Top 5 Reasons to Learn TensorFlow


Top 5 Reasons to Learn TensorFlow

Artificial Intelligence or AI has gone far beyond its beginning and self-driving cars, virtual assistants, Google Lens, and personalized marketing, are some of the initial powerful applications of AI. The present generation is yet to witness the true potential of AI in the years to come. Companies are investing huge amounts of money to leverage the power of AI and build applications that offer best-in-class solutions to real-world problems.

Broadly speaking, AI is one of the powerful fields of computer science which focuses on making machines capable of mimicking human actions. Such machines are not explicitly programmed and it learns from past experiences to make decisions quite similar to what humans do through their brain. When one starts exploring AI, it is inevitable to come across its subset i.e. Machine Learning. Further deep learning is a subset of machine learning.

This article particularly focuses on Deep Learning, its powerful library TensorFlow, and why professionals prefer achieving TensorFlow certification for better career opportunities in this field.

What is Deep Learning?

Deep Learning is a field that enables machines to think and learn just the way humans do from large amounts of data through artificial neural networks and algorithms. The artificial neural networks are designed like a human brain incorporating artificial neurons connected like a web. These networks, also known as deep neural networks, are capable of learning from unstructured data and responding to complex scenarios much faster than humans.

Traditionally, artificial neural networks were limited by computing power and were not complex in design. Also, as the data generated was quite limited a decade ago, training the networks wasn’t feasible. However, with the advent of big data analytics, today it is possible to gather a massive amount of data and allow deep learning algorithms to learn from it.

Deep Learning is being leveraged in accomplishing various complicated tasks like image recognition, speech recognition, automated driving, detecting cancer cells automatically (medical research), speech translation, automated customer service, and more. To accomplish deep learning tasks, including the ones mentioned here, we use deep learning libraries. Torch, TensorFlow, Keras, Theano, and DeepLearning4J are some of the popular deep learning libraries available in the market.

Let us know more about TensorFlow.

What is TensorFlow?

Developed by the Google Brain Team, TensorFlow is an undisputed leader among the various libraries used for deep learning-powered applications. The official website describes TensorFlow as an open-source platform that consists of a comprehensive, flexible ecosystem of tools, libraries, and community resources that allow developers to build and deploy machine learning and deep learning applications.

Some of the top features that make TensorFlow a preferred library among developers are:

  • It offers multiple levels of abstraction and various APIs that makes model building easy.
  • Models can be trained using different programming languages like Python, JavaScript, or Swift.
  • TensorFlow supports various platforms for deploying ML models, be it desktop, mobile, web, or even cloud.
  • It offers TensorFlow Extended (TFX) to deploy a production-ready ML pipeline for training and inference.
  • Being an open-source platform, TensorFlow is backed by huge community support where one can interact with developers, problem solvers, and tinkerers and share their ideas.

Are you among the thousands of aspirants willing to make a career in AI? If yes, then learning TensorFlow can help you demonstrate your skills in this domain and grab the attention of hiring managers. So let us know about the various benefits of learning TensorFlow.

Why Learn TensorFlow?

Here are some of the top reasons to consider learning TensorFlow.

1. One of the most preferred frameworks

TensorFlow is one of the most loved frameworks among developers, as per StackOverflow Developers Survey 2020. Around 65% percent of the surveyed respondents have expressed their interest in continuing to develop models using TensorFlow. Also with Google’s support, the library will be enhanced regularly to fulfill the growing needs of developers.

2. Build a strong foundation of deep learning

You may have been trained about deep learning and how to build ML and deep learning models. But only studying will never help you unless you apply the concepts practically. TensorFlow gives you that platform, and when you gain experience of using it, you’ll understand what problems are encountered while designing models to solve real-world issues.

3. Get a chance to work on ML-powered projects

Airbnb, Google, Intel, Coca Cola, GE Healthcare, and Twitter are some of the top companies using TensorFlow. As it is an open-source library and developed by Google, it has wide acceptance across companies. If you are well-versed in using TensorFlow, you’ll become a go-to employee for machine learning projects and get a chance to validate your skills.

4. Better salary prospects

According to Payscale, a machine learning engineer with deep learning skills earns an average annual salary of $112,331 in the US. With experience, such professionals can earn even more and even entry-level professionals can command high salaries. Learning TensorFlow will make you capable of designing and deploying deep learning models and validating the same in front of the employers.

5. Better job opportunities

Knowling machine learning is a must-have skill for most of the job opening related data-related roles. Be it data scientist, data analyst, machine learning engineer, or AI specialist, all the roles require a strong understanding of designing ML and deep learning models. Evidently, gaining expertise in TensorFlow allows you to apply for all these roles and enhance your career prospects.

Bottom Line

Time is ripe to explore your career opportunities in AI and machine learning and dive deep into the field. TensorFlow itself provides training programs for beginners as well as intermediates and helps them gain a strong foundation of the four learning areas, namely coding, math, machine learning theory, and how to build an ML project from start to finish. Moreover, there are other training providers who offer TensorFlow courses where one can get proficient in using the library as well as working on deep learning projects. So, enroll today and become a certified deep learning engineer.

Hubs: TensorFlow
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