Deep learning is a functional AI tool that allows computers to adjust how humans learn in real life. Deep learning is a subset of ml or machine learning artificial intelligence that fits the network in unsupervised learning from unstructured information.

Deep Learning is widely used in every industry to ensure less human intervention in complicated tasks. There are several tools for deep learning that make deep learning problems easier. Deep learning free course offered by Simplilearn online learning is a great place to learn and grow.

But choosing the right tools is what makes the difference. Here is a list of deep learning tools mentioned with what they are used in.

● PyTorch is also a computing framework that offers comprehensive support for machine learning algorithms. It employed CUDA and C/C++ libraries for processing and was made to upscale the production of building models and provide overall flexibility. PyTorch is now considered to be a competitor of TensorFlow. PyTorch runs on Python. So, anyone with a basic understanding of Python can start building their deep-learning models.

● Keras neural network library supports convolution and recurrent networks capable of running on either TensorFlow or Theano, and Keras has now been integrated into TensorFlow. Keras was designed to provide a simplistic interface for prompt prototyping by constructing effective neural networks that can work with TensorFlow. The primary usage of Keras is in classification, text generation and summarization, tagging, translation, and speech recognition.



● TensorFlow

Google Translate application: Here, Google translate uses its neural networks to translate one language into another language using Google's TensorFlow library. These qualities are excellent compared to other machine translation systems such as Microsoft Translator, Systran, etc.

Though there were many doubts about its accuracy at first, since then, Google has made many improvements to its neural network, so now it is much more accurate than before. In a short period, this application has far surpassed expectations and is at the first spot in language translation apps.

Google's machine learning group recently released its visual recognition technology as an open-source project called TensorFlow Hub. This project lets you discover, download, and use different modules for image classification and object detection. It uses deep convolutional neural networks to detect and classify every object within an image. This library categorizes the objects like a dog, cats, people, etc., on its own by just passing one sample image through without any prior training required.

● H2O.ai

H20 is a Silicon Valley open-source company. They state that they are the makers of H20, an open-source data science and machine learning platform used by Fortune 500 companies and thousands of organizations. Their vision is "to democratize AI for everyone." To do this, they use their H20 Driverless AI, which is the platform that uses AI so that AI can more efficiently deliver data science for every enterprise.

The H20 Driverless AI uses automation to perform machine learning tasks. The Driverless AI uses a library of algorithms and feature transformations to engineer high-value features for a typical dataset.

● Theano

Theano is a symbolic compiler library that builds an optimized Neural Network from mathematical expressions. So if you are using Deep Learning and not doing research on new algorithms, then it can be overkill. However, other high-level APIs like Lasagne, Block, and Keras are available to simplify the use of Theano for Deep Learning. These libraries work within the Python language, so the platform's strength comes from a richer Python ecosystem.

● Torch

Torch is based on compiled LuaJIT and provides an API suited for building Deep Learning solutions (note: Keras API is patterned after Torch). In terms of the runtime environment and managing other dependencies, the Lua environment tends to be simpler than that of Python, which has much more historical baggage. Everything is compiled, unlike in Theano, so depending on your code, Torch may have a performance advantage over Theano. However, it is essential to remember that most of the performance of Torch and Theano can be attributed to the optimized math libraries that are not written in Lua or Python.

● Apache MXNet

Apache MXNet is a speedy and scalable training and inference framework with an API for machine learning. MXNet includes the Gluon interface, which is a library in Apache MXNet. It provides a clear, brief, and basic usage API for deep learning. It makes it easy to prototype, build, and train deep learning models without affecting the training speed. It allows developers to start deep knowledge of the cloud, edge devices, and mobile apps.

You can build models for linear regression, convolutional networks, object detection, speech recognition, recommendation, and personalization in just a few sentences of Gluon code.

Deep learning tools workloads can be separated across multiple GPUs with near-linear scalability, so substantial projects can be handled in less time. Developers also save time and energy and increase productivity by running serverless and batch-based inference. So it increases overall performance and efficiency.

MXNet also supports a broad set of programming languages—including C++, JavaScript, Python, R, Matlab, Julia, Scala, Clojure, Perl, etc. Besides that, MxNet generates lightweight neural network model representations that can run on lower-powered edge devices like a Raspberry Pi, smartphone, or laptop and process data remotely in real-time. So it is beneficial for IoT and the EDGE.

● Scala

Scala is a general-purpose programming language, and it fundamentally doesn't matter which language you pick for the vast majority of tasks; it's a matter of style. Scala is an excellent language if you want an IDE that can immediately warn you about errors, even before you compile your code and run it.

That makes Scala particularly good at writing large analytics or machine learning engines. It's tough to write practical unit tests for these applications, so you generally want to rely on other methods to prevent errors, such as types and property-based tests.

Last words

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