In the past, computers simply performed tasks using a set of instructions given to them. Now, with significant advances in artificial intelligence (AI), computers can be trained with deep learning software without human intervention. Therefore, deep learning software has become very popular.

Deep learning is a promising and lucrative space that has achieved results that were thought impossible.

There are many resources for those who want to better understand deep learning. In this article, we will discuss programming languages that all programmers are familiar with, as well as deep learning programming languages, tools, and the best deep learning software.

Programming languages for deep learning

To begin with, a deep learning application developer should be proficient in these five programming languages:

Python: Familiarity with this simple and powerful programming language is essential in deep learning and other branches of artificial intelligence. Python has many libraries that make it easy to implement AI algorithms. It also supports object-oriented and procedural programming.

Java: Since deep learning deals with artificial neural networks, search algorithms, etc., Java is a very suitable programming language. Because Java has a great user interface, easy debugging, and high scalability. Java also uses Swing and the Standard Widget Toolkit, allowing you to create beautiful, data-rich graphical displays.

R: This programming language is widely used by statisticians to analyze and plot data. Also, R creates plotted plots, mathematical symbols, and formulas by countless packages.

C++: It may be one of the oldest programming languages, but it is required for deep learning. The reason C++ works so well for deep learning is that it is compatible with applications that need access to large amounts of data. Because C++ can be used for both low-level and high-level programming, it gives developers more control and efficiency.

Prolog: Prolog is a basic programming language in artificial intelligence. This programming language has a tree data structure, auto-return, and pattern-matching features.

Systems based on deep learning make decisions and execute specific commands by imitating human thought patterns and through neural network algorithms.

The neural layers of deep learning systems are not designed and built by engineers; Rather, it is these different data and information that lead to the progress and improvement of the learning process of these algorithms.

The best deep-learning software

Here are some of the best deep-learning tools and software you should be using in 2020:

1) keras

If you are looking for a deep learning tool that provides neural layers, modularity, module extensibility, and support for Python programming, Cross is for you. Cross is a high-level neural network API that can be implemented in Theano, TensorFlow, or CNTK.

Cross specializes in fast testing with minimal delays. It can also run seamlessly on CPU and GPU.

Cross features

  • User-friendly, with consistent and simple APIs that minimize the number of user actions.
  • Easy development capability in new and old modules that allows full expression.
  • Written in Python, which is compact and allows for easier extensibility and debugging.

  2) Microsoft Cognitive Toolkit (CNTK)

For people who need extensive datasets and customized parameters, algorithms, and networks; CNTK can be a good choice. CNTK is an open-source deep learning program specifically suited for commercial-level deep learning.

It is one of the best deep learning platforms that allows an easy combination of popular models and SGD learning implementations.

Features of CNTK

  • Can be used in Python, C#, C++, and Java programs
  • The possibility of combining circular neural networks and neural networks
  • Supports Open Neural Network Exchange (ONNX) format.

3) Neural Designer

Neural Designer, which is hailed as the best neural network software, is commonly used by large industries and organizations. Also, since it is based on neural networks, it is capable of advanced analysis and provides comprehensive results.

Neural Design has been favored by many industries such as engineering, finance, healthcare, etc.; And by using it, businesses and organizations can make better decisions and deliver better results.

Features of Neural Designer

  • Uses neural networks to analyze complex relationships, predict patterns, and recognize data relationships.
  • Easy to use with a step-by-step guide on how the software works
  • Provide tables, graphs, and visual charts to better visualize the results
  • Uses CPU and GPU to reduce analysis time.

4) Wolfram Mathematica

Wolfram has powerful algorithms with parallelization and GPU computing. Therefore, it can solve large-scale problems. It’s also very easy to use, and if you need help, you can find a comprehensive tutorial on the Wolfram website.

Features of Wolfram

  • Has data and matrix manipulation tools
  • Efficient and powerful programming
  • Excellent graphics support

5) Torch

Building scientific algorithms with speed and flexibility is the main goal of Torch. In addition, this deep learning software and scientific computing framework uses the LuaJIT programming language, making it easy and efficient to use.

Torch is an open-source software that provides packages in machine learning, signal, image, and parallel processing.

Features of Torch

  • Excellent user interface
  • Numerical optimization paths
  • Fast and efficient GPU support
  • Embeddable and contains ports to Android and iOS backends

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