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The Unreasonable Effectiveness of Recurrent Neural Networks

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 Related:  AINeural Networks and AI

An Introduction to Deep Learning (in Java): From Perceptrons to Deep Networks In recent years, there’s been a resurgence in the field of Artificial Intelligence. It’s spread beyond the academic world with major players like Google, Microsoft, and Facebook creating their own research teams and making some impressive acquisitions. Some this can be attributed to the abundance of raw data generated by social network users, much of which needs to be analyzed, the rise of advanced data science solutions, as well as to the cheap computational power available via GPGPUs. But beyond these phenomena, this resurgence has been powered in no small part by a new trend in AI, specifically in machine learning, known as “Deep Learning”. In this tutorial, I’ll introduce you to the key concepts and algorithms behind deep learning, beginning with the simplest unit of composition and building to the concepts of machine learning in Java. A Thirty Second Tutorial on Machine Learning In case you’re not familiar, check out this introduction to machine learning: Training the Perceptron

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 .

baidu-research/ba-dls-deepspeech CNTK, el nuevo paquete de herramientas de aprendizaje profundo de código abierto de Microsoft en GitHub - El blog de Windows para América Latina Microsoft ha comenzado a fabricar las herramientas que sus propios investigadores usan para acelerar los avances en inteligencia artificial que estén disponibles para un amplio grupo de desarrolladores, al lanzar su Paquete de Herramientas de Red Computacional en GitHub. Los investigadores desarrollaron este paquete de herramienta de código abierto, apodado CNTK, por necesidad. Xuedong Huang, jefe científico de habla en Microsoft, dijo que él y su equipo estaban ansiosos por realizar mejoras más rápidas en las formas en las que las computadoras entienden el habla, y cómo las herramientas con las que tenían que trabajar los retrasaban. Así que un grupo de voluntarios se prepararon para resolver este problema por sí solos, con ayuda de una solución casera que resaltó el rendimiento sobre todo lo demás. El esfuerzo rindió frutos. “El paquete de herramientas CNTK es mucho más eficiente que cualquier otra que hemos visto”, Huang dijo. Xuedong Huang (fotografía por Scott Eklund/Red Box Pictures

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

Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano – WildML The code for this post is on Github. This is part 4, the last part of the Recurrent Neural Network Tutorial. The previous parts are: In this post we’ll learn about LSTM (Long Short Term Memory) networks and GRUs (Gated Recurrent Units). LSTMs were first proposed in 1997 by Sepp Hochreiter and Jürgen Schmidhuber, and are among the most widely used models in Deep Learning for NLP today. LSTM networks In part 3 we looked at how the vanishing gradient problem prevents standard RNNs from learning long-term dependencies. (I’m using to mean elementwise multiplication): These equations look quite complicated, but actually it’s not that hard. . , the current input at step , and , the previous hidden state. . With that in mind let’s try to get an intuition for how a LSTM unit computes the hidden state. are called the input, forget and output gates, respectively. LSTM Gating. Intuitively, plain RNNs could be considered a special case of LSTMs. that squashes the output a bit. GRUs , and an update gate .

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!

Reinforcement Learning for Torch: Introducing torch-twrl Introducing torch-twrl Advances in machine learning have been driven by innovations and ideas from many fields. Inspired by the way that humans learn, Reinforcement Learning (RL) is concerned with algorithms which improve with trial-and-error feedback to optimize future performance. Board games and video games often have well-defined reward functions which allow for straightforward optimization with RL algorithms. Algorithmic advances have allowed for RL to be in real-world problems, such as high degree-of-freedom robotic manipulation and large-scale recommendation tasks, with more complex goals. Twitter Cortex invests in novel state-of-the-art machine learning methods to improve the quality of our products. RL algorithms (or agents) aim to learn to perform complex, novel tasks through interaction with the task (or environment). Inspired by other RL frameworks, torch-twrl aims to provide: git clone -- recursive cd torch-twrl luarocks make

Los intereses comerciales marcan el futuro de la inteligencia artificial | Ciencia El futuro de la inteligencia artificial genera muchos debates porque será decisiva en campos tan serios como la medicina, las guerras, el trabajo o incluso las relaciones humanas. Sin embargo, esos debates a menudo ignoran un asunto que sobrevuela a todos los demás: el desarrollo de las máquinas pensantes ha sido conquistado por empresas tecnológicas que están definiendo cómo será ese futuro. Compañías como Google, Facebook, Amazon, Microsoft, Apple e IBM fichan a los mejores expertos en inteligencia artificial de todo el mundo, esquilman departamentos universitarios enteros para cubrir sus necesidades, compran las empresas incipientes del sector y marcan el rumbo de la investigación con becas y ayudas. Así, un campo científico tan determinante como la inteligencia artificial puede estar volcado excesivamente en los intereses comerciales de estos negocios. ampliar foto No es solo compartir centro de trabajo con los mejores. Control sobre la academia

A Course in Machine Learning

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