Nothing to Disclose Tonight I want to show you two awesome Elevation Small Batch Prototypes. These are really stunning and the good new...

Abstract page for arXiv paper 2306.13575: Scaling MLPs: A Tale of Inductive Bias

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Deep Learning for Time Series Forecasting Predict the Future with MLPs, CNNs and LSTMs in Python …why deep learning? The Promise of Deep Learning for Time Series Forecasting Traditionally, time series forecasting has been dominated by linear methods because they are well understood and effective on many simpler forecasting problems. Deep learning neural networks are […]

As with the other videos from our codecentric.ai Bootcamp (Random Forests, Neural Nets & Gradient Boosting), I am again sharing an English version of the script (plus R code) for this most recent addition on How Convolutional Neural Nets work. In this lesson, I am going to explain how computers learn to see; meaning, how do they learn to recognize images or object on images? One of the most commonly used approaches to teach computers “vision” are Convolutional Neural Nets. This lesson builds on top of two other lessons: Computer Vision Basics and Neural Nets. In the first video, Oli explains what computer vision is, how images are read by computers and how they can be analyzed with traditional approaches, like Histograms of Oriented Gradients and more. He also shows a very cool project, that he and colleagues worked on, where they programmed a small drone to recognize and avoid obstacles, like people. This video is only available in German, though. In the Neural Nets blog post, I show how Neural Nets work by explaining what Multi-Layer Perceptrons (MLPs) are and how they learn, using techniques like gradient descent, backpropagation, loss and activation functions. Convolutional Neural Nets Convolutional Neural Nets are usually abbreviated either CNNs or ConvNets. They are a specific type of neural network that has very particular differences compared to MLPs. Basically, you can think of CNNs as working similarly to the receptive fields of photoreceptors in the human eye. Receptive fields in our eyes are small connected areas on the retina where groups of many photo-receptors stimulate much fewer ganglion cells. Thus, each ganglion cell can be stimulated by a large number of receptors, so that a complex input is condensed into a compressed output before it is further processed in the brain. How does a computer see images Before we dive deeper into CNNs, I briefly want to recap how images can take on a numerical format. We need a numerical representation of our image because just like any other machine learning model or neural net, CNNs need data in form of numbers in order to learn! With images, these numbers are pixel values; when we have a grey-scale image, these values represent a range of “greyness” from 0 (black) to 255 (white). Here is an example image from the fruits datasets, which is used in the practical example for this lesson. In general, data can be represented in different formats, e.g. as vectors, tables or matrices. I am using the imager package to read the image and have a look at the pixel values, which are represented as a matrix with the dimensions image width x image height. library(imager) im