Both frameworks TensorFlow and PyTorch, are apex libraries of machine learning and developed in Python language. These are open-source neural-network library framework. TensorFlow is a software library for differential and dataflow programming needed for various kinds of tasks, but PyTorch is based on the Torch library.

Why we use TensorFlow?

TensorFlow is a library framework for machine learning applications. This framework is a mathematical library used mainly for numerical computation to applying the data from the graph. The edges of the graph can represent multidimensional data arrays, and nodes represent various accurate representations. It teaches neural networks about the mathematical symbol, image recognition, and partial differentiation and fully capable of running on multiple GPUs and CPUs. Its architecture is flexible.

This framework might also support C#, Haskell, Julia, Rust, Scala, Crystal, and OCami.

Why we use PyTorch?

PyTorch is a machine learning library which is applicable for an application like natural language processing. Pytorch is also appropriate for building various types of applications.
This library framework has two essential features:
The first feature of the library is the automatic differentiation for training and building of the deep neural network.
The second feature would be the computational tensor ability with support from high power GPU acceleration.
Pytorch has three modules of operations. Optimum Module, Auto grad Module, and nn Module. Each Module has its specific functions and applications.
For example, the Optimum Module is used for implementing various types of the algorithm for the development of the neural network. The nn Module is for defining all the complex low- level neural networks. e566

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Comparison between TensorFlow and PyTorch

Basic	TensorFlow	PyTorch
Library	TensorFlow is a free software library, and this library is open source in nature.	The PyTorch framework is an open-source machine learning library.
Origin	This library is developed by the Google brain team based on the idea of a dataflow graph for building models.	The library is developed by a Facebook artificial intelligence research group based on the torch.
Compatibility	TensorFlow library is compatible with different coding languages like C, C++, Java.	The PyTorch library is only for Python-based coding.
Feature	This framework is used for teaching the machine about many computational methods.	This framework is used to building a neural network and natural language processing.
APIs	TensorFlow library has both low-level APIs and high-level APIs.	The PyTorch library has low-level APIs that would focus on the working of array expression.
Ability	It is famous for its fast computational ability across a few platforms.	PyTorch is famous for its research purposes. It also assists in deep learning applications.
Speed	The speed of TensorFlow is faster and provides high performance.	The speed and performance of PyTorch are much similar to the TensorFlow.
Architecture	The architecture of the TensorFlow is complex and would be a bit difficult to understand.	The architecture of the Pytorch is pretty complicated, and it would be challenging for any beginner.
Debugging Ability	The process of debugging in TensorFlow is complicated.	Debugging abilities of Pytorch is better when it has compared to Keras and TensorFlow.
Capability	TensorFlow is capable of handling large datasets, as the processing speed of the library is very fast.	Pytorch can handle large datasets and high- performance tasks.
Size	The size of the code of TensorFlow is small in format to increase accuracy.	All codes for Pytorch consist of individual lines.
Projects	Top TensorFlow projects are Magenta, Sonnet, Ludwig	High PyTorch plans are CheXNet, PYRO, Horizon
Ramp-Up Time	PyTorch is utilizing Numpy with the ability to make use of the Graphic card.	TensorFlow has the dependency where the compiled code is run using the TensorFlow Execution Engine.

So, this brings us to the end of blog. This Tecklearn ‘Difference between TensorFlow and PyTorch’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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What you will Learn in this Course?

Introduction to Deep Learning and AI

What is Deep Learning?
Advantage of Deep Learning over Machine learning
Real-Life use cases of Deep Learning
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
Pre-requisites for AI & DL
Python Programming Language
Installation & IDE

Environment Set Up and Essentials

Installation
Python – NumPy
Python for Data Science and AI
Python Language Essentials
Python Libraries – Numpy and Pandas
Numpy for Mathematical Computing

More Prerequisites for Deep Learning and AI

Pandas for Data Analysis
Machine Learning Basic Concepts
Normalization
Data Set
Machine Learning Concepts
Regression
Logistic Regression
SVM – Support Vector Machines
Decision Trees
Python Libraries for Data Science and AI

Introduction to Neural Networks

Creating Module
Neural Network Equation
Sigmoid Function
Multi-layered perception
Weights, Biases
Activation Functions
Gradient Decent or Error function
Epoch, Forward & backword propagation
What is TensorFlow?
TensorFlow code-basics
Graph Visualization
Constants, Placeholders, Variables

Multi-layered Neural Networks

Error Back propagation issues
Drop outs

Regularization techniques in Deep Learning

Deep Learning Libraries

Tensorflow
Keras
OpenCV
SkImage
PIL

Building of Simple Neural Network from Scratch from Simple Equation

Training the model

Dual Equation Neural Network

TensorFlow
Predicting Algorithm

Introduction to Keras API

Define Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with Keras
Customizing the Training Process
Using TensorBoard with Keras
Use-Case Implementation with Keras

GPU in Deep Learning

Introduction to GPUs and how they differ from CPUs
Importance of GPUs in training Deep Learning Networks
The GPU constituent with simpler core and concurrent hardware
Keras Model Saving and Reusing
Deploying Keras with TensorBoard

Keras Cat Vs Dog Modelling

Activation Functions in Neural Network

Optimization Techniques

Some Examples for Neural Network

Convolutional Neural Networks (CNN)

Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN

RNN: Recurrent Neural Networks

Introduction to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model

Application of Deep Learning in image recognition, NLP and more

Real world projects in recommender systems and others

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