This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

Courses The major educational initiative of the JHUDSL is to create open-source online courses delivered through a range of platforms including Youtube, Github, Leanpub, and Coursera. Welcome to the "Introduction to Deep Learning" course! In the first week you'll learn about linear models and stochatic optimization methods. Please note that this is an advanced course and we assume basic knowledge of machine learning. I am currently working as a data science researcher and trainee at Jheronimus Academy of Data Science.

AI and data science is transforming business. The CxO summit will give you the knowledge and connections to be at the forefront.

After introducing Artificial Intelligence (AI) as an optional skill subject in its schools, the CBSE is now pushing to integrate it with other subjects across all classes. It plans to begin training teachers to this end in the coming months. AI was introduced as an optional skill subject for class IX in the beginning of this year, with the curriculum introducing students to the three domains of AI -- data, computer vision and natural language processing. "…AI is a cognitive science which can be linked to various subjects that concern themselves with cognition and reasoning. Almost every school subject would fall in this domain… It is therefore mandated by the CBSE that all its schools would begin to integrate AI with other disciplines from classes I-XII," read a handbook prepared by the board.

Contrastive unsupervised representation learning (CURL) is the state-of-the-art technique to learn representations (as a set of features) from unlabelled data. While CURL has collected several empirical successes recently, theoretical understanding of its performance was still missing. In a recent work, Arora et al. ( 2019) provide the first generalisation bounds for CURL, relying on a Rademacher complexity. We extend their framework to the flexible PAC-Bayes setting, allowing to deal with the non-iid setting. We present PAC-Bayesian generalisation bounds for CURL, which are then used to derive a new representation learning algorithm. Numerical experiments on real-life datasets illustrate that our algorithm achieves competitive accuracy, and yields generalisation bounds with non-vacuous values.

Editor's Note: SHRM has partnered with TrainingIndustry.com to bring you relevant articles on key HR topics and strategies. The National Center for Women in Technology (NCWIT) predicts that while there will be 3.5 million "computing-related" jobs in the U.S. by 2026, 83% of them could go unfilled due to a lack of college graduates with related degrees. To meet this demand, organizations must reskill their workforces and look to candidates who have learned in-demand technical skills through alternate forms of education. In recent years, coding bootcamps have succeeded in training a diverse group of workers for careers as web, full-stack and software developers, among other roles, as well as reskilling people already in those professions. However, several major coding bootcamps have also closed in recent years, including Dev Bootcamp and The Iron Yard in 2017.

Microsoft's attempt at engaging millennials with artificial intelligence has backfired hours into its launch, with waggish Twitter users teaching its chatbot how to be racist. The company launched a verified Twitter account for "Tay" – billed as its "AI fam from the internet that's got zero chill" – early on Wednesday. The chatbot, targeted at 18- to 24-year-olds in the US, was developed by Microsoft's technology and research and Bing teams to "experiment with and conduct research on conversational understanding". "Tay is designed to engage and entertain people where they connect with each other online through casual and playful conversation," Microsoft said. "The more you chat with Tay the smarter she gets."

Thursday, October 10th at 10am PDT / 1pm EDT Machine Learning (ML) isn't just a buzzword anymore -- it's affecting how we communicate, shop, live and respond to critical IT incidents. While some IT and engineering leaders are concerned that implementing ML in their incident response processes will render their employees obsolete, others simply don't trust a machine to automate sensitive work. However, when implemented correctly, we believe ML can enhance -- not replace -- the work your teams are already doing, without requiring much or any effort on your part. To learn how, join us for a live webinar with the VictorOps Head of Data Science and resident Machine Learning expert, Will Stanton. We'll focus on: Demystifying AI, Machine Learning and AIOps: Definitions, anecdotes and fun facts to help you understand the technologies shaping the future of engineering and IT The meaning of "human-centered" ML: How VictorOps delivers insights to the end user exactly when and where they need it -- all while keeping them in complete control What makes ML work well: Ideal use cases for getting started with Machine Learning in incident response, effortlessly Demystifying AI, Machine Learning and AIOps: Definitions, anecdotes and fun facts to help you understand the technologies shaping the future of engineering and IT The meaning of "human-centered" ML: How VictorOps delivers insights to the end user exactly when and where they need it -- all while keeping them in complete control What makes ML work well: Ideal use cases for getting started with Machine Learning in incident response, effortlessly The meaning of "human-centered" ML: How VictorOps delivers insights to the end user exactly when and where they need it -- all while keeping them in complete control

This class provides a practical introduction to deep learning, including theoretical motivations and how to implement it in practice. As part of the course we will cover multilayer perceptrons, backpropagation, automatic differentiation, and stochastic gradient descent. Moreover, we introduce convolutional networks for image processing, starting from the simple LeNet to more recent architectures such as ResNet for highly accurate models. Secondly, we discuss sequence models and recurrent networks, such as LSTMs, GRU, and the attention mechanism. Throughout the course we emphasize efficient implementation, optimization and scalability, e.g. to multiple GPUs and to multiple machines. The goal of the course is to provide both a good understanding and good ability to build modern nonparametric estimators.

Imagine you're taking an online business class -- the kind where you watch video lectures and then answer questions at the end. But this isn't a normal class, and you're not just watching the lectures: They're watching you back. Every time the facial recognition system decides that you look bored, distracted, or tuned out, it makes a note. And after each lecture, it only asks you about content from those moments. This isn't a hypothetical system; it's a real one deployed by a company called Nestor.