Deep Learning is a common subset of a larger group of machine learning techniques focused on databases that is constantly evolving.
As a comparatively recent term, the immense number of tools available can be daunting for some of those already working or considering entering the industry.
Best Python Libraries For Deep Learning
We have already learned some data science and machine learning libraries. Now let’s understand the deep learning libraries, which are also used in some programs of machine learning.
In python, there are hundreds of deep learning libraries, we discuss some of them in this blog. The deep learning libraries are external open-source python libraries. We can’t install many of them by directly using the pip command. The way of installation of each library is different.
We can use a combination of some library files in the program. All libraries have their own features to solve the machine learning and deep learning problems. Most of the deep learning libraries work on Python 3.7 or later versions.
Let’s understand each library files in detail. To understand more about any library, just go to the mentioned website links of respective library files.
#1 Tensor Flow
TensorFlow is an open-source symbolic math library for machine learning based on neural networks.
It is on dataflow and differentiable programming.
TensorFlow was developed by Google in November, 2015 and the first version of it was released on February 11, 2017. It is written in C++, CUDA and Python.
The name TensorFlow originates from the operations, which neural networks perform on multidimensional data arrays, which known as tensors.
Tensors are an algebraic object, which describes a relationship between sets of algebraic objects related to a vector space.
It is good for product based firms like Airbnb, Airbus, PayPal, VSCO, Twitter etc. because it offers an outstanding model.
The feature of TensorFlow as a tensorboard allows visualizing model parameters, gradients and performance. TensorBoard is a web based visualization tool.
TensorFlow is used to develop machine learning models and it allows putting machine learning models in production mode across numerous platforms as in the cloud or on-premises, in the browser or on-device.
To deploy machine learning models, we use TensorFlow frameworks as TensorFlow lite.
TensorFlow is the best library for deep learning and its focus on training of deep neural networks. In computer graphics for deep learning, we use TensorFlow Graphics. TensorFlow mainly uses python 3.7 or later versions and anaconda.
TensorFlow is available on 64-bit Windows, Linux, macOS and mobile computing platforms including Android and iOS. Tensorflow provides backend compatible APIs for other programming languages.
TensorFlow has a dependable and large community. We can easily use tensor flow in virtual machines and run it on multiple CPUs, GPUs (graphics processing unit) and TPUs (tensor processing units).
Installation Of TensorFlow
- Tensorflow works on CPUs and GPUs.
- We can install TensorFlow by using the pip command as “pip3 install <tensorflow wheel file path> or pip3 install tensorflow or pip3 install tensorflow-gpu”.
- We can also install TensorFlow in Anaconda by using the conda command as “conda install tensorflow”.
- To run TensorFlow in GPUs, we need to install CUDA toolkit first.
- Before installing TensorFlow, we need to first install the latest version of numpy and scipy.
- Importing Module
After installing TensorFlow, we can import it by using this syntax “import tensorflow” as tf
Application Of TensorFlow
- It is mainly used to create “Automated Automatic image annotation (or automatic image tagging) software” as DeepDream. In this software, computer system automatically assigns metadata (i.e. data that provides information about other data) in the form of keywords to a digital image.
Let’s take an example of a New York City image.
- Here, the first image is an original digital image and the second image is created after applying TensorFlow.
- This application of computer vision is used in image retrieval systems to organize and locate images of interest from a database.
The TFLearn works with TensorFlow. It is a modular and transparent deep learning library built on top of TensorFlow. It is designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up the experiments, while remaining fully transparent and compatible with it.
TFLearn has easy ideas to build highly modular network layers, optimizers and various metrics embedded within them. So, we can say it is easy to use and understand. It has attractive graph visualization features for weights, gradients and activations. It has many useful functions to train the built in tensors.
Installation Of TFLearn
- We can install TFLearn after installing TensorFlow. It works with TensorFlow.
- We can install it by using the pip command as “pip3 install tflearn”.
- We can also install it in anaconda by using these commands“conda install pip” & “pip install tflearn”
After installing TFLearn, we can import it by using this syntax “import tflearn”
Application Of TFLearn
- It is used in deep learning and AI models.
PyTorch is an open source machine learning and deep learning library, which is based on the Torch library.
was developed by Facebook’s AI Research lab (FAIR) in September, 2016. It is written in C++, CUDA, and Python.
In PyTorch, py word is for python and torch word is for torch library. The Torch library is not directly used in python so facebook has created an extended version of Torch library as PyTorch, which is used in python language.
Torch is an open-source machine learning library and a scientific computing framework. It is used for the Lua programming language as LuaJIT (i.e. scripting language). It provides a wide range of algorithms for deep learning.
The torch provides a flexible N-dimensional array or Tensor, which supports basic routines for indexing, slicing, transposing, type-casting, resizing, sharing storage and cloning.
PyTorch is used by many big companies like JPMorgan Chase, Comcast, Amgen, IBM, SparkCognition for their different works.
PyTorch has a tender learning curve and different tools for computer vision, machine learning and NLP. Because of that it has become popular in the machine learning and data science market. It is easier than other machine learning libraries so we can say it is beginner friendly.
PyTorch has good community support. It works with multiple GPUs. It is used for developing computational graphs and we can change it on runtime.
If we compare PyTorch with TensorFlow, TensorFlow is better for production models and scalability. On the other hand, PyTorch is easy to learn, lighter to work with and better for building rapid prototypes and desired projects.
Installation Of PyTorch
- To install PyTorch, we need to install all dependencies such as the latest version of pip, setuptools , numpy and scipy.
- PyTorch works with/without CUDA toolkit so accordingly we have to install CUDA.
- Anaconda is the easiest way to install PyTorch because anaconda has all dependent libraries.
- We can install PyTorch by pip3 command as “pip3 install <wheel file path of pytorch>” or by anaconda as “conda install < pytorch path>”. We get the PyTorch path for Anaconda by selecting programming language on PyTorch website
After installing PyTorch, we can import it by using this syntax “import torch”
Application Of PyTorch
It is mainly used for computer vision and natural language processing applications.
Theano is an open source library used for fast numerical calculations. It is an enhancing compiler for defining, optimizing, manipulating and calculating mathematical expressions.
Theano was developed by the LISA group at the University Of Montreal, Quebec, Canada in 2007. It is written in CUDA and Python.
Theano mainly takes your structure and converts it into very well-organized code which uses Numpy. The syntax of Theano is a symbolic form. So it is easy to understand and use by beginner programmers.
All the expressions are defined in the abstract sense, compiled and later used for calculations. Theano avoids errors and exceptions automatically when working with logarithmic and exponential functions.
Theano calculates expressions faster with dynamic C code generation. The code execution is also faster in Theano.
It is used for matrix-value or multi-dimensional arrays efficiently. In Theano, calculations are expressed using a NumPy-esque syntax. Theano is the combination of Numpy and Sympy.
Installation Of Theano
- Theano works on CPU and GPUs. The GPU theano is faster than CPU theano.
- We can install theano by using pip command as “pip install theano”
- We can install it in anaconda by using the conda command as “conda install theano”.
After installing theano, we can import it by using this syntax “import theano”
Application Of Theano
- It is used for scientific computing in Deep Learning Projects. It creates Deep Learning models or wrapper libraries which are used to simplify the process.
- It is used to handle the calculation part of large neural network algorithms which is used in Deep Learning.
Keras is an open-source library and provides a python interface for artificial neural networks. It works as an interface for the TensorFlow and used to create deep learning model.
It is written in Python and developed by François Chollet in March, 2015.
Keras includes many functions which are used to build neural-network blocks like layers, objectives, activation functions and optimizers. It works with images and text dataset and creates easy deep neural network code for them.
Keras has different functions for convolutional and recurrent neural networks. Keras supports common utility layers like dropout, batch normalization, and pooling.
Keras run on CPU and GPUs. Keras supported multiple backbends like TensorFlow, Microsoft Cognitive Toolkit, Theano and PlaidML. Keras also has a large community.
Installation Of Keras
- To install Keras, we have to first install dependent libraries such as numpy, scipy and Theano.
- We can install Keras in Python by using the pip command as pip install keras.
- We can install Keras in Anaconda also by using the command as “conda install keras”.
After installing Keras, we can import it by using this syntax “import keras”
Application Of Keras
- It is used to create deep learning models, which are used for prediction, feature extraction and fine tuning.
NLTK means Natural Language Toolkit, which is used for creating Python programs, which works with human language data for applying in statistical natural language processing.
It was developed in 2001 by Steven Bird, Edward Loper, and Ewan Klein. It is written in Python.
NLTK includes functions and libraries related to text processing like libraries for word tokenization, tagging, dependency parsing, stemming, semantic reasoning, chunking and classification. So we can say, NLTK is a bunch of many machine learning libraries.
NLTK is used for natural language processing tasks like neural machine translation, language modeling and named entity recognition. It offers a synonym bank dubbed wordnet and includes n-gram.
It is good for education, computational linguistics and research work for engineers, researchers, industry users, students, linguists and educators.
Installation Of NLTK
- We can install NLTK in Python by using the pip command as pip3 install nltk.
- We can install NLTK in Anaconda also by using the command as “conda install nltk”.
After installing NLTK, we can import it by using this syntax “import nltk”
Application Of NLTK
- It helps the computer to preprocess, understand and analyze the written text or we can say it is used for text processing.
Orange is an open source python library, which contains different tools for data visualization, data mining and testing machine learning algorithms. It provides front-end visualization for data analysis and visualization. The python Orange3 library is used for data manipulation and widget alteration.
It was developed by scientists at the University of Ljubljana in 1996. It is written in C++.
Orange3 was developed for creating high- accuracy recommendation systems and predictive models. Orange3 uses numpy, scipy and scikit-learn for scientific computing and for GUI (graphical user interface), it works with Qt framework.
Orange3 is widget based structure which contains different components for different works like creating data analysis workflow after placing widgets on canvas interface, comparing algorithms, showing data tables, creating predictive models to find precise business forecast, preprocessing, subset selection etc.
Installation Of Orange3
- We can install Orange3 in Python by using the pip command as pip3 install orange3.
- We can install Orange3 in Anaconda also by using the command as “conda install orange3”.
- By default, in installation, orange3 contains a number of machine learning , preprocessing and data visualization algorithms in 6 component sets as classify, data, evaluate, visualize, unsupervised and regression. Additionally, we can add functionality for text-mining, bioinformatics and data fusion.
After installing Orange3, we can import it by using this syntax “import Orange”
Application Of Orange3
- It is used in biomedicine, genomic research and bioinformatics for testing new machine algorithms and developing new techniques.
- It is used in teaching also for educating the biology, informatics and biomedicine students for data mining methods and machine learning.
OpenNN stands for Open Neural Network. It is an all-purpose purpose artificial intelligence software package and mainly works for deep learning research.
It is developed in 2003 at the International Center for Numerical Methods in Engineering. It is written in C++.
It includes many machine learning algorithms as a bunch of functions. These algorithms are mainly used for predictive analytics tasks. It increases the computer performance by using multiprocessing programming (as OpenMP).
It designs neural networks with universal approximation properties. For supervised learning, it implements any number of layers of non-linear processing units.
Its data mining functions are integrated into other software tools through respective APIs.
It contains classy algorithms and utilities to deal with many artificial intelligence solutions.
OpenNN is much faster than PyTorch and TensorFlow.It trains models 2.51 times faster than PyTorch and 12 times faster than TensorFlow.
Installation Of OpenNN
- We can install OpenNN in Python by using the pip command as “pip install OpenNN”.
- We can install OpenNN in Anaconda also by using the command as “conda install OpenNN”.
- OpenNN uses the C++ language to use it in python, we need to install pybind11 also. A pybind11 is used to map the core C++ features to Python.
After installing OpenNN, we can import it by using this syntax “import OpenNN”
Application Of OpenNN
- It is used in engineering, energy, marketing, health and chemistry sectors for solving predictive analytics tasks.
- It is also used for advanced analytics and neural networks implementation.
Getting in touch with the community by observing and communicating with the deep learning open source applications that are already accessible is a great way to keep up to date with emerging developments.
But there you have it: our comprehensive collection of deep learning libraries and applications.
Kindly let us know if there is anything that we have missed in the comment section.