Deep learning frameworks you should know about

The world is currently witnessing many technological advancements that have changed the way humans perceive things, communicate with each other, and do business. Emerging technologies like big data analytics, artificial intelligence (AI), the Internet of Things (IoT), and augmented reality are driving the growing adoption of digital transformation. Even as a career option, people are looking forward to learning more about these technologies so as to grab the various opportunities available in the job market.

Innovations like machine learning and deep learning, essential components of the broader field of AI, have become necessary across industries to survive in the present-day dynamic business landscape. Together with a Deep Learning course and experience, well-known programming languages like Python have made it possible for businesses to make the most of the technology available to them.  

Our focus today is on a crucial subset of machine learning known as deep learning. This concept refers to the simulation of the human brain’s function to process data and uncover hidden patterns. While machine learning enables computer systems to automatically learn from data and improve over time, deep learning goes a step ahead to enable those computers to self-train and learn from data to mimic human actions. Voice assistants, chatbots, image coloring, and computer-aided diagnoses are some of the popular applications powered by deep learning.

It is inevitable to come across some of the widely-used frameworks when you start exploring the field of deep learning. This article introduces those frameworks to you and what are the top features they offer. 


Offered by Google, TensorFlow is an end-to-end platform that helps developers build and deploy machine learning models with ease. You can apply deep learning with TensorFlow to unstructured data and solve real-world problems. TensorFlow is a software library for numerical computation of mathematical expressions using data flow graphs. The framework can be used in regression, classification, curve fitting, and minimization of error functions as well.    


PyTorch is a Python-based scientific computing package and is basically designed as a replacement for NumPy to use the power of GPUs and other accelerators. Created by Facebook’s AI research lab, PyTorch is an automatic differentiation library that is used to implement neural networks. Unlike TensorFlow, you can define your graph dynamically in PyTorch. PyTorch tensors can run on GPUs and accelerate numerical computations, thereby increasing the speed of neural networks by 50 times or greater.  


With Chainer, you can bridge the gap between algorithms and implementations of deep learning. It is a Python framework that allows researchers to quickly implement, train, and evaluate deep learning models. The framework supports various network architecture, which includes feed-forward nets, convnets, recurrent nets and recursive nets. The forward computation can include any control flow statements of Python without lacking the ability of backpropagation. It also makes code intuitive and easy to debug.  


As described on the official website, Eclipse Deeplearning4j is the first open-source, commercial-grade, distributed deep learning library that is written for Java and Scala. By integrating with Hadoop and Apache Spark, DL4J brings artificial intelligence to business environments for use on distributed GPUs and CPUs. The framework lets you compose deep neural nets from various shallow nets, each of which forms a so-called ‘layer.’ You can adjust a lot of parameters when you are training a deep learning network.  


Developed by Berkeley AI Research (BAIR) and community contributors, Caffe is a deep learning framework made with expression, speed, and modularity in mind. The term ‘Caffe’ is shorthand for Convolutional Architecture for Fast Feature Embedding. Its expressive architecture encourages application and innovation. The models and optimization are defined by configuration without hard-coding. Developers love Caffe’s speed for applications like research experiments and industry deployment.


Theano is a Python library built on top of NumPy that allows users to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays. It can perform data-intensive computations up to 140x faster than on a CPU. With regards to deep learning, Theano is designed to perform high-dimensional numeric calculations of neural networks. It can be used directly to create deep learning models or wrapper libraries that simplify the process considerably. The framework also offers users extensive unit testing and self-verification. 

Wrap Up

As the adoption of artificial intelligence is growing with every passing day, the developers for deep learning frameworks are working hard to launch high-quality frameworks in the market that can fulfill the varying needs of customers and their organizations. Based on their primary requirement, an appropriate deep learning framework can be selected. 

Whatever decision you make, it remains the fact that knowledge of any of these frameworks is quite helpful when applying for jobs in the field of deep learning. If you have completed an industry project that involved the use of such frameworks, then you are quite likely to grab the attention of hiring managers and stay ahead of the competition. To learn more about these frameworks, you can enroll in a reputed deep learning online course. Apart from giving you theoretical knowledge, it will let you understand how to use the deep learning frameworks practically.