TensorFlow and Keras both are the top frameworks that are preferred by Data Scientists and beginners in the field of Deep Learning. This comparison of TensorFlow and PyTorch will provide us with a crisp knowledge about the top Deep Learning Frameworks and help us find out what is suitable for us.

TensorFlow is an open-source software library used for dataflow programming beyond a range of tasks. It is a math library that is used for machine learning applications like neural networks.

Keras is an open-source neural network library written in Python. It can run on top of TensorFlow. It is defined to enable fast experimentation with deep neural networks.

Comparison b/w both frameworks

All three frameworks are internally related to each other and have some fundamental differences that distinguish them from one another.

Origin
Speed
Level of API
Architecture
Debugging
Dataset
Popularity
APIs

Origin

TensorFlow library is developed by the Google brain team and free software library. And this library is open source in nature. And Keras is a minimalist Python library for deep learning which can run on top of Theano or TensorFlow and developed by Francois Chollet, a Google engineer using four guidelines’ principles: Modularity, Minimalism, Extensibility, and Python.

Speed

The performance is approximately lower in Keras, whereas TensorFlow and Pytorch provide a similar pace, which is fast and suitable for high performance.

Level of API

Keras is a high-level API able to run on the top of TensorFlow, CNTK, and Theano. It has gained support for its ease of use and syntactic simplicity, facilitating fast development.

TensorFlow is a framework that provides both high and low-level APIs. But Pytorch, on the other hand, is a lower-level API focused on direct work with array expressions.

Architecture

Keras has pure architecture. It is more readable and concise. TensorFlow, on the other hand, is not easy to use, although it provides Keras as a framework that makes work easier.

Debugging

There is usually very little need to debug simple networks in Keras. But in the case of TensorFlow, it is tricky to perform the debugging. PyTorch has better debugging capabilities as compared to the other two.

Dataset

Keras is used for small datasets as it is slower. On the other hand, TensorFlow and PyTorch are used for high-performance models and massive datasets that require execution fast.

Popularity

With the ascending demand in the field of Data Science, there has been a big growth of Deep learning in the industry. With this, all three frameworks have gained a lot of popularity. Keras is top in the list, followed by TensorFlow and PyTorch. It had gained immense popularity due to its simplicity when compared to the other two.

APIs

Keras library has a very high-level API, which could run on CNTK and Theano, but the TensorFlow library has both low-level and high-level APIs.

Keras is most suitable for:

Rapid Prototyping
Small dataset
Multiple back-end support

TensorFlow is most suitable for:

Large Dataset
High Performance
Functionality
Object detection

So, this brings us to the end of blog. This Tecklearn ‘Difference between TensorFlow and Keras’ 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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