Top 7 Open-Source Deep Learning libraries

Deep Learning libraries
Deep Learning libraries

Deep learning is the branch of Artificial Intelligence that allows us to implement designing strategies and analyse data to frame a pattern for future encounters with the same input. These strategies imitate the actual working of the human brain. Deep learning belongs to the superset Machine Learning in Artificial Intelligence Technologies that can manipulate raw data.

Generally, the data that we deal with is structured and labelled, but in deep learning, we handle unsorted data that, too, with the help of unsupervised learning. This method is known as “Deep Neural Learning” or “Deep Neural Network”. It finds full application in object detection, speech recognition, optical character recognition, etc. Deep Learning functions can operate without human assistance.

With the tremendous increase in big data, enterprises are trying to build strategies to automate their system, so that the machine can study the unstructured data, frame a pattern and then implement it for future use. Nowadays, chatbots are used in customer support instead of executives. Deep Learning can sort this Big Data in a few hours, while an average human being might take years to organise it. It is implemented by an artificial neural network that consists of neurons connected to form a web-like structure.

Deep learning makes use of hierarchy, and as we all know, operations like insertion, deletion, searching, sorting, etc. can be performed in exponentially lesser time with the help of nodes. With the help of vertices, we can store the relation among the various objects. Deep Learning software tries to discover coherence among values and functions by pattern formation, without the help of any explicit instructions by the programmer. They have profoundly reduced human effort and increased the accuracy of handling a significant amount of data. It has eliminated the four challenges that we face while managing big data-Volume, Velocity, Veracity and Variety.

Let’s study the top open-source deep learning libraries in 2020:

Deeplearning4j: In the year 2019, “Deeplearning4j” was nominated as one of the most innovative contributors to the Java ecosystem by the JAXenter community. Skymind introduced this open-source, distributed deep-learning library compatible with Java and Scala programming language. The propaganda of the company is to collaborate between deep neural networks and deep reinforcement learning for commercial environments. It works as a DIY tool for programmers working in Hadoop with Java, Scala, and Clojure. It has got a very high computational and recognition power.

It widely used in identifying flaws in formatted data like bank statements or balance sheets. They can even detect emotions in sound and text. We can implement them in Android to create locks and security systems driven by voice or pattern recognition. Softwares like Grammarly can be efficiently designed by using this library. With Deeplearning4j libraries, Android applications can be made more advanced. The audio applications that can be controlled with a voice can be implemented with this library. This software is perfect for developers who have less experience in Java or Scala. Apart from Hadoop, Spark also supports this library. This makes use of parallel clusters to carry out iterations.

TensorFlow: Introduced by Google, Tensorflow is the leading Deep Learning framework in the commercial market today. The top clients of this framework include Gmail, Uber, Nvidia, Airbnb, etc. However, this library is maximum compatible with python. It supports Java, C++, C# and Julia. This framework can create a nodal network to control ios and Android operating systems. It is entirely a code-based platform, so unlike Deeplearning4j, it requires experience.

It works with static computational graphs. We have to train the model first by defining the graphs and running calculations. If we have to implement any changes, we will have to re-train the model. Though it is a popular framework, it is cumbersome to train the model. This method was implemented for the sake of efficiency, but some coders consider it to be time-consuming and complicated. Google owns this software; it will probably stay in the commercial market for an extended period. So if you invest your time in earning this, that will be eventually fruitful. This library allows us to experiment with the models, study their computational structures thoroughly as most of the methods are non-abstract. The re-training of the network model to implement any change can be considered as the major disadvantage of Tensorflow. PyTorch eliminates this disadvantage of Tensorflow.

PyTorch: It is an open-source, python-based deep learning framework that was primarily developed by Facebook’s AI Research lab (FAIR). Twitter and Salesforce are the two major clients of PyTorch.It is compatible with Java, C++, and python. The prime difference between Pytorch and Tensorflow is that Pytorch uses a dynamically computational graph, whereas Tensorflow uses a statically computational graph. This means that PyTorch allows us to make changes to the architecture in progress.

We don’t need to re-train the entire model. This allows users to work with debuggers like PDB or PyCharm.It saves the developers time and facilitates quick error resolution. PyTorch is recommended for small scale projects and prototypes. The training process is simplified in Pytorch, and implementing changes is more comfortable. It even memorises the pre-trained models.,and refers to them if required. However, working with cross-platforms is considered to be difficult with PyTorch. In that case, Tensorflow is recommended.

Gluon: The Amazon web server and Microsoft released Gluon. It can be used for creating both
simple as well as complex training prototypes. Gluon projects have a characteristic flexible interface that helps us in training models, building prototypes, and implementing functionalities. A high learning pace can be maintained with Gluon. Similar to PyTorch, it also supports dynamic computation. It combines the algorithm training process of Tensorflow along with the dynamic structure of PyTorch. It is thereby ensuring high performance as well as high flexibility. In addition to that, it combines computational power with MXNet libraries. The definition of neural networks is done by writing short and effortless codes.

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Keras: It is a high-level API framework that needs a lower-level library like Theano, Tensorflow or CNTK as a base. To implement Java with Keras, we need Deeplearning4j framework. First, we have to install Deeplearning4j, and on top of that, we can install the Keras library. It helps us to build recurrent neural networks. But it assigns a cheque on the prototyping. We can create massive models by just writing a single line of code. This limits the customisable competency of the framework. It is suitable for beginners as it provides a wide range of default models. It allows us to access low-level frameworks. It increases the code readability significantly. The API generations and callbacks are highly technical. Due to the different level of attraction, we can’t compare TensorFlow and Keras.

Deep Java Library: It is an open-source deep learning framework with Java APIs.It was introduced in the year 2019 by Amazon. The most distinctive and significant feature of this library is that it can predict prototypes. The main motive behind the release of DLJ was to provide deep learning libraries to Java developers. That is the main reason behind the tools being open-source. This is compatible with any Java IDE, but Jupyter Notebook-based code is recommended. The level of abstraction of functions is considerably high in this framework. The developer does not get to know about the creation of functions. Hence, the creation of models can’t be done by the developer. A person doesn’t need to be a machine learning expert to use this, vast knowledge of Java libraries is sufficient. It allows us to switch engines at any stage of development.

Neuroph: Like, Tensorflow and PyTorch, Neuroph is also an open-source framework for neural networks. But Neuroph is a java based framework. Neuroph’s graphical user-interface allows developers to create neural nets. It follows the object-oriented programming model. The API documentation feature helps us to understand the working of neural networks. The integral classes include basic neural network concepts like neuron layers, neuron connections, creating a neural network, neural nodes, learning rules, input and output functions, transition functions, etc. Architectures like Multilayer perception with Backpropagation, Kohonen and Hopfield networks are supported by Neuroph.

All these classes are extensible and customisable and we can new learning rules according to our requirements. This contributes to the high flexibility of Neuroph. It has inbuilt functions for image recognition. We can create prototypes and save them using this framework. If you are a beginner, and you want to escape the creating of neural networks and just implement them in your project, then Neuroph is the best option for you. Its classes are abstract as well as customisable. Hence there is the proper balance between performance and flexibility. The high level of abstraction may be considered as a disadvantage, for the curious developers.

To read more about Deep Learning, click here.

By Vanshika Singolia