What is TensorFlow? Introduction, Architecture

What is TensorFlow?

Currently, the most famous deep learning library in the world is Google's TensorFlow. Google product uses machine learning in all of its products to improve the search engine, translation, image captioning or recommendations.

To give a concrete example, Google users can experience a faster and more refined the search with AI. If the user types a keyword in the search bar, Google provides a recommendation about what could be the next word.

Google wants to use machine learning to take advantage of their massive datasets to give users the best experience. Three different groups use machine learning:

• Researchers
• Data scientists
• Programmers

They can all use the same toolset to collaborate with each other and improve their efficiency.

Google does not just have any data; they have the world's most massive computer, so Tensor Flow was built to scale. TensorFlow is a library developed by the Google Brain Team to accelerate machine learning and deep neural network research.

It was built to run on multiple CPUs or GPUs and even mobile operating systems, and it has several wrappers in several languages like Python, C++ or Java.



History of TensorFlow

A couple of years ago, deep learning started to outperform all other machine learning algorithms when giving a massive amount of data. Google saw it could use these deep neural networks to improve its services:

• Gmail
• Photo
• Google search engine

They build a framework called Tensorflow to let researchers and developers work together on an AI model. Once developed and scaled, it allows lots of people to use it.

It was first made public in late 2015, while the first stable version appeared in 2017. It is open source under Apache Open Source license. You can use it, modify it and redistribute the modified version for a fee without paying anything to Google.

Read More

Introduction of the perception/neuron

Before we jump straight into neural networks we need to understand the individual components first such as a single neuron.
Artificial neural networks (ANN) actually have a basis in biology so we're going to see how we can attempt to mimic biological neurons of an artificial neuron (otherwise known as the perception) and then once we go through the process of how a simple perception works will go ahead and show you how you can represent that mathematically

Below let's see the biological neuron such as a brain cell

so the biological neuron works as in a simplified way through the following manner, basically you have dendrites that feed into the body of cell you can have many dendrites and what happens is electrical signal gets to pass through the dendrites to the body of the cell and then later on a single output or a single electrical signal is passed on down through an axon to, later on, connects to some other neuron and that's the basic idea we have a kind of the as many inputs of electoral signals to the dendrites goes to the body and in a single actual signal output through the axon so the artificial neuron also has inputs outputs so I go in attempt to mimic the biological neurons.

Read More

Deep Learning

Deep learning is a subset of machine learning which in turn is a subset of artificial intelligence. Artificial intelligence is a technique that enables a machine to mimic human behaviour. Machine learning is a technique to achieve AI through algorithms trained with data and finally, deep learning is a type of machine learning inspired by the structure of the human brain in terms of deep learning this structure is called an artificial neural network let's understand deep learning better and how it's different from machine learning.

Read More

Machine Learning Algorithms

Deep Learning
Deep learning is a subset of machine learning which in turn is a subset of artificial intelligence. Artificial intelligence is a technique that enables a machine to mimic human behaviour. Machine learning is a technique to achieve AI through algorithms trained with data and finally, deep learning is a type of machine learning inspired by the structure of the human brain in terms of deep learning this structure is called an artificial neural network let's understand deep learning better and how it's different from machine learning. We create a machine that could differentiate between tomatoes and cherries if done using machine learning we'd have to tell the Machine the features based on which the two can be differentiated these features could be the size and the type of stem on them with deep learning, on the other hand, the features are picked out by the neural network without human intervention, of course, that kind of independence comes at the cost of having a much higher volume of data to train our machine.

Read More