Understanding Tensorflow in Data Science

3 min read

Understanding Tensorflow in Data Science

Tensorflow This is something that is very important and you must understand when you want to delve into the world of data science. This has become one of the materials that must be studied if you want to become a data scientist. Data Science itself is an important thing nowadays, because it has a very important role in company development.

Introduction to Tensorflow

Introduction to Tensorflow
Introduction to Tensorflow

First of all, we need to explain about Tensorflow, which is an innovative product from Google. Tensorflow is a library from Google that was created and developed and has become a very popular library used in developing machine learning. Not only is it applied to machine learning, but it is also applied to algorithms that have a variety of complex mathematical operations.

Google itself launched this to introduce an ecosystem that can provide multiple workflows in implementing machine learning in various applications. Without realizing it, we actually use Tensorflow, like Google Voice or Google Photos. Indirectly you use Tensorflow because that model operates on a large group of gadgets from Google on perceptual tasks.

There are 2 very important points about Tensorflow itself, namely Tensor and Flow, to understand them you have to know each of these points. Below we will explain these two points.

1. Tensor

The definition of Tensor can be interpreted as a container that can store data in the form N. In theory, Tensor is a mathematical object that is used to describe physical properties such as vectors and scalars. The basis of Tensor is actually just a generalization of vectors and scalars, where the scalar is Tensor 0.

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Meanwhile, the Vector is the first ranking Tensor, and you can determine the Tensor ranking from the total directions needed. This is also characterized by the Array dimension.

2. Flow

Flow or better known as computational graphics is an entity that can be interpreted as a directed graph. So each node corresponding to a mathematical operation will exist in the directed graph. You could say that Flow is a tool for expression and also evaluating a number of mathematical expressions in each node.

From these two main points, we will be able to understand that Tensorflow is a Tensor and computational graph that will traverse nodes to Edge.

Reasons to Use Tensorflow

Reasons to Use Tensorflow
Reasons to Use Tensorflow

Until now, there are many large companies that use Tensorflow, which is a product from Google. Of course, it’s not without reason that some of these companies use Tensorflow from Google, here are some of the reasons.

  1. Much more flexible code generation for easy prototyping, straightforward iteration, intuitive debugging, and also fast debugging.
  2. Create and train models with high-level hard APIs for easy machine learning customization
  3. Multi-level abstraction for various applications
  4. The path to faster production starts with training and deploying easy models regardless of language or platform.
  5. Enables distribution of training across different hardware settings without any change in model definition due to the Distribution Strategy API for large ML tasks.
  6. Train and deploy models in a JavaScript environment using Tensorflow to run models and inference for mobile and Edge devices.
  7. Good control and flexibility in creating complex topologies featuring model subclassing APIs and hard functional APIs.
  8. Access to the Add On library system and powerful models for experimentation
  9. Providing experiments as research and also developing proof of concept
  10. Can develop, train, and apply advanced algorithms without having to reduce model speed and performance.
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Several Examples of Applications from Tensorflow

Several Examples of Applications from Tensorflow
Several Examples of Applications from Tensorflow

Actually, there are several examples of applications of Tensorflow that perhaps not many people know about and not many people are aware of. Its implementation actually makes it very easy for various necessary matters, you know. Below are some examples of implementation that might be an inspiration for you.

  1. Can estimate house prices quite accurately
  2. Pictures from the sign language movement like at Google and Microsoft
  3. Cars that can drive themselves or self-driving features
  4. Analyze sentiment, be it market or other
  5. Detecting credit card borrower defaults
  6. Summarizing or concluding a text
  7. Speech or voice recognition system
  8. Recommendation system

Tensorflow Users to Date

Tensorflow Users to Date
Tensorflow Users to Date

In this modern era, machine learning has become very important and useful for many companies. Many companies are using machine learning to solve their biggest problems, and they need all the tools and resources to apply it. So Tensorflow, which can build and train ML, has ended up being widely used, from e-commerce, HealthCare, to social networks.

Below we will inform you about who has used Tensorflow to date.

  1. Paypal uses it to detect possible fraud
  2. Lenovo uses it for its scalable Intel Xeon Processors
  3. Airbnb uses it to categorize listing photos
  4. Twitter uses it to provide ratings on the home timeline
  5. Coca-Cola uses it to support mobile proof of purchase at Coca-Cola
  6. Lenovo also uses it for Lenovo Intelligent Computing Orchestration to help accelerate the intelligence revolution.
  7. GE or General Electric trained a neural network with Tensorflow for anatomical identification on brain MRI.
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What is Tensorflow Lite?

What is Tensorflow Lite?
What is Tensorflow Lite?

Google released Tensorflow Lite which is used specifically for mobile devices, be it iOS or Android. Tensorflow Lite is a machine learning library from Google specifically created to better optimize machine learning models on Edge devices. So this lite version is actually the core ML Kit for running those machine learning models.

There are two components of the lite version that make training and model deployment easier, here is a complete explanation.

1. Model Maker

This is a Python library that can make it easier to train a lite version of Tensorflow models using your own data. This training uses a base of a few lines of code without machine learning expertise.

2. Task Library

This is a cross-platform library that makes it easy to deploy models with just a few lines of code in mobile apps.

So you could say this lite version was created by Google so that it can work more optimally on mobile devices. This is because there are several process and system simplifications that make the lite version run well on these mobile devices.

So Tensorflow is a library from Google which is very helpful in creating Machine Learning which is also important for many companies.

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