Deep Learning is a subset of Machine Learning that focuses on teaching and training computers to do something that comes naturally to humans – learn through examples and experience. It seeks to imitate the functioning of the human brain, particularly how it processes data and creates neural patterns for making decisions.
Deep Learning involves algorithms that are inspired by the structure of the human brain. These algorithms are referred to as Artificial Neural Networks. A Deep Learning model can learn to perform classification functions directly from images, or text, or sound.
These models are trained using labeled datasets and neural network architectures comprising multiple layers. With adequate training and data, Deep Learning models can achieve such an accuracy that can exceed the intelligence of humans.
Since Deep Learning is still an evolving concept, it can be pretty overwhelming for beginners just entering the field. In such a scenario, the best way to move forward is by making use of open-source Deep Learning platforms. These Deep Learning libraries come with active communities that can help you learn more about the field and hone your machine learning skills.
Here are ten open-source Deep Learning libraries that you must acquaint yourself with!
Table of Contents
Top Open Source Deep Learning Libraries
TensorFlow features a neat and flexible architecture that facilitates the speedy development of state-of-the-art ML models and ML computation. It can run seamlessly on both CPUs and GPUs and also on TPU platforms.
TensorFlow also has a lightweight library for deploying models on mobile and embedded devices called TensorFlow Lite. And for those who wish to train, validate, and deploy ML models in large production environments, there’s TensorFlow Extended.
Keras is an open-source neural network library that can run on top of TensorFlow, Theano, Microsoft Cognitive Toolkit, and PlaidML. It is written in Python and hence, is highly user-friendly, modular, and extensible.
Although Keras allows for fast experimentation with deep neural networks, it cannot handle low-level computation. It uses another library called “backend” for low-level computations.
Speed is a core advantage of Keras – since it has built-in support for data parallelism, it can process large volumes of data while simultaneously speeding up the time needed to train models. Also, Keras is supported on a host of devices and platforms, and hence it is widely used by many AI communities for Deep Learning models.
3) Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit designed by Microsoft for commercial-grade distributed Deep Learning. It demonstrates the functioning of neural networks as a series of computational steps through a directed graph.
CNTK can either be used as a standalone ML tool through its own model description language (BrainScript) or be incorporated as a library in Python/C#/C++ code. It allows you to combine popular model types, including feed-forward DNNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
Furthermore, it can also implement stochastic gradient descent (error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
Caffe is a Deep Learning library written in C++ but with a Python interface. It was developed at the University of California, Berkeley.
Caffe was designed, keeping in mind three fundamental features – speed, expression, and modularity. While it has an expressive architecture that facilitates application and innovation, Caffe’s extensible code encourages active development.
There’s no need for hard coding for defining models and performance optimization. Caffe’s speed makes it perfect for industry deployment and research experiments.
PyTorch is an open-source ML framework based on the Torch library. It is designed to accelerate the journey from research prototyping to production deployment. PyTorch has a C++ frontend along with a highly polished Python interface, which is the core focus of development. It has two high-level aspects –
- Deep Neural Networks designed on a tape-based auto diff system.
- Tensor computing having a strong acceleration feature via graphics processing units.
PyTorch is used for applications like natural language processing and computer vision. Its “torch.distributed” backend facilitates scalable distributed training and performance optimization in research and production.
Deeplearning4j (DL4J) is the first commercial-grade, distributed Deep Learning library written in Java it is and developed for Java and Scala. Hence, it is compatible with any JVM language like Scala, Clojure, or Kotlin.
DL4J leverages the latest distributed computing frameworks – Apache Spark and Hadoop to accelerate training and to bring AI to business environments for use on distributed CPUs and GPUs. In fact, DL4J’s performance on multi-GPUs equals Caffe’s performance.
Theano is a Python library that helps you define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays. Theano features excellent integration with NumPy and uses GPU to perform fast data-intensive computations. Also, it has an efficient symbolic differentiation and enables dynamic code generation in C.
Theano is primarily is mainly designed to handle different types of computation required for large neural network algorithms used in Deep Learning. Hence, it is a powerful tool for developing Deep Learning projects. It can take structures and convert them into efficient code that uses NumPy and other native libraries.
There are numerous other Deep Learning libraries apart from the seven we’ve mentioned. They include TFLearn, Caffe2, Torch, DLib, Neon, Chainer, H2O.ai, and Shogun, to name a few. If you are a beginner in Deep Learning, don’t rush with every tool and Deep Learning platform at once.
Choose one based on the project you are willing to take on and work your way through. If you want to improve your machine learning skills and become a IIIT-B certified machine learning engineer, check out our PG Diploma in Machine Learning and AI program which is designed for working professionals and more than 450 hours of ‘deep’ machine learning.
How is Keras different from TensorFlow?
The TensorFlow library is an open source library, while Keras is a type of neural network library. Keras does not provide low-level APIs, while TensorFlow is known for producing both high-level and low-level APIs. When compared in terms of speed, Keras is much faster than TensorFlow. TensorFlow is preferred for use for high performance models, while Keras is used for low-performance models.
Will a beginner find TensorFlow difficult to understand?
TensorFlow is a machine learning framework, and it simplifies the creation of machine learning models for both novices and experts. If you want to work in machine learning, you'll need to spend six to twelve months researching and honing your TensorFlow skills. You won't have too many problems if you know the foundations of programming languages like R and Python.
Are there any disadvantages of using TensorFlow?