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Amelia Matteson's Blog (53)

Curve Fitting using Linear and Nonlinear Regression

This article was written by Jim Frost.

In regression analysis, curve fitting is the process of specifying the model that provides the best fit to the specific curves in your dataset. Curved relationships between variables are not as straightforward to fit and interpret as linear relationships.…

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Added by Amelia Matteson on February 6, 2018 at 6:00pm — 1 Comment

Python Data Science Handbook- An essential handbook for Python enthusiasts

This article was written by Prakarsh Saxena

Python Data Science Handbook

In a world where each of us is surrounded by data and its insights, Data Science does seem a promising field of work and research to many. Although there are many books and courses which help us dive into the area right from the scratch, it is also essential for the adepts to have a separate…

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Added by Amelia Matteson on January 20, 2018 at 1:30pm — No Comments

The DeepMind Strategy - How AI is Revolutionizing Business Models

This article was written by Francesco Corea.

Image Credit: …

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Added by Amelia Matteson on January 11, 2018 at 2:30pm — No Comments

Limits of Linear Models for Forecasting

This article was written by Blaine Bateman

In this post, I will demonstrate the use of nonlinear models for time series analysis, and contrast to linear models. I will use a (simulated) noisy and nonlinear time series of sales data, use multiple linear regression and a small neural network to fit training data, then predict 90 days forward.  I implemented all of this in R, although it could be done in a number of…

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Added by Amelia Matteson on January 5, 2018 at 12:30pm — No Comments

Extreme Event Forecasting at Uber - with Recurrent Neural Networks

This article is by Nikolay Laptev, Slawek Smyl, and Santhosh Shanmugam.

At Uber, event forecasting enables us to future-proof our services based on anticipated user demand. The goal is to accurately predict where, when, and how many ride requests Uber will receive at any given time.

Extreme events—peak travel times such as holidays, concerts, inclement weather,…

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Added by Amelia Matteson on December 31, 2017 at 12:00pm — No Comments

PCA with Rubner-Tavan Networks

This article is written by Giuseppe Bonaccorso.

One of the most interesting effects of PCA (Principal Component Analysis) is to decorrelate the input covariance matrix C, by computing the eigenvectors and operating a base change using a matrix V:…

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Added by Amelia Matteson on December 23, 2017 at 12:00pm — No Comments

Microsoft CNTK Tutorial: Build a Neural Network with Python

This article was written by Andy at Adventures in Deep Learning.

Fully connected neural network example…

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Added by Amelia Matteson on December 23, 2017 at 12:00pm — No Comments

Standard Error of the Regression vs. R-squared

This article was written by Jim Frost.

The standard error of the regression (S) and R-squared are two key goodness-of-fit measures for regression analysis. While R-squared is the most well-known amongst the goodness-of-fit statistics, I think it is a bit over-hyped.…

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Added by Amelia Matteson on December 20, 2017 at 1:30pm — 1 Comment

Time Series Analysis with Generalized Additive Models

This article is by Algobeans.com 

Whenever you spot a trend plotted against time, you would be looking at a time series. The de facto choice for studying financial market performance and weather forecasts, time series are one of the most pervasive analysis techniques because of its inextricable relation to…

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Added by Amelia Matteson on December 8, 2017 at 1:00pm — No Comments

Benefits and use cases for blockchain in banking

This article comes from Consultancy.uk

Blockchain, while still relatively new to the financial space, is seeing interest from around 90% of banking sector executives, according to a new study on the potential of the technology in the industry. 40% of banks find themselves still at the exploration phase, while around 30% are pursuing proof of concepts. Intra-bank cross-border transactions are regarded as the most…

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Added by Amelia Matteson on December 7, 2017 at 12:30pm — No Comments

One-shot Learning with Memory-Augmented Neural Networks

This article comes from Rylan Schaeffer Github.

I've found that the overwhelming majority of online information on artificial intelligence research falls into one of two categories: the first is aimed at explaining advances to lay audiences, and the second is aimed at explaining advances to other researchers. I haven't found a good resource…

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Added by Amelia Matteson on December 6, 2017 at 11:00am — No Comments

RNNoise: Learning Noise Suppression with Deep Learning

This article was written by Jean-Marc Valin.

This demo presents the RNNoise project, showing how deep learning can be applied to noise suppression. The main idea is to combine classic signal processing with deep learning to create a real-time noise suppression algorithm that's small and fast. No expensive GPUs required — it runs easily on a Raspberry Pi. The result is much simpler (easier to tune) and sounds…

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Added by Amelia Matteson on November 26, 2017 at 12:30pm — No Comments

Implementing a Neural Network from Scratch in Python – An Introduction

This article was written by Denny Britz.

In this post we will implement a simple 3-layer neural network from scratch. We won’t derive all the math that’s required, but I will try to give an intuitive explanation of what we are doing. I will also point to resources for you read up on the details.

Here I’m…

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Added by Amelia Matteson on November 24, 2017 at 10:00am — No Comments

Deep Learning And The Information Bottleneck

This article was written by Ray of RayOnStorage Blog. Original title: Compressing information through the information bottleneck during deep learning.…

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Added by Amelia Matteson on November 20, 2017 at 11:00am — No Comments

Deep Learning for Natural Language Processing: Tutorials with Jupyter Notebooks

This article was written by Jon Krohn.

At untapt, all of our models involve Natural Language Processing (NLP) in one way or another. Our algorithms consider the natural, written language of our users’ work experience and, based on real-world decisions that hiring managers have made, we can assign a…

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Added by Amelia Matteson on November 15, 2017 at 11:30am — 1 Comment

On Building a “Fake News” Classification Model

This article is written by George McIntire.

"A lie gets halfway around the world before the truth has a chance to get its pants on." -Winston Churchill

Since the 2016 presidential election, one topic dominating political discourse is the issue of “Fake News”. A number of political pundits claim…

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Added by Amelia Matteson on November 8, 2017 at 11:00am — 1 Comment

Nuts and Bolts of Building Deep Learning Applications: Ng @ NIPS2016

This article was written by Tomasz Malisiewicz.

You might go to a cutting-edge machine learning research conference like NIPS hoping to find some mathematical insight that will help you take your deep learning system's performance to the next level. Unfortunately, as Andrew Ng reiterated to a…

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Added by Amelia Matteson on November 3, 2017 at 10:00am — No Comments

A Comparative Roundup: Artificial Intelligence vs. Machine Learning vs. Deep Learning

This article was written by Paramita Ghosh

A 1969 McKinsey article claimed that computers were so dumb that they were not capable of making any decisions. In fact they said, it was human intelligence that drives the dumb machine. Alas, this claim has become a bit of a “joke” over the years, as the modern computers are gradually replacing…

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Added by Amelia Matteson on November 1, 2017 at 10:00am — No Comments

A Primer on Deep Learning

This article was written by Dallin Akagi.

What is deep learning?

I like to use the following three-part definition as a baseline. Deep learning is:

  1. a collection of statistical machine learning techniques
  2. used to learn feature hierarchies…
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Added by Amelia Matteson on October 27, 2017 at 9:30am — No Comments

I built a Chatbot in 2 hours and this is what I learned

This article was written by Shival Gupta.

We spend about 5 hours on our smartphones every day as per this study from Flurry. Not only is this statistic surprising in its own right, about 65% of this time is spent on communication related activities like social media, texting, emailing and phone calls. That’s 3 hours and 15 minutes. Every. Single.…

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Added by Amelia Matteson on October 20, 2017 at 9:30am — No Comments

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