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Deep Learning

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A Beginner's Guide to Recurrent Networks and LSTMs Contents The purpose of this post is to give students of neural networks an intuition about the functioning of recurrent networks and purpose and structure of a prominent variation, LSTMs. Recurrent nets are a type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, the spoken word, or numerical times series data emanating from sensors, stock markets and government agencies. They are arguably the most powerful type of neural network, applicable even to images, which can be decomposed into a series of patches and treated as a sequence. Since recurrent networks possess a certain type of memory, and memory is also part of the human condition, we’ll make repeated analogies to memory in the brain.1 Review of Feedforward Networks

Image Classification using Deep Neural Networks — A beginner friendly approach using TensorFlow Note: You can find the entire source code on this GitHub repo tl;dr We will build a deep neural network that can recognize images with an accuracy of 78.4% while explaining the techniques used throughout the process. Introduction A Neural Network Playground Um, What Is a Neural Network? It’s a technique for building a computer program that learns from data. It is based very loosely on how we think the human brain works. First, a collection of software “neurons” are created and connected together, allowing them to send messages to each other.

os01 by tuhdo This book helps you gain the foundational knowledge required to write an operating system from scratch. Hence the title, 0 to 1. After completing this book, at the very least: How to write an operating system from scratch by reading hardware datasheets. In the real world, it works like that. Sentiment Analysis is the use of natural language processing, statistics, and text analysis to extract, and identify the sentiment of text into positive, negative, or neutral categories. We often see sentiment analysis used to arrive at a binary decision: somebody is either for or against something, users like or dislike something, or the product is good or bad. Sentiment analysis is also called opinion mining since it includes identifying consumer attitudes, emotions, and opinions of a company’s product, brand, or service.

Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs 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. It’s a multi-part series in which I’m planning to cover the following: As part of the tutorial we will implement a recurrent neural network based language model. The applications of language models are two-fold: First, it allows us to score arbitrary sentences based on how likely they are to occur in the real world.

Deep_Learning_Project ""Sometimes, we tend to get lost in the jargon and confuse things easily, so the best way to go about this is getting back to our basics. Don’t forget what the original premise of machine learning (and thus deep learning) is - IF the input and output are related by a function y=f(x), then if we have x, there is no way to exactly know f unless we know the process itself. However, machine learning gives you the ability to approximate f with a function g, and the process of trying out multiple candidates to identify the function g best approximating f is called machine learning. Ok, that was machine learning, and how is deep learning different? Deep learning simply tries to expand the possible kind of functions that can be approximated using the above mentioned machine learning paradigm.

Unveiling the Hidden Layers of Deep Learning In a recent Scientific American article entitled “Springtime for AI: The Rise of Deep Learning,” computer scientist Yoshua Bengio explains why complex neural networks are the key to true artificial intelligence as people have long envisioned it. It seems logical that the way to make computers as smart as humans is to program them to behave like human brains. However, given how little we know of how the brain functions, this task seems more than a little daunting. So how does deep learning work?

30 Top Videos, Tutorials & Courses on Machine Learning from 2016 Introduction 2016 has been the year of “Machine Learning and Deep Learning”. We have seen the likes of Google, Facebook, Amazon and many more come out in open and acknowledge the impact machine learning and deep learning had on their business. Last week, I published top videos on deep learning from 2016. I was blown away by the response. I could understand the response to some degree – I found these videos extremely helpful. AI memories — expert systems December 3, 2015 This is part of a four post series spanning two blogs. As I mentioned in my quick AI history overview, I was pretty involved with AI vendors in the 1980s. Here on some notes on what was going on then, specifically in what seemed to be the hottest area at the time — expert systems. Summing up: The expert systems business never grew to be very large, but it garnered undue attention (including from me).

Deep Learning with Python Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc.

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. 1. Supervised learning — scikit-learn 0.18.1 documentation Custom Search Previous User Guide User Guide Next 1.1. Generali... 1.1. Generalized Linear Models Up User Guide User Guide Data Mining - Entropy (Information Gain) [Gerardnico] Entropy is a function “Information” that satisfies: where: p1p2 is the probability of event 1 and event 2 p1 is the probability of an event 1 p1 is the probability of an event 2 Mathematics - Logarithm Function (log)

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