This article we will walk you through and compare the code usability and ease to use of TensorFlow and PyTorch on the most widely used MNIST dataset to classify handwritten digits.
Implementing neural networks necessitates the use of a variety of specialized building elements, such as multidimensional arrays, activation functions, and automatic differentiation.
PySyft decouples private data from model training, using federated learning, differential privacy, multi-party computation (MPC) within the main deep learning framework like PyTorch, Keras and TensorFlow.
Google’s TensorFlow and Facebook’s PyTorch are the most popular machine learning frameworks. The former has a two-year head start over PyTorch (released in 2016). TensorFlow’s popularity reportedly declined after PyTorch bursted into the scene. However, Google released a more user-friendly TensorFlow 2.0 in January 2019 to recover lost ground. Interest over time for TensorFlow (top) […]
Get a hands-on understanding of using the Intel oneAPI AI Analytics toolkit to maximise the performance of heterogeneous computing with this free workshop.
Torch-Points3D is a flexible and powerful framework that aims to make deep learning on 3D data both more accessible and reproducible.
Preface First, let’s discuss all the buzzwords, and then we will move to the implementation part where we code a starter project in stock market trading. Reinforcement learning Reinforcement learning is one of the three basic paradigms of Machine learning alongside supervised and unsupervised learning. It concerned with how intelligent agents take action by themselves […]
Recently, two researchers from the University of Montreal, Yoshua Bengio and Anirudh Goyal proposed new inductive biases that are meant to boost the deep learning performance. This paper focuses mainly on those inductive biases that concern mostly higher-level and sequential conscious processing. To be specific, this research’s main idea is to bridge the gap between […]
In this article, I’ll discuss the deep learning frameworks available for different programming language interfaces.
Recently, Uber open-sourced Neuropod, a library that provides a uniform interface to run deep learning models from multiple frameworks in C++ and Python. This library is Uber ATG’s open-sourced deep learning inference engine that makes deep learning frameworks look the same when running a model. With the advancements of various frameworks, the technique like deep […]
There’s one aspect that has affected the growth of deep learning research — the proliferation of deep learning frameworks. Popular Deep Learning frameworks such as TensorFlow (Google), PyTorch (one of the newest frameworks that is rapidly gaining popularity), Caffe, MXNet and Keras among others have helped DL researchers achieve human-level efficiencies on tasks such as […]
The arrival of deep learning frameworks in the public domain has kick-started a framework war of sorts. In this article, we discuss how Microsoft Cognitive Toolkit, previously known as CNTK, stacks up against the ever-popular TensorFlow and PyTorch. While Google’s TensorFlow is immensely popular among developers and is also known for its better documentation, Microsoft […]