Perceptron.blog
Machine Learning & Neuroscience
Category project
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...
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
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...
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...
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...
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...
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....
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...
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
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...
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...
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....
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
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
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
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
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...
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
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...
