PyTorch vs TensorFlow for building deep learning models
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.
A Guide to Chainer: A Flexible Toolkit For Neural Networks
Implementing neural networks necessitates the use of a variety of specialized building elements, such as multidimensional arrays, activation functions, and automatic differentiation.
Difference Between PyTorch And PySyft
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.
How PyTorch Is Challenging TensorFlow Lately
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) […]
Hands-On Workshop: Learn About oneAPI AI Analytics Toolkit
Get a hands-on understanding of using the Intel oneAPI AI Analytics toolkit to maximise the performance of heterogeneous computing with this free workshop.
Hands-On Guide To Torch-Points3D: A Modular Deep Learning Framework For 3D Data
Torch-Points3D is a flexible and powerful framework that aims to make deep learning on 3D data both more accessible and reproducible.
How To Automate The Stock Market Using FinRL (Deep Reinforcement Learning Library)?
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 […]
Yoshua Bengio Proposes New Inductive Biases to Augment Deep Learning
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 […]
Popular Deep Learning Frameworks: An Overview
In this article, I’ll discuss the deep learning frameworks available for different programming language interfaces.
Uber Open-Sources Neuropod – A Library To Run Deep Learning Models Using Multiple Frameworks
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 […]
Why Tech Giants Are Pinning Their AI Strategy On Deep Learning Frameworks
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 […]
Does Microsoft Cognitive Toolkit Really Lag Behind TensorFlow And PyTorch In Deep Learning?
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 […]
