Artificial intelligence systems are now finding their way into societal norms more and more each year, and with that comes a profusion of modern advancements in machine learning applications. Human technology has advanced exponentially in the last few decades alone, and scientists are estimating the digital era to become more saturated with groundbreaking innovations in the fifty years.

With new technologies being invented, there is new learnings as well. In this article, we’ll see how to learn TensorFlow from Scratch.

It is still relatively early to determine whether these applications will evolve even further to be free of human interaction. With the progress at which machine learning is advancing, that might just be a reality soon. One of the renowned and prestige-filled names among machine learning applications is TensorFlow, and today, you’ll be learning about the nature and intricacies of what it takes to learn the platform. 

What is TensorFlow?

Many of you might be thinking that answering the question “How to learn TensorFlow from scratch” is as easy as watching a 3-minute video on YouTube. While that may be viable as an introductory approach to the complex framework and platform, the wise decision would be to study further and better understand why it is one of the top choices for large-scale machine learning and extensive numerical computations in a library.

Machine learning is a pretty complicated and intricate discipline to learn but expressing data inside specific platforms is significantly less daunting than most people think. In its simplest form, TensorFlow is the brainchild of Google and the talented team behind their famous “Google AI” organization. The platform is used in the selection of specific libraries for numerical composition, as well as machine learning and deep learning frameworks.

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Artificial intelligence tools are constantly evolving daily, and TensorFlow is an application that is updated regularly due to its features and popularity. Python is used for primary API in building applications within the framework, while high-performance C++ is utilized to execute operations.

TensorFlow gives developers and programmers the ability to create dataflow graphs which are structures and models that provide descriptions on how data goes through charts, or in other words, processing nodes. Every node represents a mathematical computation while each node connection is a multi-dimensional data array or “tensor,” hence the name TensorFlow.

It should also be noted that professionals who use TensorFlow as their main library and application state that training and operating networks for various scenarios is a primary attraction. However, taking the time to memorize all the tools will also benefit users in the long run, especially when efficiency during productivity is needed.

Why is TensorFlow So Popular?

Anyone looking to learn TensorFlow from scratch should understand why it became so popular in such a short period. In the grand scheme of things, popularity and positive reception among forums such as Stack Overflow, Quora, and GitHub, have vastly contributed to TensorFlow’s prestige and the Google branding is just a long-standing promise of quality. 

As an AI framework, TensorFlow has been selected in thousands of open-source archives, which shows core usage in various real-world situations and scientific domains. How does one exactly learn TensorFlow from scratch? Well, the answer lies in observing scenarios where it is chosen over its competitors.

It’s already evident that many companies have noticed the advantages of using TensorFlow for their research and development areas. Artificial Intelligence tasks are just more manageable on the platform, and popular names such as NVIDIA, Twitter, Snapchat, and Uber have proven that by relying on TensorFlow exclusively.

Choosing The Best Courses to Learn TensorFlow for Beginners

TensorFlow is one of the leading choices for frameworks and libraries in the market today, and several trusted courses with certification follow its prominence. You will find that getting familiar with a few of these will help you conclude your “How to learn TensorFlow from scratch” search. Here are some of the best courses for TensorFlow in 2021:

• Intro to Machine Learning with TensorFlow – Udacity
• TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning – Coursera
• TensorFlow Deep Learning Certification Course – edX
• TensorFlow in Practice Specialization – Coursera
• TensorFlow Data and Deployment Specialization – Coursera
• Advanced Machine Learning with TensorFlow – Coursera
• Complete Guide to TensorFlow for Deep Learning with Python – Udemy
• Deep Learning with TensorFlow – edX
• Free TensorFlow Tutorials & Courses – LinkedIn Learning
• TensorFlow for Deep Learning – Udacity

The TensorFlow Approach

Understanding anything related to artificial intelligence will always require a significant amount of your time. However, taking the popularity and diversity of TensorFlow’s nature and incorporating it into your approach to learn TensorFlow from scratch will divert attention from some of the time-consuming methods you may have seen online.

Step 1: Make sure that you fully understand how graphs regarding computational models work. The overall procedure may be comparable to Numpy, but there are also notable differences. A quick visit to some of the forums above, such as Stack Overflow or GitHub, will allow for a better comprehension of the procedures that most people go through when specifying TensorFlow calculations.

Step 2: Finish all of the tutorials and introductions posted on TensorFlow’s website. It may come off as a cliché method, but no place is better to visit as a beginner than TensorFlow’s official website. After getting familiar with how the platform works, choosing a course will be easier.

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Step 3: Don’t be afraid to explore and tinker with various deep learning algorithms, machine learning, and subsequent developments in learning architectures. Analyzing new outlets to digest meaningful information will help answer specific questions in the future.

Step 4: Dedicate yourself to doing projects and tasks related to TensorFlow. Getting acquainted with different Google communities will also help find like-minded individuals who have the same goal.

Step 5: A basic rundown of what you will be learning in TensorFlow is as follows:

• Using KERAS (Python’s Deep Learning API).
• Loading Data inside TensorFlow’s application.
• Building a framework model, which comes after getting comfortable with Keras and loaded data.
• Familiarizing different neural networks such as Convolutional Neural Networks and Recurrent Neural Networks.
• Understanding the Saved Model method to store created machine learning models.

Summary: TensorFlow Course for Beginners

While it is impossible to determine a sure-fire method in learning TensorFlow from scratch, this article aims to minimize the scope at which you need to scour the internet to achieve this goal. However, like with any other discipline, hard work and honest dedication to getting accurate results are required to succeed in mastering this platform.