A deep learning framework is a software package used by researchers and data scientists to design and train deep learning models. The idea with these frameworks is to allow people to train their models without digging into the algorithms underlying deep learning, neural networks, and machine learning. These frameworks offer building blocks for designing, training, and validating models through a high-level programming interface. Widely used deep learning frameworks such as PyTorch, TensorFlow, MXNet, and others can also use GPU-accelerated libraries such as cuDNN and NCCL to deliver high-performance multi-GPU accelerated training. An open-source software library created by Google, TensorFlow is a popular tool for machine learning, especially for training deep neural networks.
From personalizing customer experience to automating processes, Deep Learning applications are offering smart solutions to businesses across industries, opening up a world of opportunities for them. Deep Learning algorithms use sophisticated structures, such as Convolutional Neural Networks, belief networks, or recurrent neural networks. Effective DL frameworks also help simplify the implementation of large and complex models like Convolutional Neural Networks. In this post, we present the top Deep Learning frameworks preferred by data scientists and Deep Learning experts across the globe. We have also included the major pros and cons of each framework, enabling you to choose the right one for your upcoming project.
To understand how to accomplish a specific task in TensorFlow, you can refer to the TensorFlow tutorials. Keras is one of the most popular and open-source neural network libraries for Python. Initially designed by a Google engineer for ONEIROS, short for Open-Ended Neuro Electronic Intelligent Robot Operating System, Keras was soon supported in TensorFlow's core library making it accessible on top of TensorFlow.
We have seen an explosion in developer tools and platforms related to machine learning and artificial intelligence during the last few years. From cloud-based cognitive APIs to libraries to frameworks to pre-trained models, developers make many choices to infuse AI into their applications. AI engineers and researchers choose a framework to train machine learning models. These frameworks abstract the underlying hardware and software stack to expose a simple API in languages such as Python and R. For example, an ML developer can leverage the parallelism offered by GPUs to accelerate a training job without changing much of the code written for the CPU. These frameworks expose simpler APIs that translate to complex mathematical computations and numerical analysis often needed for training the machine learning models. Apart from training, the machine learning frameworks simplify inference -- the process of utilizing a trained model for performing prediction or classification of live data.
AI coupled with the right deep learning framework has truly amplified the overall scale of what businesses can achieve and obtain within their domains. The machine learning paradigm is continuously evolving. The key is to shift towards developing machine learning models that run on mobile in order to make applications smarter and far more intelligent. Deep learning is what makes solving complex problems possible. As put in this article, Deep Learning is basically Machine Learning on steroids.