Data Warehousing
Database vs Data Warehouse: Key Differences
What is Database? A database is a collection of related data which represents some elements of the...
TensorFlow is an open-source deep learning library that is developed and maintained by Google. It offers dataflow programming which performs a range of machine learning tasks. 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.
In this tutorial, you will learn:
KERAS is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow. It is designed to be modular, fast and easy to use. It was developed by François Chollet, a Google engineer. It is a useful library to construct any deep learning algorithm.
Here are important features of Tensorflow:
Here are important features of Keras:
Here, are important differences between Kera and Tensorflow
Keras | TensorFlow |
Keras is a high-level API which is running on top of TensorFlow, CNTK, and Theano. | TensorFlow is a framework that offers both high and low-level APIs. |
Keras is easy to use if you know the Python language. | You need to learn the syntax of using various Tensorflow function. |
Perfect for quick implementations. | Ideal for Deep learning research, complex networks. |
Uses another API debug tool such as TFDBG. | You can use Tensor board visualization tools for debugging. |
It started by François Chollet from a project and developed by a group of people. | It was developed by the Google Brain team. |
Written in Python, a wrapper for Theano, TensorFlow, and CNTK | Written mostly in C++, CUDA, and Python. |
Keras has a simple architecture that is readable and concise. | Tensorflow is not very easy to use. |
In the Keras framework, there is a very less frequent need to debug simple networks. | It is quite challenging to perform debugging in TensorFlow. |
Keras is usually used for small datasets. | TensorFlow used for high-performance models and large datasets. |
Community support is minimal. | It is backed by a large community of tech companies. |
It can be used for low-performance models. | It is use for high-performance models. |
Here, are pros/benefits of Tensor flow
Here, are pros/benefits of Keras:
Here, are cons/drawbacks of using Tensor flow:
Here, are cons/drawback of using Keras framework
Here, are some criteria which help you to select a specific framework:
Development purpose | Library to Choose |
You are a Ph.D. student | TensorFlow |
You want to use Deep Learning to get more features | Keras |
You work in an industry | TensorFlow |
You have just started your 2-month internship | Keras |
You want to give practice works to students | Keras |
You don't even know Python | Keras |
What is Database? A database is a collection of related data which represents some elements of the...
With many Continuous Integration tools available in the market, it is quite a tedious task to...
{loadposition top-ads-automation-testing-tools} A flowchart is a diagram that shows the steps in a...
$20.20 $9.99 for today 4.6 (118 ratings) Key Highlights of Tableau Tutorial PDF 188+ pages eBook...
In this tutorial on the difference between Data lake vs. Data warehouse, we will discuss the key...
ETL is a process that extracts the data from different RDBMS source systems, then transforms the...