Online Courses Udemy - Computer Vision: Python OCR & Object Detection Quick Starter, Quick Starter for Optical Character Recognition, Image Recognition Object Detection and Object Recognition using Python Hot & New Created by Abhilash Nelson English Students also bought Python 3.8 for beginners 2020 Docker for Beginners Python Programming from Basics to Advanced FL Studio 20 - EDM Masterclass Music Production in FL Studio Microsoft Azure Data Lake Storage Service (Gen1 & Gen2) Geospatial Data Analyses & Remote Sensing: 4 Classes in 1 Preview this course GET COUPON CODE Description Hi There! welcome to my new course'Optical Character Recognition and Object Recognition Quick Start with Python'. This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document.

Here are five reasons more than half of the employers are interested in artificial intelligence. Through AI, you can train more people and improve their learning experience. Gaps in knowledge can be identified and resolved efficiently through AI assessments and training (i.e., turning downtime into productive learning-time). Your team will be more confident in doing their jobs and more prepared than ever. Creating credibility is the key to successful AI implementation.

Some would say that being an AI expert is the job of the century. I think that point of view puts a lot of pressure on the candidates who plan to take a shot at the artificial intelligence industry. It might create the impression that only the most brilliant students who have had straight A's since high school are good enough for an AI role. Learning and practicing AI is not a cakewalk. You need to put in the hours and struggle for solutions.

One of the most fundamental aspects of any machine learning algorithm is the training data used by the algorithm. We introduce the novel concept of ɛ- approximation of datasets, obtaining datasets which are much smaller than or are significant corruptions of the original training data while maintaining similar model performance. We introduce a meta-learning algorithm called Kernel Inducing Points (KIP) for obtaining such remarkable datasets, inspired by the recent developments in the correspondence between infinitely-wide neural networks and kernel ridge-regression (KRR). For KRR tasks, we demonstrate that KIP can compress datasets by one or two orders of magnitude, significantly improving previous dataset distillation and subset selection methods while obtaining state of the art results for MNIST and CIFAR-10 classification. Furthermore, our KIP -learned datasets are transferable to the training of finite-width neural networks even beyond the lazy-training regime, which leads to state of the art results for neural network dataset distillation with potential applications to privacy-preservation. Datasets are a pivotal component in any machine learning task. Typically, a machine learning problem regards a dataset as given and uses it to train a model according to some specific objective. In this work, we depart from the traditional paradigm by instead optimizing a dataset with respect to a learning objective, from which the resulting dataset can be used in a range of downstream learning tasks. Our work is directly motivated by several challenges in existing learning methods. Kernel methods or instance-based learning (Vinyals et al., 2016; Snell et al., 2017; Kaya & Bilge, 2019) in general require a support dataset to be deployed at inference time. Achieving good prediction accuracy typically requires having a large support set, which inevitably increases both memory footprint and latency at inference time--the scalability issue. It can also raise privacy concerns when deploying a support set of original examples, e.g., distributing raw images to user devices. Additional challenges to scalability include, for instance, the desire for rapid hyper-parameter search (Shleifer & Prokop, 2019) and minimizing the resources consumed when replaying data for continual learning (Borsos et al., 2020). A valuable contribution to all these problems would be to find surrogate datasets that can mitigate the challenges which occur for naturally occurring datasets without a significant sacrifice in performance.

This is an edited version of my undergraduate thesis, submitted to the Harvard Mathematics Department in May 2020. It differs from the original thesis in one major respect, namely that this version omits the proofs of a number of theorems that are readily-available in other expositions. Whereas the original version reproduced these proofs in full, this version simply contains references to these proofs in other works. This thesis is built upon an extensive body of prior work in learning theory, graph theory, differential geometry, and manifold learning. In particular, I would like to thank Professors Lorenzo Rosasco and Tomaso Poggio for their lectures on statistical learning theory, Professor Daniel Spielman for his notes on spectral graph theory, Professor Yaiza Canzani for her notes on analysis on manifolds, and Professor Mikhail Belkin for his work on manifold learning. Finally, I wish to thank those people without whom I could never have written this thesis: my family, friends, and wonderful advisor Professor Arjun Manrai. Unlike the manifolds discussed herein, their support was truly boundless. I hope you enjoy and learn something from this thesis! If you have comments, corrections, or would like to contact me for anything else, feel free to email me.

In this paper, we study efficient differentially private alternating direction methods of multipliers (ADMM) via gradient perturbation for many machine learning problems. For smooth convex loss functions with (non)-smooth regularization, we propose the first differentially private ADMM (DP-ADMM) algorithm with performance guarantee of $(\epsilon,\delta)$-differential privacy ($(\epsilon,\delta)$-DP). From the viewpoint of theoretical analysis, we use the Gaussian mechanism and the conversion relationship between R\'enyi Differential Privacy (RDP) and DP to perform a comprehensive privacy analysis for our algorithm. Then we establish a new criterion to prove the convergence of the proposed algorithms including DP-ADMM. We also give the utility analysis of our DP-ADMM. Moreover, we propose an accelerated DP-ADMM (DP-AccADMM) with the Nesterov's acceleration technique. Finally, we conduct numerical experiments on many real-world datasets to show the privacy-utility tradeoff of the two proposed algorithms, and all the comparative analysis shows that DP-AccADMM converges faster and has a better utility than DP-ADMM, when the privacy budget $\epsilon$ is larger than a threshold.

Explicit and implicit bias clouds human judgement, leading to discriminatory treatment of minority groups. A fundamental goal of algorithmic fairness is to avoid the pitfalls in human judgement by learning policies that improve the overall outcomes while providing fair treatment to protected classes. In this paper, we propose a causal framework that learns optimal intervention policies from data subject to fairness constraints. We define two measures of treatment bias and infer best treatment assignment that minimizes the bias while optimizing overall outcome. We demonstrate that there is a dilemma of balancing fairness and overall benefit; however, allowing preferential treatment to protected classes in certain circumstances (affirmative action) can dramatically improve the overall benefit while also preserving fairness. We apply our framework to data containing student outcomes on standardized tests and show how it can be used to design real-world policies that fairly improve student test scores. Our framework provides a principled way to learn fair treatment policies in real-world settings.

Google is one of the pioneers in ML/AI based solutions.

Udemy Online Courses - Create a SHMUP with Unity 3D, Build a Shoot em up game for mobile with C# and Unity 3D 4.7 (50 ratings), Created by Romi Fauzi, English [Auto-generated] Create a complete SHMUP game like Skyforce Upgrade (include timed upgrades) and Save features. Upgrade (include timed upgrades) and Save features. In this course we will create a full Shoot Em Up game (Skyforce, Raiden) from scratch in Unity. You will learn about object oriented programming and have an overall better understanding of C#. We will provide you with all the assets needed to create the game (including 3d models, audio), feel free to use these assets in your own games.

This is currently in an Early Bird Beta access, meaning we are still going to be continually adding content to the course (even though we are already at over 20 hours of content!) Since we're still adding content and taking student feedback as we complete the course through the start of 2021, students who enroll now will get access to a wide variety of benefits! You will get exclusive access to weekly live video streams where we will go through interactive machine learning projects! You'll be able to directly ask questions during the streams that will coincide with section launches corresponding to new machine learning algorithms added to the course content! These weekly streams will also include live Q&A with the instructor of the course, Jose Portilla. We will also be taking in student feedback to shape certain upcoming streaming projects.