Category project

AI learns to sail upwind

Solving sailing using Q-Learning

The year 2020 was not the greatest for sailors. The pandemic limited the available options for voyages or even to gather a crew. I am a huge sailing enthusiast and...

Building an Automated Detection of Anomalous RFID Behaviour for a Smart Intercom

On Medium

Having to monitor multiple custom-built intercoms can become a tedious process. A daily routine of checking their state and fearfully awaiting for a Slack notification telling you about one crashing...

Category tutorial

Defusing Diffusion Models

How not to get a headache working with diffusion models.

Diffusion models are all the rage these days. They are a new class of generative models, which are capable of generating high-quality images. They have attracted a lot of attention...

Hodgkin-Huxley Model

Quantifying the activity of a neuron

Back in 1952 Hodgkin and Huxley found that there are three main types of currents describing the dynamics of a neuron: Sodium (N), Potassium (K), and a leak current, which...

Langevin Monte Carlo: Sampling using Langevin Dynamics

How to sample from a distribution using Langevin Dynamics and why it works.

Generative models rely on the ability to sample from a distribution. When the distribution is known the task of sampling from it is straightforward, for example, sampling from a normal...

What is linear regression?

On fitting a line to your data

Linear Regression (LR) is one of the most fundamental algorithms in machine learning. It is the simplest regression model out there it is very often a fitting solution for a...

What is a Loss Function?

How to formulate the right objective.

Any machine learning student will learn about loss functions sooner rather than later. They are a fundamental element of learning and optimisation, therefore understanding is necessary for mastering machine learning....

What is gradient descent?

Finding minimums of unobvious functions

Gradient descent is probably one of the most widely used algorithm in Machine Learning and Deep Learning. At the same time, it is one of the easiest to understand. In...

Getting Started: React Admin

On the Teacode blog

At Teacode.io we come across the need to provide administrative technology on a daily basis. However, knowing that admin panels are not the actual money-maker for our clients we had...

Category machine learning

What is linear regression?

On fitting a line to your data

Linear Regression (LR) is one of the most fundamental algorithms in machine learning. It is the simplest regression model out there it is very often a fitting solution for a...

AI learns to sail upwind

Solving sailing using Q-Learning

The year 2020 was not the greatest for sailors. The pandemic limited the available options for voyages or even to gather a crew. I am a huge sailing enthusiast and...

What is a Loss Function?

How to formulate the right objective.

Any machine learning student will learn about loss functions sooner rather than later. They are a fundamental element of learning and optimisation, therefore understanding is necessary for mastering machine learning....

What is gradient descent?

Finding minimums of unobvious functions

Gradient descent is probably one of the most widely used algorithm in Machine Learning and Deep Learning. At the same time, it is one of the easiest to understand. In...

Category loss functions

What is a Loss Function?

How to formulate the right objective.

Any machine learning student will learn about loss functions sooner rather than later. They are a fundamental element of learning and optimisation, therefore understanding is necessary for mastering machine learning....

Category reinforcement learning

AI learns to sail upwind

Solving sailing using Q-Learning

The year 2020 was not the greatest for sailors. The pandemic limited the available options for voyages or even to gather a crew. I am a huge sailing enthusiast and...

Category sailing

AI learns to sail upwind

Solving sailing using Q-Learning

The year 2020 was not the greatest for sailors. The pandemic limited the available options for voyages or even to gather a crew. I am a huge sailing enthusiast and...

Category probabilistic machine learning

Defusing Diffusion Models

How not to get a headache working with diffusion models.

Diffusion models are all the rage these days. They are a new class of generative models, which are capable of generating high-quality images. They have attracted a lot of attention...

Langevin Monte Carlo: Sampling using Langevin Dynamics

How to sample from a distribution using Langevin Dynamics and why it works.

Generative models rely on the ability to sample from a distribution. When the distribution is known the task of sampling from it is straightforward, for example, sampling from a normal...

Category neuroscience

Hodgkin-Huxley Model

Quantifying the activity of a neuron

Back in 1952 Hodgkin and Huxley found that there are three main types of currents describing the dynamics of a neuron: Sodium (N), Potassium (K), and a leak current, which...