It is no more news that Artificial intelligence has gone from science-fiction to an extremely essential tool for the majority of businesses, with the increase in popularity of buzzwords like machine learning and big-data. Machine learning continues to solidify itself in almost every industry, from finance to healthcare.
With the results and benefits of using machine learning models in business processes pushing companies to invest more into artificial intelligence technologies for insights into their data. With deep-learning leading the way among these technologies.
Deep-learning is a branch of machine learning. It is unique in that it brings a whole new height of accuracy and efficiency to the table. With deep-learning initiating solutions like self-driving tech for cars, facial-recognition, natural language understanding, fraud detection, and a shedload of applications.
With its dynamic nature, unlimited capacity to learn, and flexibility, Deep Learning networks can analyze ridiculous amounts of data, consequently presenting a whole new window of opportunity in the world of analytics.
Because deep learning is the key to executing tasks of higher levels of sophistication, building and deploying them becomes quite a challenge for machine learning engineers and data scientists all over the world.
Luckily there is a multitude of frameworks at their disposal that simplifies the abstractions and complexities of developing deep learning applications. A software framework, or framework as it is also called, is a platform for developing software applications.
And we are going to be taking a look at five of the best among these frameworks.
TensorFlow is inarguably the most preferred deep learning framework. Birthed by the Google Brain team, this framework exists for both desktops and mobile phones. It supports languages such as C++, Python, and R for creating deep learning models along with wrapper libraries.
The most popular application use case would undoubtedly be Google translate, which possesses abilities such as natural language processing, text classification, summarization, speech/image/handwriting recognition, and forecasting. Besides being used for operating and deploying models on powerful computing machines, this software framework also run models on mobile platforms (iOS and Android devices). It is free, reliable, and continuously supported by Google.
This is an open-source Deep Learning framework, based on the Torch library and developed by Facebook. In recent years, PyTorch has become widely adopted in the deep learning framework community, and it is considered a suitable competitor for the more main-stream TensorFlow. PyTorch is renowned for its flexibility, ease of use, and simplicity.
And unlike Torch, PyTorch runs on Python; consequently, anyone with a basic knowledge of Python will be able to quickly integrate this framework into their workflow as they build their deep learning model. For the most part, PyTorch is the go-to framework for developing Deep Learning applications like natural language processing and computer vision.
Following the challenges accompanying the TensorFlow interface, neophytes in the deep learning world needed a more simplistic interface for rapid prototyping, and this birthed the deep learning framework – Keras. This software framework is chiefly built on Tensor and operates by constructing active neural networks that are compatible with TensorFlow and, as such, can be readily managed. And if you are just starting on your journey, Keras should be at the top of your list of frameworks as it offers TensorFlow’s backend workflow but doesn’t require as much coding.
In summary, Keras is an easy-to-use, lightweight, and minimalist framework perfect for beginners and experts who want to hit the ground running and focus on the most important aspects of Deep Learning.
There are various reasons why Keras is a part of TensorFlow’s core API. Its primary usage in text generation and summarization, speech recognition, classification, tagging, and translation are some of the most important reasons.
This is a deep learning library for the Java Virtual Machine (JVM), the “j” in its name represents Java, making it read “Deep Learning for Java.” Just as you might have guessed, DEEPLEARNING4J is developed in Java and supports other JVM languages like Kotlin, Clojure, and Scala.
This framework is more efficient when compared to Python for image recognition tasks using multiple GPUs since it is directly implemented in Java. DEEPLEARING4J has a very robust potential for natural language processing, text mining, fraud detection, image recognition, and parts of speech tagging. For the fluent Java programmers in search of a flexible and effective means for running and deploying deep learning models to production, the DEEPLEARNING4J framework is very suitable.
Caffe is an open-source framework highly known for its speed and has the potential to process over 60 million+ images daily, making it very suitable for image recognition. Whether it’s C, C++, Python, the framework is compatible with everyone, and it is also easier to set up and train, without having to build onto the network.
We’ve taken a look at five of the most common deep learning libraries out there. Each has its own unique set of features, which is why data scientists go for one over the other. It is quite obvious that the existence of deep learning has initiated practical applications of machine learning and Artificial intelligence.
In a nutshell, a lot is going on within the deep learning industry, and a lot more to be done, the frameworks above can give a fair start into the field of deep learning and artificial intelligence.