MIT 6.S094: Deep Learning for Self-Driving Cars The Unreasonable Effectiveness of Recurrent Neural Networks There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simple your model is to the quality of the results you get out of it blows past your expectations, and this was one of those times. What made this result so shocking at the time was that the common wisdom was that RNNs were supposed to be difficult to train (with more experience I’ve in fact reached the opposite conclusion). We’ll train RNNs to generate text character by character and ponder the question “how is that even possible?” By the way, together with this post I am also releasing code on Github that allows you to train character-level language models based on multi-layer LSTMs. Recurrent Neural Networks
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: dlc_practical_prologue.py 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.
Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs – WildML Recurrent Neural Networks (RNNs) are popular models that have shown great promise in many NLP tasks. But despite their recent popularity I’ve only found a limited number of resources that throughly explain how RNNs work, and how to implement them. That’s what this tutorial is about. As part of the tutorial we will implement a recurrent neural network based language model. I’m assuming that you are somewhat familiar with basic Neural Networks. What are RNNs? The idea behind RNNs is to make use of sequential information. A recurrent neural network and the unfolding in time of the computation involved in its forward computation. The above diagram shows a RNN being unrolled (or unfolded) into a full network. There are a few things to note here: You can think of the hidden state as the memory of the network. What can RNNs do? RNNs have shown great success in many NLP tasks. Language Modeling and Generating Text since we want the output at step to be the actual next word. Machine Translation .
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
Understanding LSTM Networks -- colah's blog Posted on August 27, 2015 Recurrent Neural Networks Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Traditional neural networks can’t do this, and it seems like a major shortcoming. Recurrent neural networks address this issue. Recurrent Neural Networks have loops. In the above diagram, a chunk of neural network, , looks at some input and outputs a value . These loops make recurrent neural networks seem kind of mysterious. An unrolled recurrent neural network. This chain-like nature reveals that recurrent neural networks are intimately related to sequences and lists. And they certainly are used! Essential to these successes is the use of “LSTMs,” a very special kind of recurrent neural network which works, for many tasks, much much better than the standard version. The Problem of Long-Term Dependencies Conclusion
Syllabus | CS 231N The Spring 2020 iteration of the course will be taught virtually for the entire duration of the quarter. (more information available here ) Unless otherwise specified the lectures are Tuesday and Thursday 12pm to 1:20pm. This is the syllabus for the Spring 2020 iteration of the course. Understanding Machine Learning Infographic Other Infographics Understanding Machine Learning Infographic Understanding Machine Learning Infographic We now live in an age where machines can teach themselves without human intervention. What It Is Machine learning (ML) deals with systems and algorithms that can learn from various data and make predictions. Theory The main goal of a learner is to generalize, and a learning machine able to do that can perform accurately on new or unforeseen tasks. History In the early days of AI, researchers were very interested in machines that could learn from data. How It Is Done Supervised ML – relies on data where the true label is indicated. Approaches There are over a dozen approaches employed in ML, Some of these include: Applications The importance of ML is that, since it’s data-driven, it can be trained to create valuable predictive models that can guide proper decisions and smart actions. Embed This Education Infographic on your Site or Blog!
COMS W4721 Machine Learning for Data Science @ 422 Mudd BuildingSynopsis: This course provides an introduction to supervised and unsupervised techniques for machine learning. We will cover both probabilistic and non-probabilistic approaches to machine learning. Focus will be on classification and regression models, clustering methods, matrix factorization and sequential models. Prerequisites: Basic linear algebra and calculus, introductory-level courses in probability and statistics. Text: There is no required text for the course. T. Google researchers teach AIs to see the important parts of images — and tell you about them | TechCrunch This week is the Computer Vision and Pattern Recognition conference in Las Vegas, and Google researchers have several accomplishments to present. They’ve taught computer vision systems to detect the most important person in a scene, pick out and track individual body parts and describe what they see in language that leaves nothing to the imagination. First, let’s consider the ability to find “events and key actors” in video — a collaboration between Google and Stanford. Footage of scenes like basketball games contain dozens or even hundreds of people, but only a few are worth paying attention to. The CV system described in this paper uses a recurrent neural network to create an “attention mask” for every frame, then track relevance of each object as time proceeds. Over time the system is able to pick out not only the most important actor, but potential important actors, and the events with which they are associated. Featured Image: Omelchenko/Shutterstock
A Course in Machine Learning AI just defeated a human fighter pilot in an air combat simulator Retired United States Air Force Colonel Gene Lee recently went up against ALPHA, an artificial intelligence developed by a University of Cincinnati doctoral graduate. The contest? A high-fidelity air combat simulator. And the Colonel lost. In fact, all the other AI’s that the Air Force Research Lab had in their possession also lost to ALPHA…and so did all of the other human experts who tried their skills against ALPHA’s superior algorithms. And did we mention ALPHA achieves superiority while running on a $US35 Raspberry Pi? Saying that Lee is experienced when it comes to aerial combat is a remarkable understatement. Yet, he was not successful in winning against ALPHA. "I was surprised at how aware and reactive it was. ALPHA makes decisions using a genetic fuzzy tree system, which is a subtype of fuzzy logic algorithms. The future of air combat UC grad and Psibernetix President and CEO Nick Ernest, David Carroll, and Gene Lee (seated).
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.