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Machine Learning

Machine Learning
About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. Related:  video_tuts

Practical Deep Learning For Coders—18 hours of lessons for free CS 229: Machine Learning Object Detection with Luminoth In this article, we’ll see how we can use the Luminoth library to detect objects in pictures or videos. Luminoth is an open source computer vision library built in Python and based on TensorFlow and Sonnet. This library was developed by Tryolabs. Tryolabs | Machine Learning & Data Science ConsultingTryolabs is a Machine Learning and Data Science consulting firm that helps companies build and implement custom… Sonnet is a TensorFlow-based neural network library. Installation We can install Luminoth via a quick pip install command: Luminoth provides pre-trained checkpoints we can use. The beauty of this library is that it makes the work of object detection easy. Source In order to do this, we’ll need to first fire up our terminal. Managing checkpoints is done using the lumi checkpoint command, which will download pre-trained models that we’ll use to make predictions. Let’s now look at our downloaded checkpoints. We can clearly see that we have two checkpoints: or Conclusion

CS446: Fall 2017 - RELATE Course Description The goal of Machine Learning is to build computer systems that can adapt and learn from their experience. This course will study the theory and application of learning methods that have proved valuable and successful in practical applications. We review the theory of machine learning in order to get a good understanding of the basic issues in this area, and present the main paradigms and techniques needed to obtain successful performance in application areas such as natural language and text understanding, speech recognition, computer vision, data mining, adaptive computer systems and others. Topics to be covered include: Linear/Logistic RegressionVariable Selection / SparsityOptimization - Gradient DescentSupport Vector MachinesConvolutional/Recurrent Neural NetworksClusteringGraphical ModelsExpectation MaximizationVariational InferenceGenerative Adversarial NetworksMultilabel ClassificationStructured Prediction Required text Exams Homework Scribe Scribe Submission Project

Statistical Learning | Stanford Lagunita About This Course This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). Course Staff Trevor Hastie Trevor Hastie is the John A Overdeck Professor of Statistics at Stanford University. Rob Tibshirani

Luminoth 0.1: Open source Computer Vision toolkit Luminoth is an open-source computer vision toolkit, built upon Tensorflow and Sonnet. We just released a new version, so this is a good time as any to dive into it! Version 0.1 brings several very exciting improvements: An implementation of the Single Shot Multibox Detector (SSD) model was added, a much faster (although less accurate) object detector than the already-included Faster R-CNN. This allows performing object detection in real-time on most modern GPUs, allowing the processing of, for instance, video streams.Some tweaks to the Faster R-CNN model, as well as a new base configuration, making it reach results comparable to other existing implementations when training on the COCO and Pascal datasets.Checkpoints for both SSD and Faster R-CNN models are now provided, trained on the Pascal and COCO datasets, respectively, and providing state-of-the-art results. We’ll now explore each of these features through examples, by incrementally building our own detector. $ pip install luminoth

MIT 6.S094: Deep Learning for Self-Driving Cars Introduction to mathematical thinking About the Course NOTE: For the Fall 2015 session, the course website will go live at 10:00 AM US-PST on Saturday September 19, two days before the course begins, so you have time to familiarize yourself with the website structure, watch some short introductory videos, and look at some preliminary material. The goal of the course is to help you develop a valuable mental ability – a powerful way of thinking that our ancestors have developed over three thousand years. Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system. School math typically focuses on learning procedures to solve highly stereotyped problems. The course is offered in two versions. Course Syllabus Instructor’s welcome and introduction 1. 2. 3. 4. 5. 6. 7. 8. 9. Recommended Background High school mathematics. Suggested Readings There is one reading assignment at the start, providing some motivational background. Course Format

luminoth Luminoth is an open source toolkit for computer vision. Currently, we support object detection, but we are aiming for much more. It is built in Python, using [TensorFlow]( and [Sonnet]( Read the full documentation [here]( ! > DISCLAIMER: Luminoth is still alpha-quality release, which means the internal and external interfaces (such as command line) are very likely to change as the codebase matures. # Installation Luminoth currently supports Python 2.7 and 3.4–3.6. ## Pre-requisites To use Luminoth, [TensorFlow]( must be installed beforehand. ## Installing Luminoth Just install from PyPI: `bash pip install luminoth ` Optionally, Luminoth can also install TensorFlow for you if you install it with pip install luminoth[tf] or pip install luminoth[tf-gpu], depending on the version of TensorFlow you wish to use. ### Google Cloud `bash pip install luminoth[gcloud] ` # Usage

IDEAL MOOC Implementation of DEvelopmentAl Learning - Free Massive Open Online Course - from October 13th to December 7th 2014. The IDEAL MOOC is over but you can still use it as a "permamooc". You can follow the lessons at your own pace and engage with the community by posting comments and new posts in our dedicated Google+ community. The IDEAL MOOC will teach you the cognitive science background and the programming bases to design robots and virtual agents capable of autonomous cognitive development driven by their intrinsic motivation. The IDEAL MOOC welcomes all audience without prerequisite. Curious persons who wish to discover the current state of the art of Developmental AI and understand its underlying concepts. The IDEAL MOOC is designed to satisfy these three types of participants. Programming activities will be optional. The first edition will start in October 2014. Figure 1: Progressive organization of the IDEAL MOOC. Video 2: Example of self-programming. Learning to what end? Support

Learn Android Programming From Scratch - Beta The course provide an introduction to Android Programming and allows someone with a basic knowledge of programming to start creating Android Applications. It is a light course to cover fundamentals of Android. It will teach you the Android programming Paradigm and how to think while creating an Android program. We will cover topics such as Installation, Activities, Layouts, List Views, SQLite, Services Multimedia and Google Play. The course is divided into 6 units covering each of the above topics. You will start with basic installation process and will move on to the first Android example which will outline the structure of Android Programs. It will be a fun learning course that is sure to help you get going with Android programming.

A Gentle Introduction to Object Recognition With Deep Learning Last Updated on July 5, 2019 It can be challenging for beginners to distinguish between different related computer vision tasks. For example, image classification is straight forward, but the differences between object localization and object detection can be confusing, especially when all three tasks may be just as equally referred to as object recognition. Image classification involves assigning a class label to an image, whereas object localization involves drawing a bounding box around one or more objects in an image. Object detection is more challenging and combines these two tasks and draws a bounding box around each object of interest in the image and assigns them a class label. In this post, you will discover a gentle introduction to the problem of object recognition and state-of-the-art deep learning models designed to address it. After reading this post, you will know: Let’s get started. Overview This tutorial is divided into three parts; they are: What is Object Recognition? Papers

Deep Learning Course ⇢ François Fleuret You can find here the materials for the EPFL course EE-559 “Deep Learning”. These documents are under heavy development, in particular due to pytorch updates. Please avoid to distribute the pdf files, and share the URL of this page instead. Info sheet: dlc-info-sheet.pdf We will use the pytorch framework for implementations. Thanks to Adam Paszke, Alexandre Nanchen, Xavier Glorot, Matus Telgarsky, and Diederik Kingma, for their help, comments, or remarks. Course material You will find here the slides I use to teach, which are full of “animations” and not convenient to print or use as notes, and the handouts, with two slides per pages. Practical session prologue Helper python prologue for the practical sessions: Lecture 1 (Feb 21, 2018) – Introduction and tensors Lecture 2 (Feb 28, 2018) – Machine learning fundamentals Empirical risk minimization, capacity, bias-variance dilemma, polynomial regression, k-means and PCA. Cross-entropy, L1 and L2 penalty.