Deep learning is the functional side of artificial intelligence that allows computers to learn, just like how humans learn. Deep learning tools or programs will be able to imitate the functioning of the human brain for processing data and identify patterns for decision making.
Deep learning algorithms help businesses to develop models that can predict more accurate outcomes to help them make better decisions.
Deep learning applications are responsible for multiple changes in the world today, a majority of which have far-reaching implications on the way we live in the world. Let’s look at the various deep learning tools that are available in the market now.
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Most Useful Deep Learning Tools in 2022
1. Neural Designer
Neural Designer is a professional application to discover unknown patterns, complex relationships, and predicting actual trends from data sets using neural networks. The Spain based startup company Artelnics developed Neural Designer, which has become one of the most popular desktop applications for data mining. Neural Designer uses neural networks as mathematical models mimicking the human brain function. It builds computational models that function as the central nervous system.
H2O was developed from scratch using Java as the core technology and efficiently integrated with most other products like Spark and Apache Hadoop. This gives extreme flexibility to customers. With H2O, anyone can apply predictive analytics and machine learning easily to solve tough business problems.
It uses an open-source framework with an easy-to-use web-based GUI, the most familiar interface. All common database and file types are supported using standard data-agnostic support. The tool is massively scalable and helps in real-time data scoring.
Apple uses this deep learning framework in most of its products like iOS, OS X, tvOS, etc. Apple uses it to support pre-trained deep learning models on Apple’s devices that have GPUs. DeepLearningKit uses Deep Convolutional Neural Networks like image recognition. It is currently trained with the Caffe Deep Learning framework, but the long-term goal is to support using other deep learning models like TensorFlow and Torch.
4. Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit is a commercially used toolkit that trains deep learning systems to learn precisely like hum brain. It is free open-source and effortless to use. It provides exceptional scaling capabilities along with speed and accuracy and enterprise-level quality. It empowers users to harness the intelligence within massive datasets through deep learning.
Microsoft Cognitive Toolkit describes neural networks as a sequence of computational steps through a directed graph. The leaf nodes of the directed graph represent input values or network parameters. The tools work exceptionally well with massive datasets. Microsoft products like Skype, Cortana, Bing, Xbox use the Microsoft Cognitive Toolkit to generate industry-level Artificial Intelligence.
Keras is a deep learning library that has minimal functionalities. It was developed with a focus on enabling fast experimentation and works with Theano and TensorFlow. The key benefit is that it can take you from idea to result in a swift speed.
It is developed in Python and works as a high-level neural networks library capable of running on top of either TensorFlow or Theano. It allows for easy and fast prototyping using total modularity, extensibility, and minimalism. Keras supports convolutional networks, recurrent networks, a combo of both, and arbitrary connectivity schemes like multi-input and multi-output training.
The torch is a highly efficient open-source program. This scientific computing framework is supporting machine learning algorithms using GPU. It uses a dynamic LuaJIT scripting language and an underlying C/CUDA implementation. The torch has a powerful N-dimensional array feature, lots of routines for indexing, slicing, transposing, etc. It has excellent GPU support and is embeddable so that it can work with iOS, Android, etc.
So here are some of the most popular best deep learning tools. We hope this article was able to shed some light on deep learning and deep learning software tools.
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What is the difference between deep learning and artificial intelligence?
With the rising popularity of emerging technologies like artificial intelligence, machine learning, and deep learning, there is an increasing tendency to use these terms interchangeably. Even though these are all deeply interconnected, these technologies are different. Both machine learning and artificial intelligence are those fields of computer science that involve concepts about teaching computers to mimic humans. But AI is the broadest category; it is employed to predict, optimize and automate operations. Machine learning is a subfield of AI, and deep learning is the subfield of machine learning. The backbone of deep learning is formed by neural networks.
How much do data scientists earn in India?
Data scientists are analytics experts who apply their technical expertise and knowledge of social science to identify data patterns and develop models to handle data. The average earning of data scientists in India is roughly INR 7 lakhs per year for professionals with less work experience. For those with 5 to 9 years of work experience, the salary ranges at about INR 12 to 14 lakhs a year. For professionals with many more years of relevant work experience, it can even go up to INR 1 crore a year.
Which companies hire data scientists in India?
Data science is one of the hottest career paths in India today. The gap in demand and supply of data scientists, with the right combination of knowledge and skill set, is creating more openings for data science aspirants. The best thing is that data science professionals can work with the biggest names in the tech industry today. Companies like Google, Microsoft, Amazon, Accenture, JP Morgan Chase Bank, LinkedIn, NetApp, Mercedes, PayPal, SAP, Shell, TCS, Uber, United Healthcare, Wipro, Reliance, Infosys, and many others are always looking for suitable data science candidates.