TensorFlow is a software library created by Google. It is open-source and primarily used to implement deep learning and machine learning systems. In essence, this library allows a computer to learn spotting patterns and make the best possible decisions from them.
Top Machine Learning and AI Courses Online
At its very core, TensorFlow is all about dataflow programming and leverages different optimisation techniques to make mathematical calculations easier and more intuitive. Some key features that make TensorFlow a supremely efficient tool include:
- Works efficiently with mathematical expressions, especially those involving n-dimensional arrays and vectors.
- Supports deep neural network concepts with ease.
- Provides GPU as well as CPU computing abilities.
- Offers scalability of computations across vast data sets and different machines and systems.
Together, these features make TensorFlow the perfect framework for machine intelligence at a production scale.
This TensorFlow tutorial will help you set up and install TensorFlow. We’ll also talk in-depth about its use cases, features, advantages, limitations, and more!
Trending Machine Learning Skills
Enrol for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.
Installing TensorFlow
In this tutorial, we will be looking at the TensorFlow Python API that works with Python 3.3+. The Linux Only version of this requires the Cuda Toolkit and cuDNN.
First, we will begin by using the Conda package manager to install TensorFlow onto our system. That way, we can separate multiple environments on one system. Learn more about installing Conda from here.
Once you have Conda installed and in place, you can quickly create the environment to be used while TensorFlow is being installed and used. After installing Conda, we can make the environment that we will use for TensorFlow installation and use. The following line of code is essential for setting up the environment with additional important libraries like NumPy etc., which are crucial for working with TensorFlow:
conda create –name TensorflowEnv biopython
To make things simpler for you, you can install biopython, like we have above, instead of just installing NumPy. Biopython in itself includes NumPy and a few other vital packages that will be required while working with TensorFlow. Plus, you can always install packages that you need using the pip install or conda install commands.
Now that our environment is created, we need to activate the domain to begin working. Once we have activated the environment, we will start using different packages it provides without mixing them with other globally installed packages. Here is the line of code that can be used for activating the conda environment:
source activate TensorFlowEnv
One thing to note here is that the pip installation tool is an integral part of the Conda environment. Using that, we will be able to install the TensorFlow library. But before doing that, it is a good idea to update pip to the latest version using the following line of code:
pip install –upgrade pip
Now, simply run the below-mentioned command to quickly install TensorFlow:
pip install tensorflow
The final download of TensorFlow may take several minutes, so be patient. Meanwhile, let’s move ahead and try to understand what Tensors mean.
What are Tensors?
As the tool’s name would suggest, TensorFlow provides primitives for defining functions using Tensors and computing their differentiations. At this point, it is crucial to take a moment and see what Tensors are.
Simply put, tensors are just higher dimensional arrays that can be utilised to represent a massive amount of data in the form of numbers in computer programming terms. NumPy and several other n–d array libraries are available, too, but TensorFlow stands apart by providing methods to create tensor functions that let it automatically find the derivatives.
With that settled, let’s now look at some more uses of TensorFlow.
Use Cases of TensorFlow
TensorFlow makes it easier to build decision trees or k-nearest neighbours and other machine learning algorithms for the best result. The below image represents the entire ecosystem of TensorFlow:
As you can see from the above image, TensorFlow integrates seamlessly and provides dependencies that include Cpp, Python, GPU processing and can be used to integrate with dockers as well.
Now, let’s move on to understand the concept of a TensorBoard in TensorFlow.
TensorBoard in TensorFlow
TensorBoard is simply a collection of visualisation tools provided by TensorFlow for creating seamless, intuitive, and easy-to-understand graphs and visualisations. Using this board, you can also plot different quantitative metrics about the graph and provide it additional information such as images to pass through it.
Operation in TensorFlow
TensorFlow runs on several platforms, and the installation method it follows is more Linux-only, which is slightly tedious than CPU-only installations. However, you can install it using the conda or pip environment. In terms of operations, the TensorFlow application goes well beyond deep learning and machine learning and supports all the different and essential aspects of machine learning like reinforcement learning, supervised learning, and such. These techniques are important for developing goal-oriented applications like helping a robot navigate uneven terrain, winning at video games, etc.
Applications of TensorFlow
TensorFlow comes with many applications that can be used by engineers and scientists alike. To begin with, TensorFlow makes it easy to explore different machine learning strategies like google translate, text summarisation, image recognition, sentiment analysis, and much more.
The software is trusted and employed by industry leaders and giants like Uber, IBM, Dropbox, Airbnb, Facebook, Google, Instagram, and many more. So wherever you need robust machine learning or deep learning algorithms or systems capable of learning independently, you will find TensorFlow in action.
In this TensorFlow beginner tutorial, let’s look at some of the most common applications and use cases of TensorFlow.
Computer Vision (Image Recognition)
One of the most popular uses for TensorFlow is computer vision or image recognition. With its ability to process large amounts of data quickly, it can be used to detect objects in images and videos. This technology has been used in facial recognition software, autonomous vehicles, medical imaging applications, and more. The tensorflow tutorial for beginners helps you to understand the basics of image recognition.
Natural Language Processing (NLP)
Another great application for TensorFlow is natural language processing (NLP). TensorFlow can be used to analyze text, understand sentiment and intent, and generate natural language responses. This technology has been used in chatbots, virtual assistants like Siri or Alexa, translation software, and more.Tensor tutorial for beginners is great for those who want to get started with natural language processing.
Recommender Systems
As per the tensorflow tutorial for beginners, TensorFlow can also be used to create recommender systems. A recommender system is an algorithm that uses past user data to suggest the user’s desired content. For example, streaming services such as Netflix or Hulu often use them to recommend movies and TV shows based on a user’s viewing history. They can also be found in online shopping sites such as Amazon to suggest products based on what the user has previously looked at or purchased.
Time Series Analysis
TensorFlow can also be used for time series analysis. Time series data is a sequence of data points that are collected at regular intervals. This type of data is useful in forecasting, as it can be used to predict future values based on past trends. TensorFlow can be used to analyze time series data and make predictions about future values. With the complete tensor tutorial for beginners, you can start exploring the possibilities of generative models.
Generative Adversarial Networks (GANs)
One of the most interesting applications for TensorFlow is generative adversarial networks (GANs). GANs are neural networks that can generate new content based on existing data sets. For example, they have been used to create realistic images from scratch, generate new music or videos, and much more. If you want to learn about GANs in depth, it would be best to go for the Tensorflow beginner tutorial.
Deep Learning
Finally, TensorFlow can also be used for deep learning. Deep learning is a type of machine learning that uses multiple layers of neural networks to analyze data and make predictions. This technology has been used for many applications, such as self-driving cars, medical diagnosis systems, image classification, and more.The Tensorflow beginner tutorial not only helps you understand the basics of deep learning but also provides easy-to-follow steps to get started.
Tensorflow – Features, Advantages, and Limitations
Now that we understand TensorFlow’s basic operations and applications, let’s dive deeper and discuss TensorFlow’s features, advantages, and disadvantages as a software programming library.
TensorFlow is feature-rich and provides APIs for C++, MATLAB, Python along with a wide array of other programming languages support. With each passing day, as machine learning and the tools behind it get more sophisticated, TensorFlow is improving, too. In a recent Summit, TensorFlow introduced a javascript library for working with machine learning models – called tensorflow.js. You can check it out in more detail at playground.tensorflow.org. Here you can see real-time changes. How cool is that!
Now let’s look at a few important advantages of using TensorFlow in your machine learning projects and endeavours:
- The TensorBoard provided with TensorFlow is superb for creating simple and quick visualisations to convey your findings to a large population of people.
- TensorFlow is inherently platform-agnostic. So, it can be used with different platforms and languages as per your need.
- Training TensorFlow models is easy, both on GPU and CPU distributed computing.
- TensorFlow brings with it auto differentiation abilities. That way, it benefits gradient-based learning algorithms.
- It has support for asynchronous computation, queues, and threads.
- Last but not least, the library is entirely open-source, allowing for more modifications and improvements as per the developers’ choices.
Now, let’s check out some limitations or drawbacks of TensorFlow. These drawbacks are being worked on as you read this, and with time, they will be resolved by the developers.
- TensorFlow comes with GPU memory that might conflict with Theano if both are important in the same application scope.
- TensorFlow lacks any support for OpenCL.
- To properly work with TensorFlow, you need understanding and knowledge of calculus, algebra, and not to forget – machine learning.
Popular AI and ML Blogs & Free Courses
Conclusion
In this tutorial, we saw what TensorFlow is, how it is installed, what Tensors are, along with different applications, features, benefits, and limitations of using TensorFlow. If you have read this far, chances are, you would want to know more about working with TensorFlow.
This introductory tutorial was written to give you a basic understanding of TensorFlow as a tool. Now, with this knowledge by your side, you will be able to dive deeper into the world of creating and training machine learning models using the open-sourced TensorFlow library.
If you still feel stuck, check out our Master of Science in Machine Learning & AI, where we take you from the very basics and help you develop your models and systems. The course is offered in collaboration with IIIT-Bangalore and is designed to help both beginners and people with some experience.
Check it out and get yourself enrolled today!
![15 Interesting MATLAB Project Ideas & Topics For Beginners [2024]](/__khugblog-next/image/?url=https%3A%2F%2Fd14b9ctw0m6fid.cloudfront.net%2Fugblog%2Fwp-content%2Fuploads%2F2020%2F05%2F561-metalab-projects.png&w=3840&q=75)
