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Some Lesser-Known Deep Learning Libraries


In this article, we have compiled a list of some of the lesser-known Deep Learning libraries. This is an open source framework for distributed deep learning on big-data clusters. This is a library for TensorFlow that allows users to define preprocessing pipelines and run these using large scale data processing frameworks, while also exporting the pipeline in a way that can be run as part of a TensorFlow graph. The downhill package provides algorithms for minimizing scalar loss functions that are defined using Theano. Knet is the Koç University deep learning framework that supports GPU operation and automatic differentiation using dynamic computational graphs for models defined in plain Julia.

Top 12 Javascript Libraries for Machine Learning


Rapidly evolving technologies like Machine Learning, Artificial Intelligence, and Data Science were undoubtedly among the most booming technologies of this decade. The s specifically focusses on Machine Learning which, in general, helped improve productivity across several sectors of the industry by more than 40%. It is a no-brainer that Machine Learning jobs are among the most sought-after jobs in the industry. There are various programming languages, such as JavaScript, Python, and many others, that act as a reputable entry point into the world of Machine Learning, and that brings us to the goal behind this write-up. Through this article, we will try to shed some light on more than 10 of the most popular JavaScript libraries to help you learn Machine Learning.

Shapash- Python Library To Make Machine Learning Interpretable


The above quote is quite interesting and yes, they speak the truth most of us are from the technical field so we probably know about what machine learning is? it is the current worldwide digital technology ruled over the world. If you are familiar with machine learning then you come across the words data, train, test, accuracy, and many more, and many of you are capable of writing machine learning scripts if you notice that we didn't see the background calculations of the machine learning models because machine learning is not interpretable. Many people say that the machine learning models are the black box models, suppose if we give input there are a lot of calculations are happening inside and we got the output, that particular calculation based on what feature we are actually giving. Suppose we give the input of 5 features inside this, it may be a situation where some of the feature value may be increasing and some of them are decreasing, so we not able to see this, but python has a beautiful library which makes a machine learning model interpretable by this we can able to understand that underground calculations. This beautiful library is developed by a group of MAIF Data Scientists.

10 Best Frameworks and Libraries for AI - DZone AI


Artificial intelligence has existed for a long time. However, it has become a buzzword in recent years due to huge improvements in this field. AI used to be known as a field for total nerds and geniuses, but due to the development of various libraries and frameworks, it has become a friendlier IT field and has lots of people going into it.

Open sourcing our neural network libraries – Blog – Neural Network Libraries


We are very excited today to open-source Sony's neural network libraries, a software that helps the workflows of deep learning research, development and production. Neural networks are the core ingredients of deep learning models. Deep learning has first received huge attention in 2012, when an image classification model accomplished a great leap in image recognition, winning against other models with a large gap, in the ImageNet Large Scale Visual Recognition Challenge. Nowadays, deep learning is widely used in many applications as an essential tool, not only as a pattern recognition algorithm, but also as a tool capable of modeling black-box systems. The architectures of deep learning models vary at a wide range, in various aspects; from small to large, from feed-forward to recurrent, from unsupervised to supervised and so on.