frames in Deep Learning

Today, due to digitization and data reaching a large capacity, systems that produce fast solutions and codes that are not difficult to write and read have gained great importance. Machine learning feeds on these conveniences while approaching problems. And TensorFlow-Keras-PyTorch plays a leading role in the creation of these models.

In professional life, the specialties and expectations associated with the above three are reflected in job postings.

What is usually required in these postings?

Knowing about at least one of these three libraries

Creating new application areas for machine learning

Being able to analyze data with Python

Examining and updating existing models

This list may get longer, but this is enough for now. Before I get into the details, I should point out that they are all open source libraries.

1- TensorFlow

TensorFlow, produced by the Google Brain team; It is a library that allows users to develop current technology and machine learning applications. Tools and libraries that can be used to create the code structure serve to create a flexible solution. It also has a serious ecosystem that grows day by day.

TensorFlow facilitates application development by combining specific algorithms for deep learning. However, it cannot be said that it is the easiest language to learn. It might make more sense to get started with the basics to use the other two tools instead.

Who is it easier to use?

Python, C ++ and CUDA users are advantageous. Because TensorFlow is written to read in these 3 languages.

Why is it difficult or not?

In general, using almost any language or library will be more difficult unless you start in the right place. However, the most complained issue for TensorFlow is related to debugging lines based on feedbacks.

It’s also important to note that TensorFlow is a symbolic math library. Of course, it may be more difficult to use for those who are not familiar with the NumPy library. However, this is a big mistake. Because TensorFlow does not bother you with the difficult part of mathematical operations, on the contrary, it saves you time by speeding up the processes for the experiment you will do. And as we stated above, using it with NumPy will generally be more convenient to use.

NumPy is a Python library where you can do advanced math operations, but this library will usually need to be imported for math operations in TensorFlow.

import tensorflow as tf

import numpy as np

2-Keras

It can work on Tensor Flow. It is used for fast experiments with deep neural networks and to design the appropriate model. Generally, being expandable makes it user friendly.

Why Keras?

It has become very popular in scientific articles in recent years. It is possible to draw this conclusion from developments at CERN, NASA and arxiv.org. It has an accelerating effect that will reduce the processing time for users. And, according to TensorFlow, it differs in providing clear and actionable feedback on user error. Allowing machine learning developers who are particularly productive and who want to experiment more to perform more tests smoothly is perhaps the strongest point of it.

Additionally, Keras allows you to use classes and functions without adding any additional code. This means a starter library that is easy for AI enthusiasts.

However, there are some points that are not clearly known:

— For example, when building a neural network model, we can use TensorFlow to do our job. Also, what is the reason to use TensorFlow with Keras in backend applications?

Yes, it has become the standard reaction to believe that one such tool will replace the other after creating the other, because it is based on a similar scenario with many products under development. This is of course a normal and typical question for users. And it is the part that developers should really explain in a more understandable way.

With TensorFlow, Keras has been getting better and better on feeding each other in recent years. So Keras API can integrate very well with TensorFlow. And Keras was not actually designed to stand on its own. Because it does not have the proper code stack.

In this respect, Keras is doing the best thing to do:

So it only functions as an extension that makes it easier to read and write machine learning programs. One of the most basic coding information is that the code should be written cleanly. And that’s why we need APIs. Keras covers this gap well.

And Keras needs to use a library on the backend, which does not include all calculations, to create a model in a final machine learning project, which is mostly TensorFlow.

3- PyTorch

Developed by Facebook’s AI research group, PyTorch is used to design and train neural network-based deep learning models. Python is the more prominent language, but it is also compatible with C ++.

Why PyTorch?

PyTorch provides an opportunity to create prototypes fast like Keras. With PyTorch, it is easier to define and modify graphics. Compared to the code structure in other TensorFlow, PyTorch is more pythonic. It is frequently used in applications such as Natural Language Processing.

However, since it is so new, it cannot be said to be strong enough in terms of the community. Although it is easy to learn and has an accelerating side, it can be said that the newness in terms of scalability and distribution has some disadvantages. TensorFlow seems more experienced in this respect.

The most frequently asked question after TESLA: “Will PyTorch replace TensorFlow in the future?

TESLA uses PyTorchu to develop autonomous engines. This is especially preferred as it facilitates scenario-based learning for trajectory optimization, motion planning, dynamic road building, driver optimization, and highways. This is a new area in which it has peaked in popularity.

In fact, PyTorch is already the referral frame for machine learning researchers today. It is unknown what updates TensorFlow will make in the future. As a result, it doesn’t make sense to put someone else directly in their place. But big companies like Microsoft are using PyTorch more and more every day for machine learning and business productivity. And we can say that PyTorch has made its way to a really good spot among popular frames that will need to be learned.