Artificial Neural Networks have reached Grandmaster and even super-human performance across a variety of games: from those involving perfect-information (such as Go) to those involving imperfect-information (such as Starcraft). Such technological developments from AI-labs have ushered concomitant applications across the world of business - where an AI brand tag is fast becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong - an autonomous vehicle crashes; a chatbot exhibits racist behaviour; automated credit scoring processes discriminate on gender etc. - there are often significant financial, legal and brand consequences and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that, 'all the impressive achievements of deep learning amount to just curve fitting'. The key, Judea Pearl suggests, is to replace reasoning by association with causal-reasoning - the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for the New York Times: 'we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets - often using an approach known as Deep Learning - and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space and causality'. In this paper, foregrounding what in 1949 Gilbert Ryle termed a category mistake, I will offer an alternative explanation for AI errors: it is not so much that AI machinery cannot grasp causality, but that AI machinery - qua computation - cannot understand anything at all.

This report documents ideas for improving the field of machine learning, which arose from discussions at the ML Retrospectives workshop at NeurIPS 2019. The goal of the report is to disseminate these ideas more broadly, and in turn encourage continuing discussion about how the field could improve along these axes. We focus on topics that were most discussed at the workshop: incentives for encouraging alternate forms of scholarship, restructuring the review process, participation from academia and industry, and how we might better train computer scientists as scientists. Videos from the workshop can be accessed at Lowe et al. (2019).

These are the lecture notes for FAU's YouTube Lecture "Deep Learning". This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. If you spot mistakes, please let us know!

These are the lecture notes for FAU's YouTube Lecture "Deep Learning". This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. If you spot mistakes, please let us know!

People who wish to advance their career by getting into one of technology's trending fields, data science

Natural Language Processing Explained NLP Tutorial For Beginners - will provide you with a detailed description of NLP (Natural Language Processing). You will also learn about the various applications of NLP in the industry. This Edureka video will provide you with a detailed description of NLP (Natural Language Processing). You will also learn about the various applications of NLP in the industry.

Automation Anywhere is one of the most popular vendors which offers user-friendly and powerful RPA capabilities. Edureka is partnering with Automation Anywhere for this webinar on Decoding futuristic career roles in RPA with Automation Anywhere. The guest speaker for this session would be Arjun Meda who is an RPA evangelist and part of the Bot Store developer relations team. The average salary for an Automation Anywhere Engineer is around $113k per annum – Payscale.com The Robotic Process Automation market is estimated to reach USD 2,467.0 million by 2022, at a CAGR of 30.14% between 2017 and 2022 – MarketsandMarkets.com

In our last post, we demonstrated how to develop a machine learning pipeline and deploy it as a web app using PyCaret and Flask framework in Python. If you haven't heard about PyCaret before, please read this announcement to learn more. In this tutorial, we will use the same machine learning pipeline and Flask app that we built and deployed previously. This time we will demonstrate how to deploy a machine learning pipeline as a web app using the Microsoft Azure Web App Service. In order to deploy a machine learning pipeline on Microsoft Azure, we will have to containerize our pipeline in a software called "Docker".

Deploy Models with TensorFlow Serving and Flask TensorFlow Serving makes the process of taking a model into production easier and faster. Create a web application with Flask to work as an interface to a served model. In this 2-hour long project-based course, you will learn how to deploy TensorFlow models using TensorFlow Serving and Docker, and you will create a simple web application with Flask which will serve as an interface to get predictions from the served TensorFlow model. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser.

To provide awareness of the two most integral branches (i.e. To build appropriate neural models from using state-of-the-art python framework. To build neural models from scratch, following step-by-step instructions. To build end – to – end solutions to resolve real-world problems by using appropriate Machine Learning techniques from a pool of techniques available. To use ML evaluation methodologies to compare and contrast supervised and unsupervised ML algorithms using an established machine learning framework.