TENSORFLOW – More accessible and impact-oriented AI.

TensorFLow- simplify reality

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TensorFlow is open-source software and free data flow and differentiable programming library across a variety of assignments. It is a symbolic math library and is also used for apps for machine learning, such as neural networks. TensorFlow provides desktop, mobile, internet, and cloud-based APIs for beginners and professionals to create.

USE OF IT:

By generating a computational graph, the TensorFlow library enables users to conduct tasks. TensorFlow has taken the world by storm because it is free, (comparatively) simple to use and provides developers with entry-level machine learning backgrounds access to a strong library rather than building all of their AI models from scratch.

WHY IT IS CALLED TensorFlow

Because as a multi-dimensional array, also known as tensors, it requires input. You can build a kind of operations flowchart (called a graph) you want to conduct on that input. The input goes in at one end, and then it flows through this multi-operation scheme and comes out as output from the other end.

ARCHITECTURE :

It works in three parts:
1. Preprocessing the data
2. Build the model
3. Train and estimate the model

WHERE will RUN?

Categorize hardware and software demands into-

Development Phase: This is when the mode is being trained. Training generally takes place on your desktop or laptop.
Run Phase or Inference Phase: Tensorflow can be executed on many separate platforms once training is completed.

Platform Requirements

a. Desktop running Windows, macOS or Linux
b. Cloud as a web service
c. Mobile devices like iOS and Android

HOW WE OPERATE TensorFlow:

It operates, A graph with distinct computations defines each session. Multiplying by amount can be an easy instance.
Tensorflow requires three steps:
1. Define the variable
2. Define the computation
3. Execute the operation

ALGORITHM LIST :

Currently integrated API for:

a. Linear regression
b. Classification/Deep learning classification
c. Deep learning wipe and profound
d. Booster tree regression and classification

DIFFERENCE BETWEEN KERAS, TensorFlow and PyTorch?

Keras

Keras is a Python-based open-source neural network library. It can run at the top of TensorFlow. Intended to allow profound neural networks to experimented quickly.

TensorFlow

An open-source software library for programming data flows across a variety of functions. It is a symbolic math library that is used for apps such as neural networks to learn machines.

PyTorch

PyTorch is a fully Python-based open-source machine learning library, can be used for applications like NLP (natural language processing) and was developed by AI (Artificial Intelligence) group of Facebook.

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