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.

One of my favorite places to learn data science is an under-the-radar educational website, DataCamp. DataCamp doesn't get nearly the attention that some of the larger, more well-funded online coding schools get, but, I often find myself on one of their tutorials whenever I'm learning something new related to statistics or machine learning. Over the past few months, I've dedicated at least a few hours a week to learning the underpinnings of automation and, where I find something interesting, to blog about my experience. Unlike almost every other school or tutorial I've encountered, DataCamp has a delightfully distinct and powerful approach to education: every single piece of instruction is paired with a simple example and interactive tutorial. There are no long lectures; there are no complicated diagrams.

I truly believe that artificial intelligence (AI) will shape our future and will bring tremendous impact and applications in industries such as health and agriculture. One of the things that I aim to achieve with dair.ai is to discuss interesting open-source AI technologies that help to address important problems such as medical diagnosis and personalized learning. One of the tools that have caught my attention this week is MedicalTorch (developed by Christian S. Perone), which is an open-source medical imaging analysis tool built on top of PyTorch. It contains a set of loaders, pre-processors and utility functions to efficiently and easily analyze medical images such as those acquired from magnetic resonance imaging (MRI) scans. In this post, I will summarize some of the functionalities offered by the medicaltorch library and how it can be used to conduct medical imaging analysis. Specifically, this will be a tutorial on how to perform spinal cord gray matter segmentation using a technique based on convolutional neural networks (CNNs).

Prime Minister Narendra Modi will attend Monday the fourth edition of the NITI Lecture Series focussed on'leveraging artificial intelligence for inclusive growth', according to an official statement. Modi will attend the lecture series in which the key note address will be delivered by Jensen Huang, president and co-Founder of US-based technology firm NVIDIA Corporation, the Niti Aayog said Sunday. The government think tank said the 2018 theme for the lecture series'AI for All: Leveraging Artificial Intelligence for Inclusive Growth' is part of the National Strategy for Artificial Intelligence aimed at evolving a robust ecosystem in India for AI research and adoption. Union ministers, policy makers, experts from different walks of life along with Niti Aayog vice chairman, CEO, members and other senior officials will also be present on the occasion. The Union Budget 2018 had mandated the Niti Aayog to come up with a national programme on employing artificial intelligence towards national development and since the Aayog has published a National Strategy for artificial intelligence (AI).

As you plan your agenda, artificial intelligence (AI) is undoubtedly a hot topic on your list. This year we have a lot of great technical content on AI, machine learning (ML), and deep learning (DL)--with over 200 breakout sessions, hands-on workshops, deep-dive chalk talks, and more. You'll hear success stories about machine learning on AWS firsthand from customers and partners such as Sony, Moody's, NFL, Intuit, 21st Century Fox, Toyota, and more. This year's re:Invent also includes the AI Summit, where thought leaders in the academic community will share their perspectives on the future of AI. Here are a few highlights of this year's lineup from the re:Invent session catalog to help you plan your event agenda.