From the heartbreaking loneliness epidemic during the pandemic, to the rapid growth of everyday social media influencers

What is Deep Learning? — Machine Learning Mastery Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the…machinelearningmastery.com

Artificial Intelligence has had a crazy couple of year and 2020 has been like no other. With a pandemic, a global recession along with incredible gains in AI, there’s simply so much to keep an eye on.

I studied my MPhil in Machine Learning in 2015 and back then, AI was in relative infancy but it was actually the perfect time to see the foundation of the industry that was to form.

A Short Introduction to Bayesian Neural Networks * David Stutz With the rising success of deep neural networks, their reliability in terms of robustness (for example, against various…davidstutz.de

A Bayesian Neural Network differs from ordinary Neural Networks as the weights are actually assigned probability distributions, rather than single-updating point estimates. Bayesian networks have been around for a while but with increased computational power, we’re much better placed to use the distributions of each weight to estimate uncertainty in the predictions of a neural network.

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le to fix the “Error converting data type varchar to numeric” by converting my float column to string with exactly the same decimal point number as defined in the SQL Server table. It was quite surprising to me that pyodbc doesn’t handle that under the hood (or maybe is it fixed in more recent Pyodbc versions?).

A Deep Gaussian Process is kind of like a stacked Gaussian Process, which by construction, is able to overcome limitations of standard Gaussian Processes. Essentially, the Gaussian assumptions of a Gaussian Process limit the amount that the model can express (as every marginal distribution is jointly Gaussian), therefore, random intonations like jumps (and such) can’t be modelled. Given this, deep Gaussian Processes are rich models than standard Gaussian Processes.

Pre 2015, the theory was pretty sophisticated but the active day-to-day implementation of AI or Machine Learning was quite old. Highly sophisticated algorithms looked a bit more like Kalman Filters, Support Vector Machines, and maybe even a Random Forest.

We asked designers and friends of the UX Collective: what was your biggest lesson from 2020, that we can use as we go into 2021? Share your lessons with us: [email protected]

Deep Learning essentially emulates the concepts reflected in your brain. Given some input data (and the black box of your brain), a neural network will break down the input and be able to make sense of it. With enough training data, it’ll be able to infer something from it.

Deep Learning has been around for what feels like a while but in reality, it’s only the past 5 years that it exploded with interest because of the gains that Deep Mind made. Pre-2015, yes it was interesting, but there wasn’t that element of explosive interest.

Making Your Neural Network Say “I Don’t Know” — Bayesian NNs using Pyro and PyTorch A tutorial code on writing a bayesian image classifier on MNIST dataset.towardsdatascience.com

We asked designers and friends of the UX Collective: what was your biggest lesson from 2020, that we can use as we go into 2021? Share your lessons with us: [email protected]

Agreeably, not much has changed, but the number of challenges that AI has smashed is startling. In what follows, I’ll briefly go over a few topics that have really set the tone for this year, but are also important topics for the coming years.

Deep Learning is interesting because it doesn’t really have a pure statistical foundation, but rather, a layered functional form. A Deep Neural Network is built in a lattice like structure, where through an optimisation process, the weights (located in each adjoining lattice connection) will be optimised for the input to match the output. By adding more layers, you increase the expressivity here, but also more complicated connections (like LSTM or Gates) and also complicated net forms (e.g. Encoders) can produce amazing results in their specific domains.