Google loves machine learning (ML). Now, it's launched a new course module that aims to help you, a human, recognize your own bias before training ML models. Named'Fairness,' the course is 70 minutes on how humans are compromising machine learning models. As ML practitioners build, evaluate, and deploy machine learning models, they should keep fairness considerations (such as how different demographics of people will be affected by a model's predictions) in the forefront of their minds. Additionally, they should proactively develop strategies to identify and ameliorate the effects of algorithmic bias.

Hospitals are on the radar of hackers as "soft" and valuable targets. The modern medical facility is connected to the internet in a multitude of ways. These connections include email clients, multi-location data integration systems, medical devices, and off-premise vendor support; all which leave hospitals and clinical networks extremely vulnerable to attack.

During the first phase of this program, students take Udacity's "Introduction to Deep Learning with PyTorch" course. The duration of this course is two months. Program participants will receive support from community managers throughout their learning experience in this course, and will be part of a dynamic student community and network of scholars. The top 300 students from the first phase of the program will earn a full scholarship to Udacity's Deep Learning Nanodegree program, where they'll cover Convolutional and Recurrent Neural Networks, Generative Adversarial Networks, Deployment, and more. Students will use PyTorch, and have access to GPUs to train models faster, as they learn from authorities like Sebastian Thrun, Ian Goodfellow, Jun-Yan Zhu, and Andrew Trask.

Cloud OnBoard Big Data and Machine Learning is a free, full-day training event that will provide you with a detailed view of Google Cloud Platform's data processing and machine learning capabilities. Through a combination of instructor-led presentations and demos, you will learn how to leverage the ease, flexibility, and power of data and machine learning tools like Cloud Dataproc, Dataflow, Machine Learning APIs, Tensorflow and BigQuery. Who should attend this event? The topics that will be discussed at Cloud OnBoard Big Data and Machine Learning are best-suited for data analysts, data scientists and business analysts. This event will provide powerful insights if you're responsible for:

Fun and Easy Machine Learning Course in Keras and Python Promotional Video (Coupon Code in Description) https://www.udemy.com/machine-learnin... Limited Time - Discount Coupon Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing world of Machine Learning. Each section consists of fun and intriguing white board explanations like this one with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.

Reconstructing the position of an interaction for any dual-phase time projection chamber (TPC) with the best precision is key to directly detect Dark Matter. Using the likelihood-free framework, a new algorithm to reconstruct the 2-D (x; y) position and the size of the charge signal (e) of an interaction is presented. The algorithm uses the charge signal (S2) light distribution obtained by simulating events using a waveform generator. To deal with the computational effort required by the likelihood-free approach, we employ the Bayesian Optimization for Likelihood-Free Inference (BOLFI) algorithm. Together with BOLFI, prior distributions for the parameters of interest (x; y; e) and highly informative discrepancy measures to perform the analyses are introduced. We evaluate the quality of the proposed algorithm by a comparison against the currently existing alternative methods using a large-scale simulation study. BOLFI provides a natural probabilistic uncertainty measure for the reconstruction and it improved the accuracy of the reconstruction over the next best algorithm by up to 15% when focusing on events over a large radii (R > 30 cm). In addition, BOLFI provides the smallest uncertainties among all the tested methods.

In the era of big data, logistic planning can be made data-driven to take advantage of accumulated knowledge in the past. While in the movie industry, movie planning can also exploit the existing online movie knowledge library to achieve better results. However, it is ineffective to solely rely on conventional heuristics for movie planning, due to a large number of existing movies and various real-world factors that contribute to the success of each movie, such as the movie genre, available budget, production team (involving actor, actress, director, and writer), etc. In this paper, we study a "Blockbuster Planning" (BP) problem to learn from previous movies and plan for low budget yet high return new movies in a totally data-driven fashion. After a thorough investigation of an online movie knowledge library, a novel movie planning framework "Blockbuster Planning with Maximized Movie Configuration Acquaintance" (BigMovie) is introduced in this paper. From the investment perspective, BigMovie maximizes the estimated gross of the planned movies with a given budget. It is able to accurately estimate the movie gross with a 0.26 mean absolute percentage error (and 0.16 for budget). Meanwhile, from the production team's perspective, BigMovie is able to formulate an optimized team with people/movie genres that team members are acquainted with. Historical collaboration records are utilized to estimate acquaintance scores of movie configuration factors via an acquaintance tensor. We formulate the BP problem as a non-linear binary programming problem and prove its NP-hardness. To solve it in polynomial time, BigMovie relaxes the hard binary constraints and addresses the BP problem as a cubic programming problem. Extensive experiments conducted on IMDB movie database demonstrate the capability of BigMovie for an effective data-driven blockbuster planning.

Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.

This tutorial will teach you how to perform object tracking using dlib and Python. After reading today's blog post you will be able to track objects in real-time video with dlib. A couple months ago we discussed centroid tracking, a simple, yet effective method to (1) assign unique IDs to each object in an image and then (2) track each of the objects and associated IDs as they move around in a video stream. The biggest downside to this object tracking algorithm is that a separate object detector has to be run on each and every input frame -- in most situations, this behavior is undesirable as object detectors, including HOG Linear SVM, Faster R-CNNs, and SSDs can be computationally expensive to run. Is such a method possible?

Having a solid grasp on deep learning techniques feels like acquiring a super power these days. From classifying images and translating languages to building a self-driving car, all these tasks are being driven by computers rather than manual human effort. Deep learning has penetrated into multiple and diverse industries, and it continues to break new ground on an almost weekly basis. Understandably, a ton of folks are suddenly interested in getting into this field. But where should you start? What are the core concepts that actually make up this complex yet intriguing field? I'm excited to pen down a series of articles where I will break down the basic components that every deep learning enthusiast should know thoroughly. My inspiration comes from deeplearning.ai, In this article, I will be writing about Course 1 of the specialization, where the great Andrew Ng explains the basics of Neural Networks and how to implement them. Note: We will follow a bottom-up approach throughout this series – we will first understand the concept from the ground-up, and only then follow it's implementation.