Open Source Tools for Deep Learning

Artificial Intelligence is an arena that is rapidly evolving and becoming more mainstream day by day. Anyone who is therefore interested in this field has a lot to catch up on at any given point and the best way to do this is by learning newer and the latest techniques in deep learning. Many tools are freely available to aid data scientists in this very quest in order to help them implement the tasks as effectively as possible. These tools are called open source tools which essentially refer to software material that is freely available for all to use without any commercial license or the need to purchase it.

Nowadays most data scientists are aware of R and how to use Python programming in machine learning, however many still opt for using open source tools that may be more apt for the task they need to implement! Lets therefore check out some of the mainstream open source tools that are easily available to make implementing deep learning more straightforward!

1. TensorFlow

This powerful toolkit was developed in 2015 and is hailed as one of the easiest to use and deploy in machine/ deep learning. TensorFlow was initially created by the Google Brain team to deal with its research and production goals, with the project constructing deep neural networks to carry out tasks like natural language processing, image recognition and translation. Since it is provided with a wide array of free tools, libraries and community resources, its use is now largely accepted even by companies like Uber, Twitter and eBay. It is available in Python, Haskell, C++, Java and even JavaScript.

2. Keras

Written in Python and initially released in 2015, Keras is a high level neural network API (Application Programming Interface) that was created to simplify machine learning and deep learning. It has the ability to be deployed on top of TensorFlow or Theano. Being highly extensive, modular and user friendly, Keras enables fast experimentation by easy prototyping which is essential in good research while running effectively on both CPUs (Central Processing Units) and GPUs (Graphical Processing Units).

3. PaddlePaddle

Parallel Distributed Deep Learning otherwise called as PaddlePaddle is a flexible, efficient and vast open source tool kit that is easy to use. It was originally developed by Baidu scientists to aid with their production. It was open sourced in 2016 to the professional community with advanced features in deep learning, end-to-end development kits and since has been opted by many in the sectors of manufacturing and agriculture.

4. PyTorch

Using the Python scripting language this open source toolkit is generally made use for natural language processing and computer vision. Having a strong GPU, memory usage efficiency and dynamic computational graph makes it popular for use by aiding in the development of dynamic neural networks with the capacity to build graphs and visualizations as per the user’s requirement as well.

5. Theano

Released in 2007, this open source library is scripted in Python and allows the user to define, customize and evaluate mathematical expressions, thereby enabling the easy fashioning of deep learning models. As it is one of the oldest freely available libraries, it has inspired developments in deep learning that has led to efficient symbolic differentiation.

6. Weka

Initially developed as a machine learning open source tool kit, Weka also features a deep learning package, which allows more complex analyses within the use of a simple interface be it in package management, classification or visualization. It is a Java application but can be used with R or Python via application programming interfaces and since it was originally developed for teaching platforms, it is still generally confined to being a teaching tool or used in smaller projects.

7. Caffe

Caffe or Convolutional Architecture for Fast Feature Embedding is an open source deep learning network that was designed for speed, expression and modularity. It makes use of C++ but also comes with a Python interface. Developed and released in 2017 by the Berkeley AI Research team, it has an elaborate architecture, well written codes and speedy performance all of which alludes to quick industrial deployment.

8. Torch

Initially developed and released as a machine learning library in 2002, Torch offers a wide array of algorithms intended for deep learning with an emphasis on GPUs and offers iOS and Android platform support as well. It’s composed of the scripting language LUA with an underlying C component, making it easy, efficient and fast to use.

Now that you know the open source tools that are easily available to the novices and the trained programmers in the field, get cracking and speed up your tasks by making use of these handy open source tools for deep learning in order to fulfill your tasks according to your requirements!