AI ML

TensorFlow Tutorial: What is TensorFlow, and How Do AI/ML Professionals Use It?

Written by Contributing Writer
|
Updated on September 30, 2024

Are you interested in learning more about deep learning and artificial intelligence?

Today, we will focus on TensorFlow, a helpful tool that any machine learning professional can use. Whether you are simply a student who wants to expand your knowledge or a professional who has decided to improve existing knowledge, this article is for you. Also, as a bonus, we will share an AI and machine learning bootcamp toward the end of this article that can help you get started on your AI and ML journey!

Let’s get started.

What Is Deep Learning?

(Source: DALL-E generated image)

Before running through TensorFlow, let’s discuss deep learning. One can understand deep learning as a subfield of machine learning, which, in turn, is a subfield of artificial intelligence. What is quite interesting is that deep learning employs layered neural networks for different data analysis characteristics. This is a process like removing layer after layer of an onion until the center is revealed.

Also Read: What Machine Learning Skills Are Companies Looking For?

An Overview of Deep Learning Libraries

Today, stable libraries are necessary for deep learning tasks. These libraries are sets of tools and implemented routines that help us when we do not want to reinvent the wheel. Some of the widely used deep learning frameworks are PyTorch, Keras, TensorFlow, and many more. All have their peculiarities, though TensorFlow can be considered the most versatile and all-encompassing.

What Is TensorFlow?

So, what is TensorFlow? Let’s put ourselves in a situation where you are a developer of some of these state-of-the-art machine learning techniques. You require software that is effective in solving general and special mathematical problems. Well, TensorFlow is the answer for that situation. Coded by the Google Brain team, TensorFlow is an open-source library for numeric computation and machine learning. Imagine it as the multifunctional knife for developers, with all the main options for solving different AI issues.

TensorFlow is not an average tool; it’s an engine that one can use to develop and train powerful models to recognize objects, comprehend the text, and produce innovative products, including music and art. TensorFlow is for you, from casual AI enthusiasts experimenting with minimal AI projects in their leisure to teams or organizations implementing massive systems in international organizations. Since it can be used in every situation, every developer, junior and senior, can take advantage of the tool.

In other words, TensorFlow creates a common ground for incorporating machine learning into products, mirroring its name. It allows everyone, from a student who is attempting to create an AI for the first time to an experienced engineer who is working on projects that require the application of this type of algorithm. The interactive help and shared TensorFlow resources are vast, extending developers’ capabilities and encouraging them to experiment in boundlessness. Therefore, when considering making a machine learning project next time, remember that your dream projects are possible with TensorFlow.

Also Read: Machine Learning Engineer Salary: Expected Trends in 2024

Why Use TensorFlow?

Why TensorFlow can be used preferentially against other libraries? The answer to this is very simple: social media is versatile and enjoys lots of support from the public. To consistently work on a platform that ranges from the desktop environment to the mobile environment, TensorFlow is incredibly adaptable. Also, it has a rich history, abundant reference material, and a rather active community, so you won’t be left hanging for long. Besides, due to TensorFlow’s capability of dealing with big data and operations, it is preferred by many. Of course, one cannot ignore TensorFlow’s compatibility with other Google services, which can yield even better results when incorporated into your machine learning applications.

TensorFlow Tutorial: Key Features of TensorFlow

Flexibility and Control: TensorFlow is very different from other sophisticated high-level libraries. For example, it provides almost limitless freedom and control in building a model. TensorFlow is a framework that allows it to build and change machine models based on individual small parts, so you have tighter control over what the model is doing. This makes it well suited for the use cases in Research and Development where one needs to tinker with the model architectures.
Extensive Tooling: TensorFlow has numerous tools for different objectives of machine learning processes. Such components are TensorBoard for data visualization, TensorFlow Model Analysis for bias detection in machine learning models, and TFX for a complete ML pipeline. This tooling reduces the need to use numerous third-party software packages for development, thereby Cutting down the developmental process.
Scalability: One of TensorFlow’s most significant advantages is that it is suitable for learning simple models on a single machine and large, complex models in large clusters or on special hardware such as GPUs and TPUs. This Scalability makes TensorFlow promising for the project’s future since one can start with a small project and then upgrade to a larger one without having to change shortcodes or programs.
High Performance: TensorFlow is optimized for performance from the ground up. It can deal with a large amount of data and intensive computation, which is beneficial to deep learning, which demands massive data and huge computation. TensorFlow used in the training process involves H/W acceleration, thus allowing for faster training and optimal resource use.
Ecosystem and Community Support: TensorFlow is one of the most open-source communities with a supporting cast of tools in the machine learning field. There is hardly a task without a module—from mobile application development (TensorFlow Lite) to the device on the edge (TensorFlow. js). Also, TensorFlow has a rich community of active developers, researchers, and industry experts who work besides contributing to many documents, tutorials, and community platforms for constant support and development.
Integration Capabilities: TensorFlow’s incorporation features greatly boost its operation. It can easily be combined with other Google services, such as Google Cloud and Google Colab, providing computation and storage functions. This compatibility makes integrating your models into your production convenient and gives you an all-around approach to managing your machine learning cycle.
Pre-Trained Libraries: Anyone familiar with deep learning must have heard of pre-trained models and transfer knowledge. TensorFlow’s library contains a set of ready models for many tasks that require less time and effort to train. Furthermore, transfer learning support enables you to proceed from these bases and optimize the models at your discretion. This feature is especially useful when the amount of data is small or if you need a rapid start to give the model a performance boost.

Also Read: How Does AI Work? A Beginner’s Guide

What is TensorFlow, and What are Its Components?

To understand TensorFlow better, let’s break down its main components:

Tensors are single and multi-dimension arrays in TensorFlow basic data types similar to those used in MATLAB.
Graphs define the computation scopes or tasks the computation is to tackle.
Definite scope/ unmistakable tasks define the computation when creating your model. I would compare it to the blueprint of the building you will construct.
Sessions articulate the graphs. They are the panorama where the computation takes place.

The components mentioned above are collectively designed to assist you in building and training ML models. Imagine building a house: In this case, tensors are your base metals, graphs are your blueprints, and sessions are your construction.

Linear Regression Using TensorFlow

Linear regression is ranked among the simplest and most basic algorithms in machine learning. It is applied to estimate a single value measured on a continuum from the input variables. In TensorFlow, implementing linear regression is not a big deal. With TensorFlow, you decide the variables to use, design the cost function, and then tune the cost to as low as possible through the set of optimizers. Sometimes, it feels like working on a simple recipe – gather your ingredients (data), apply the recipe (algorithm), and adjust it as necessary. This keeps linear regression rather basic and makes it a good starting point for people who wish to get used to TensorFlow briefly.

TensorFlow Tutorial: An Example of a Simple Linear Regression Model

Let’s illustrate it using an example of house price and size. Features will be derived from the problem and include house size, while labels will include house prices.

With TensorFlow, you can create a relatively simple linear regression model that will help you identify the most suitable pattern regarding the correlation of said variables. The model will be built to decrease error as input sizes on the material to be forecasted are introduced to enable the accurate prediction of prices using it.

What is RNN?

(Source: AWS)

RNN stands for recurrent neural network. It should be noted that RNNs have connections that construct directed cycles, as opposed to neural networks. This allows them to hold a ‘state’ of the previous inputs, hence ideal for sequential data such as time series or natural language data. suppose you must grasp a word in a sentence and understand that it has come after several words—RNNs. For this reason, ensure such a thing does not happen.

There are several types of RNNs, each suited to different tasks:

Basic RNN: This form is the simplest one and it can handle long-term dependencies only with great difficulties.
LSTM (Long Short-Term Memory): Intended to store information for lengths of time, ideal for the type of problem this is, language modeling.
GRU (Gated Recurrent Unit): It’s similar to LSTM, but it is a more efficient one generally used for similar tasks, though it is slightly less complex.

RNN Use Case

Consider a practical example of RNNs – When you speak to your phone to dictate a message, RNNs take in the data in sequence and learn to identify patterns and what succeeding words might be from the previous ones. That sequential understanding, therefore, is central to an accurate transcription. It also has great use in tasks like live translation or virtual customer service, which requires actual context comprehension, without which an adequate response cannot be provided.

Also Read: Deep Learning vs. Machine Learning: Everything You Need to Know

Applications of TensorFlow

TensorFlow’s applications are vast and varied:

Image Recognition: For vision-oriented jobs, TensorFlow is the best place to start. It can perform photo recognition and facial recognition. Who could think of an application that can easily identify plant species just by photographing them? TensorFlow offers this with almost impeccably precise precision.
Natural Language Processing (NLP): Applied in chatbots, translation, and sentiment analysis, TensorFlow teaches machines to understand human language. For example, an intelligent assistant that does not only recognize your voice and command but also the emotional context for more effective answers.
Recommendation Systems: Have you ever got show suggestions on Netflix? TensorFlow enables these smart recommendation systems. Using TensorFlow, many businesses can create better, more individualized services that attract clients and increase their satisfaction.
Healthcare: The next key application of TensorFlow is diagnosing diseases from medical images and patient parameters. In other words, deep learning will contribute to early diagnosis and, in equal measure, create an efficient treatment plan, hence saving lives.
Financial Services: Regarding prediction, TensorFlow is super-rich; indeed, it is one of the most sophisticated fraud detection models to date, even for the stock market. The financial industry requires models that can determine the risk of the investment and the strategy’s potential outcome, which is why TensorFlow is used.

What is TensorFlow? Real-World Case Studies

Healthcare Innovations: TensorFlow has been useful in the healthcare sector in several revolutionary applications. For instance, by applying TensorFlow, models that predict Diabetic Retinopathy have been created by assessing images of the retina. This has enabled patients to be identified and treated early, thus reducing significantly the likelihood of severe complications.
Autonomous Vehicles: Self-driving cars run TensorFlow for real-time decisions and driving. TensorFlow, the leading software for these autonomous vehicles, helps these vehicles extract from several sensors and cameras, recognize obstacles, and learn safe ways to maneuver independently. Many organizations, such as Waymo, rely on TensorFlow to enhance complex algorithms that make self-driving possible.

TensorFlow Tutorial: Getting Started with TensorFlow

Before you begin your TensorFlow experience, you’ll be glad to know that this is not as complicated as one might imagine, especially with all the readily available resources. TensorFlow has a website section dedicated to providing documentation, tutorials, and guides for beginners and professionals. Finding a new project to work on is simple. Then, start with a fundamental guide and move toward a more complicated one upon enriching your knowledge.

Installing TensorFlow

Before installing TensorFlow, ensure you have Python installed on your computer. If not, go ahead and install it.

You can then use a package manager for Python known as <pip to install TensorFlow>. However, Anaconda, which is a data science platform, has TensorFlow pre-installed, and you can use it right away!

AI and Machine Learning Resources

Here, it is necessary to mention that there are more ways to study and practice TensorFlow, except for the above-mentioned official documentation. This is in the form of online classes, books, YouTube videos, and online forums where users can discuss various topics.

This brings us to an AI and machine learning program where you can get structured training on core machine learning concepts and work on 25+ hands-on projects over 11 months.

So why wait? Begin practicing AI and machine learning with TensorFlow today and revolutionize AI solutions for your projects and applications.

FAQs

What is TensorFlow used for?

TensorFlow is used for various machine learning tasks, such as image and speech recognition, natural language processing, and predictive analytics. It’s widely adopted in industries like healthcare, finance, and technology

Why is it called TensorFlow?

TensorFlow comes from its operations on multidimensional data arrays, called “tensors.” The “flow” refers to data flow through these operations, creating a computational graph.

Why TensorFlow is better than other frameworks?

TensorFlow is considered superior due to its flexibility, scalability, and strong community support. It supports multiple platforms and languages, making it very good for various applications. Its efficient handling of large data sets and complex computations makes it a top choice for developers.

Is TensorFlow difficult to learn?

While TensorFlow has a steep learning curve for beginners, its comprehensive documentation, tutorials, and community support make it accessible. With consistent practice and study, you can master TensorFlow and leverage its powerful features.