Artificial intelligence has transformed the world. Enterprises are frequently on the lookout for the latest technologies to improve their efficiency and make better decisions for their customers.
Deep learning is part of artificial intelligence (AI) that helps computers learn information as humans do. To make this possible, several tools, algorithms, and applications are used. Deep learning tools or programs helps to understand the impersonate working of the human mind for preparing information and recognize designs for dynamic.
Let's understand a few terms involved in deep learning.
Deep learning applications have a significant impact on the way we live. They help in technological advancements, and their ultimate aim is to simplify life for humans. Some of these applications include automatic speech recognition, image recognition, natural language processing (NLP), etc. Deep learning is undoubtedly becoming one of the most evolving fields in Artificial Intelligence and Machine Learning.
Deep learning algorithms build models for businesses that help anticipate more accurate results. It helps companies make better decisions. Unlike traditional and linear machine learning, deep learning algorithms are non-linear and more involved.
Finally, Deep learning tools, also called programs, tries to mirror how a human brain works. These tools then help computers process data and find out patterns for complex decision making.
In this article, we will look at deep learning tools. But before that, let's find out the different software involved in deep learning
Top Deep Learning Software Tools in 2021
Subscribe to our youtube channel to get new updates..!
Neural networks are the primary modes of machine learning. The neural designer is mainly used for data mining. The neural networks aim to imitate the brain’s functioning, and therefore, they are the brain's mathematical and computational models.
H20.ai is a machine learning API for applications like gradient boosting, random forest, etc. This deep learning tool is fast, reliable, and scalable. Nowadays, almost all businesses are looking to solve their everyday problems with machine learning and predictive analytics. You can also integrate H20 with Apache Hadoop and Spark.
DeepLearningKit has the Apache 2.0 License. It supports Convolutional Neural Networks for applications such as image recognition. DeepLearningKit's primary goal is to use deep learning models on Apple devices with the graphics processing unit (GPU). The Caffe Deep Learning Framework trained the DeepLearningKit tool.
4.Microsoft Cognitive Toolkit
The key features of this deep learning tool are:
- It is a free program.
- It is an easy-to-use tool.
- It is open-sourced
- It has commercial standards.
Microsoft Cognitive Toolkit teaches the deep learning algorithms in computers to gain knowledge and function like the human brain. Previously called the CNTK, it provides massive scaling, speed, accuracy, high quality, and is mostly compatible with programming languages.
[ Related Blog: Machine LearningTutorial ]
Theano and TensorFlow use this deep learning tool. Keras is written in Python, and its primary goal is experimentation. This deep learning library also enables prototyping to become faster and easier. Keras supports convolutional networks and recurrent networks.
Torch is a deep learning tool that makes GPU a priority. What makes this tool easy and efficient is its scripting language called LuaJIT, and the C/CUDA implementation. Some of the useful core features of Torch are neural networks, routines for numeric optimization, indexing, GPU support, ports for iOS, etc.
Users can define, optimize, and analyze mathematical expressions using Theano, which is a Python library. Some of its main features include:
- Big data problems can be solved with super-fast speeds.
- The computer algebra system (CAS) can be combined with a compiler.
- Personalized C code can be generated.
Expression, modularity, and speed are three features that make Caffe a useful deep learning tool. Berkeley AI Research (BAIR) and other contributors developed Caffe, which is under the BSD 2-Clause license. It drives innovation possible through striking architecture.
The Gensim tool analyzes plain-text documents with statistical semantics. Not only that, its most profitable features include its robustness, high efficiency, and easy to use the software. Gensim is under the OSI-approved GNU LGPLv2.1 license. Conclusion
This article has covered the brief definitions of a few useful terms in deep learning and some of the most popular deep learning tools.
So, what is deep learning again? It is part of machine learning that allows computers to gain knowledge and learn information naturally, like humans. It has several applications in our world, such as hands-free speakers to play music, adding sounds to silent movies, driverless cars, etc. It is undoubtedly one of the fastest-growing fields and is achieving excellent results.
Deep learning software
The different deep learning software is:
- Visualization: It helps big data like images and animations in communication.
- Self-learning: It includes unsupervised learning.
- Neural networking modeling: In this software, artificial neural networks calculate results.
- Model training: In this software, machine learning algorithms learn from training data.
- ML algorithm library: Deep learning consists of an open-sourced machine learning algorithm library.
- Image segmentation: It distributes an image into different pieces, which makes it easier to interpret it.
- Document classification: Documents are classified into images, music, text, etc. It makes it easy to organize and manage.
- Convolutional neural networks: In this software, deep artificial neural networks are used to evaluate visual images. Images are organized according to the similarity
Many useful deep learning tools are available in the market right now. We hope that you now have an idea of some of the most popular ones with this article.
If you want to know more about deep learning and artificial intelligence, check out our AI and Machine Learning courses to develop your career and learn new skills.