programming 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
DS-GA 1003: Machine Learning and Computational Statistics, Spring 2015 This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve both the traditional and the novel data science problems found in practice. This course will also serve as a foundation on which more specialized courses and further independent study can build. This is a required course for the Center for Data Science's Masters degree in Data Science, and the course is designed for the students in this program. Other interested students who satisfy the prerequisites are welcome to take the class as well. Course details can be found in the syllabus. This term we will be using Piazza for class discussion. See the Course Calendar for all schedule information. For registration information, please contact Varsha Tiger.
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 .
NYU > Courant Institute > CIMS Bulletin Thursday, December 10, 2015 Analysis Seminar Title: Optimal Hardy-type inequality for nonnegative second-order elliptic operator: an answer to a problem of Shmuel Agmon Yehuda Pinchover, Technion Applied Mathematics Lab Seminar Title: Science-Driven Robots to Study the Fluid Mechanics of Animal Propulsion Michael Triantafyllou, MIT Friday, December 11, 2015 Numerical Analysis And Scientific Computing Seminar Title: Transport of probability measures in high dimensions with applications to Bayesian inference Alessio Spantini, MIT Probability Seminar Title: Asymptotics in periodic TASEP with step initial condition Zhipeng Liu, CIMS Title: A universality result for the random matrix hard edge Brian Rider, Temple University Computer Science Colloquium Title: GIVEN A NETWORK, PREDICT ITS FUTURE Roger Guimera, ICREA and Rovira i Virgili University Graduate Student And Postdoc Seminar Title: Odometers, cutting and stacking, graphs, and flat surfaces: a magic trick Rodrigo Treviño, Applied Mathematics Seminar
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, \(A\), looks at some input \(x_t\) and outputs a value \(h_t\). 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. LSTM Networks Conclusion
Interview Brute force Algorithm. Have 2 for loops for i = 1 to i less than array.length -1 for j=i+1 to j less than array.length This way you can get substring of every possible combination from the array Have a palindrome function which checks if a string is palindrome so for every substring (i,j) call this function, if it is a palindrome store it in a string variable If you find next palindrome substring and if it is greater than the current one, replace it with current one. Finally your string variable will have the answer Brute force approach for this problem takes O(n3) time. Another approach is Reverse the string and store it in different string Now find the largest matching substring between both the strings This too will take O(n2) time We can solve this problem using suffix trees, but constructing the suffix tree itself seems to be more complex in terms of both time and space complexity. For instance, position 2 in the string "racecar" would start as: Runtime: JavaCode:
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!
HTML5 & CSS3 Fundamentals: Development for Absolute Beginners 10 minutes, 20 seconds 35 minutes, 14 seconds 29 minutes, 21 seconds 25 minutes, 25 seconds 48 minutes, 58 seconds 20 minutes, 18 seconds 6 minutes, 59 seconds 15 minutes, 30 seconds 13 minutes, 0 seconds 27 minutes, 27 seconds