Researchers in the deep learning domain use TensorFlow and Theano, and more often than not, are compared for their popularity, technological benefits, ease of use, and many more.

While TensorFlow was developed by engineers and researchers working on Google Brain Team, and Theano is developed by Yoshua Bengio, University of Montreal’s MILA, and has been in existence since 2007.

Key Differences between both Theano and TensorFlow:

Both are the libraries that serve almost the same purpose. The following are some of the critical differences that are described below:

Theano is developed by the LISA group, which is part of the varsity of Montreal, while the Google Brain team has developed TensorFlow for internal use. Through it was designed for the internal usage of it has been made public afterward.
Theano is preferred when the application needs insufficient resources, and the computation is not more complicated. While developing the algorithms that need moderate system configuration, Theano can be used without any doubt. TensorFlow is preferred when massive calculations are required, and the resources are appropriately available. It is an advantage of TensorFlow that it lets the complicated algorithm run in the system.
Theano library provides where only Python-based applications can be able to authorize it. Due to the limitations, it is not preferred by the researchers who are interested in working with C++. TensorFlow lets us use it with C++ and python as well that eventually offers an extended environment for research.
Both are developed for the same objective, but due to the role of organizations, they hold the label of reliability with it. Being developed by Google, which has a dedicated team named brain team, continuously develops this.
TensorFlow is pretty much popular than Theano.
Theano has been developed by a group named LISA and works perfectly fine, but it is not as popular as TensorFlow due to some of the limitations it has.
In the case of popularity, TensorFlow wins a high point is a fact that it has support from one of the leading tech legends, Google. In contrast, up until now, Theano was actively developed and maintained by the University of Montreal’s MILA, with more than 50 members in a team, contributing their time to improve it continuously.
Many experts believe that TensorFlow is like a complete re-implementation of Theano and that Google created TensorFlow to replace Theano. Even then, the latter is considered faster than TensorFlow in many respects.
Theano also supports a more extensive range of operations and while a variety of procedures, and while TF has shown promise in these aspects, there is still time before it reaches par with Theano.

Comparison between Theano and TensorFlow

Theano	TensorFlow
It is a python-based library Theano is a fully python-based library, which means it has to be used with the only python. This library will work with the python language and depends on python programming to be implemented.	It is C++ and python-based library TensorFlow is a C++ and python-based library that means it can be used in both the C++ and python programming. Being able to deliver in two languages, it is considered more by the developers.
It uses Single CPU It uses a single CPU for processing or performing the computations. It makes efficient use of a single CPU and generates the outcome, which is based on the processing power of the CPU.	Uses one or more CPUs TensorFlow is capable of using one or more CPUs based on how it has been performing. Using a multiple CPU over single has a preference as it leads to reduce the time it may take to complete computations.
Moderate compile speed Theano is keen to perform complex computations, but sometimes it is not able to meet the requirement due to its low compilation speed. The compile-time is too high but leads to taking time if the program complexity of the program is more elevated.	Fast compile speed TensorFlow is considered for taking less compilation time as compared to Theano. The fact is that it could make use of multiple CPUs makes it the one that can do complex computations in some time than what is taken by Theano.
Moderate popularity As compared to TensorFlow, it is considered less popular due to limitations in its feature. It can be used in the python programming and limited to use a single CPU hence only preferred where regular computations are required.	Higher popular The TensorFlow library has been developed with python and C++. It is capable of working with many CPUs. Due to these popular and preferred at the place that needs complex computations.

So, this brings us to the end of blog. This Tecklearn ‘Difference between TensorFlow and Theano’ 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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Advantage of Deep Learning over Machine learning
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Pre-requisites for AI & DL
Python Programming Language
Installation & IDE

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Python – NumPy
Python for Data Science and AI
Python Language Essentials
Python Libraries – Numpy and Pandas
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More Prerequisites for Deep Learning and AI

Pandas for Data Analysis
Machine Learning Basic Concepts
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Data Set
Machine Learning Concepts
Regression
Logistic Regression
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Decision Trees
Python Libraries for Data Science and AI

Introduction to Neural Networks

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Sigmoid Function
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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
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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
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Using TensorBoard with Keras
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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
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Introduction to RNN Model
Application use cases of RNN
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Training RNNs with Backpropagation
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