Briefly
The IEEE 7010-2020 Standard
The IEEE 7010-2020 Standard for free
OpenAccess Publication System: welcome to OpenScience
We have launched, within the open publishing platform “PubPub“, a research community in English language (its equivalent in Spanish too). The benefit is based on four pillars: 1) readers, 2) authors, 3) reviewers and 4) journals. (This is based in OpenSource software). As part of the Knowledge Futures Group, we’re committed to making PubPub open …
OpenAccess Publication System: welcome to OpenScience Read More »
Open Access courses
Yann LeCun’s Deep Learning Course at CDS
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.
Bayesian Data Analysis: book & course
This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics
Books
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Tutorials
labml.ai Deep Learning Paper Implementations
This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations, and the website renders these as side-by-side formatted notes. We believe these would help you understand these algorithms better.
EvalML: a library for automated machine learning and model understanding
EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions, it is a library for automated machine learning (AutoML) and model understanding, written in Python