In today’s world, both Machine Learning, as well as Predictive Analytics, are imbibed in the majority of business operations and have to be proved in a quite integral. As the popularity of Machine Learning continues to solidify in the industry, with it is rising another innovative area of study in Data Science – Deep Learning.
Deep Learning is the sub-branch of Machine Learning(ML), offer tried and tested foundations for designing and training by simplifying machine learning algorithms. The unique aspect of Deep Learning is the accuracy and efficiency that brings to the table – when trained with a vast amount of data, Deep Learning systems can match or even exceed the cognitive powers of the human brain.
Deep learning is a broader field of ML in which uses artificial neural networks(ANN) to derive high-level features from the inputs. Deep learning or deep neural networks(DNN) architecture consists of multiple layers in which theya eare specifically hidden layers between the input and output layers.
Some of the common Deep Learning architectures are Convolutional neural networks(CNNs), Recurrent neural networks(RNNs), Long Short Term Memory(LSTM), deep belief networks(DBN). All of them have been applied to computer vision, audio and speech recognition and natural language processing(NLP) use cases. Even the structured and unstructured tabular data also shown good performance using deep learning models.
Each of the framework is built in a different manner and for different purposes. Here, we look at some of the top 5 deep learning frameworks available for different programming language interfaces.
PyTorch is an open-source Deep Learning framework developed by Facebook, based on the Torch library. It was designed for expedite the entire process from research prototyping to production deployment. What’s interesting thing about PyTorch is that it has a C++ frontend atop a Python interface.
While the frontend serves as the core ground for model development and backend promotes scalable distributed training and performance to optimize in both research and production.
Torch is a scientific computing framework that offers a broad support in machine learning algorithms. It is a Lua based deep learning framework in which are used widely amongst industry giants such as Facebook, Twitter, and Google.
Keras is another very popular open-source Deep Learning framework. It provides a Python interface for developing artificial neural networks. Keras software library acts as an interface for the Tensorflow library. The nifty tool can run on top of TensorFlow, Theano, Microsoft Cognitive Toolkit, and PlaidML.
Keras is particularly useful because it can scale to large clusters, shared layers, and even multiple inputs or outputs. The USP of Keras is its speed and built-in support for data parallelism. It can process a massive volumes of data while accelerating the training time for models.
It’s nutshell, Keras is lightweight and easy-to-use in a minimalist approach. These are the very reasons as to why Keras is a part of TensorFlow’s core API. The primary usage of Keras framework is in classification, text generation, and summarization, tagging, translation along with speech recognition, and others features.
The core tool of TenserFlow allows you to build and deploy models on browsers, you can also use TensorFlow Lite to deploy models on mobile or embedded devices. Tensorflow leverages data flow graphs in which the branches (tensors) of data can be processed by a series of algorithms on a graph. The movement of this type of data on the system is known as flow, hence the name Tensorflow. This helps to build and train ML models using intuitive APIs like Keras which can be also be used in speech recognition systems, image and video recognition and tagging, self-driving cars, text summarization, and sentiment analysis softwares.
MXNet known for its fast model training. It can run on smart devices as its lightweight and memory efficient and also Compatible with Windows, macOS, Linux. Using the hybrid nature through distributed training MXNet allows combined symbolic and imperative operations, making it efficient, flexible and portable. The active community of MxNet is by far the most efficient one having synchronised ideas on AI and deep learning.
A machine learning group that includes the authors Adam Gibson Alex D. Black, Vyacheslav Kokorin, Josh Patterson developed this DL Framework Deeplearning4j. Deeplearning4j Written in Java and JVM (Java Virtual Machine), Hence it is compatible with any JVM language like Scala, C++, C, CUDA, DL4J supports different neural networks, like CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and LSTM (Long Short-Term Memory).
Deeplearning4j covers a wide range of deep learning algorithms. Operating systems which supported are Linux, Windows, macOS, iOS and Android. Has support from Apache Spark and Hadoop.
The platform uses both Apache Spark and Hadoop distributed systems – this helps expedite model training and to incorporate Artificial Intelegence within business environments for use on distributed CPUs and GPUs. In fact that on multiple-GPUs, it can equal Caffe in performance.
Deep Learning has initiated in many practical use cases of ML and AI in general. Breaking down tasks in the simplest ways in order to assist machines it could be the most efficient manner has been made likely by Deep Learning.
Deep learning frameworks are widely used and implemented and include many more than five that we have discussed in this article. Other increasingly popular DL frameworks that were not mentioned above include ONNX,Sonnet, Gluon, Caffe,Chainer, and more.
Copyright © 2021 Nexart. All rights reserved.