The successor to Android Pie will simply be called Android 10, Google has announced, bringing to an end one of tech's most unusual naming conventions. A complete overhaul to the world's most popular mobile operating system was introduced by the technology giant in a blog post. Android's open platform has expanded to tablets, cars, watches and TVs, having first launched for smartphones in 2008. Google claims it is now used with more than 2.5 billion active devices around the world. "As we continue to build Android for everyone in the community, our brand should be as inclusive and accessible as possible," said Sameer Samat, Android's head of product management.

Description: A down-to-earth, shy but confident take on machine learning techniques that you can put to work today.

In this course, we will introduce some of the most widely used ML methods such as regression, classification, model selection, clustering, and dimensionality reduction. We will discuss these methods in a hands-on fashion using coding assignments which include implementations of ML methods using the programming language Python. The course is organized in six rounds: "Introduction", "Regression", "Classification", "Model Validation and Selection", "Clustering" and "Dimensionality Reduction". Each round covers a certain part of the course book and includes a Python notebook with the coding assignment. The course is intended for students of Network university FITech.

In this tutorial, you will learn how to perform video classification using Keras, Python, and Deep Learning.

Everyone's talking about the fast.ai Massive Open Online Course (MOOC) so I decided to have a go at their 2019 deep learning course Practical Deep Learning for Coders, v3. I've always known some deep learning concepts/ideas (I've been in this field for about a year now, dealing mostly with computer vision), but never really understood some intuitions or explanations. I also understand that Jeremy Howard, Rachel Thomas and Sylvain Gugger (follow them on Twitter!) are influential people in the deep learning sphere (Jeremy has a lot of experience with Kaggle competitions), so I hope to gain new insights and intuitions, and some tips and tricks for model training from them. I have so much to learn from these folks.

ABOUT: You want to include a machine learning component in your IT systems? The process is a little more involved than clicking through an AI tutorial on your laptop. It's not just the first working model you run that you need to consider; you also need to think about things like integration, scaling, and testing. What's more, postlaunch, you'll want to continuously adapt your model to respond to the changing environment. Christoph and Arif will give an introduction into Continuous Delivery for Machine Learning (CD4ML) - a set of tools and processes that ensure that software under development in Machine Learning can be reliably released to production at any time and with high frequency.

Algorithms: Design and Analysis of is a textbook designed for the undergraduate and postgraduate students of computer science engineering, information technology, and computer applications. It helps the students to understand the fundamentals and applications of algorithms. The book has been divided into four sections: Algorithm Basics, Data Structures, Design Techniques and Advanced Topics. The first section explains the importance of algorithms, growth of functions, recursion and analysis of algorithms. The second section covers the data structures basics, trees, graphs, sorting in linear and quadratic time. Section three discusses the various design techniques namely, divide and conquer, greedy approach, dynamic approach, backtracking, branch and bound and randomized algorithms used for solving problems in separate chapters.

I am a Mechanical engineer by education. And I started my career with a core job in the steel industry. But I didn't like it and so I left that. I made it my goal to move into the analytics and data science space somewhere around in 2013. From then on, it has taken me a lot of failures and a lot of efforts to shift.

There are many options to do the course work, e.g., AWS, PaperSpace, etc., but I found Google Colaboratory is the best and easiest option. Here is the instruction for the Fast.ai Unlike other option, Colab guarantees to work because Google starts with a clean, new virtual machine (VM) every time, and in the first few steps in the notebooks, it loads the required correct version of Pytorch and Fast.ai.

In science and engineering, it is an ubiquitous problem to estimate the probability of event based on Monte Carlo simulation. For instance, in engineering technology, a critical c oncern is the probability of failure or risk, which is generally considered as the probability that certain pre -specified requirements for the relevant system are violated in the presence of uncertainties. Ever since th e advent of modern computers, extensive research works have been devoted to quantitative approaches o f risk evaluation for engineering systems (see, e.g., [1, 8, 9, 11, 16, 18, 20] and the references therein). I n additional to theoretical development, many softwares have been developed for risk evaluation. For exam ple, for control systems, a software called RACT has been developed for evaluating the risk of uncertain syste ms [7, 21]. Many softwares such as APMC [13], PRISM [15], UPPAAL [6], have been developed for evaluating t he risk of stochastic discrete event systems (see, [1] and the references therein). One of the remarkable achievements of existing theories and softw ares is the rigorous control of error in the estimation of probability, that is, the probability of relevant ev ent can be evaluated with certified reliability. Theoretically, for a priori given α, δ (0, 1), existing methods are able to produce an estimate null p for the true value of the probability p so that one can be 100(1 δ)% confident that null p p α holds. 1 Unfortunately, existing methods suffer from huge computational complexity as the margin of absolute error α is small, e.g. 10