Notes and links on SVMs (WIP)
Saturday, April 26, 2025
Support Vector Machines (SVM) are a mathematical approach for classifying data by finding optimal separating hyperplanes, applicable even in non-linear scenarios using kernel methods.
Learning
[IA Series 2/n] Search Algorithms and Intelligent Agents
Thursday, April 24, 2025
The document discusses various search algorithms used by Intelligent Agents for navigating mazes, detailing their types, characteristics, tradeoffs, and implementations.
[IA Series 1/n] AI Search - Terms and Algorithms
Thursday, April 24, 2025
This text introduces key concepts and algorithms related to intelligent agents in AI, focusing on search terms, uninformed and informed search strategies, and adversarial search techniques.
“But who was learning, you or the machine?”
“Well, I suppose we both were”
Amazing book 🔥🤓
#TheAlignmentProblem #Learning #ResponsibleAI
The Alignment Problem by Brian Christian 📚
[NN Series 5/n] Regularisation: reducing the complexity of a model without compromising accuracy
Monday, March 17, 2025
Regularisation is known to reduce overfitting when training a neural network. As with a lot of these techniques there is a rich background and many options available, so asking the question why and how opens up to a lot of information. Diving through the information, for me at least, it wasn’t clear why/how it did this until I reframed what it was doing. In short, regularisation changes the sensitivity of the model to the training data.
[NN Series 4/n] Feature Normalisation
Thursday, March 6, 2025
This is an interesting one as I’d thought it was quite academic, with limited utility. Then I saw these graphs Error per epoch This graph shows the error per epoch of training a model on the data as is We can see that it takes around 180-200 epochs to train with a learning rate (eta) of 0.0002 or lower. Now compare it to this one Here we see the training takes around 15 epochs with a learning rate of 0.
[NN Series 3/n] Calculating the error before quantisation: Gradient Descent
Tuesday, February 25, 2025
Next I’m looking at the Adaline in python code. This post is a mixture of what I’ve learnt in my degree, Sebestien Raschka’s book/code, and the 1960 paper that delivered the Adaline Neuron. Difference between the Perceptron and the Adaline In the first post we looked at the Perceptron as a flow of inputs (x), multiplied by weights (w), then summed in the Aggregation Function and finally quantised in the Threshold Function.
[NN Series 2/n] Circuits that can be trained to match patterns: The Adaline
Monday, February 24, 2025
The text discusses the development and significance of the Adaline artificial neuron, highlighting its introduction of non-linear activation functions and cost minimization, which have important implications for modern machine learning.
#BeingHuman - look after your << self >>: love is all it needs.
Sunday, February 23, 2025
The author shares personal reflections on self-kindness and positive thinking as tools for finding peace amid societal challenges.
[NN Series 1/n] From Neurons to Neural Networks: The Perceptron
Wednesday, February 12, 2025
This post looks at the Percepton, from Frank Rosenblatt’s original paper to a practical implementation classifying Iris flowers. The Perceptron is the original Artificial Neuron and provided a way to train a model to classify linearly separable data sets. The Perceptron itself had a short life, with the Adaline coming in 3 years later. However it’s name lives on as neural networks have, Multilayer Perceptrons (MLPs). The naming shows the importance of this discovery.
[RL Series 2/n] From Animals to Agents: Linking Psychology, Behaviour, Mathematics, and Decision Making
Friday, February 7, 2025
intro Maths, computation, the mind, and related fields are a fascination for me. I had thought I was quite well informed and to a large degree I did know most of the science in more traditional Computer Science (it was my undergraduate degree…). What had slipped me by was reinforcement learning, both its mathematical grounding and value of application. If you’ve come from the previous post ([RL Series 1/n] Defining Artificial Intelligence and Reinforcement Learning) you know I’ve said something like that already.
[RL Series 1/n] Defining Artificial Intelligence and Reinforcement Learning
Friday, January 31, 2025
intro I’m learning about Reinforcement Learning, it’s an area that has a lot of intrigue for me. The first I recall hearing of it was when ChatGPT wes released and it was said Reinforcement Learning from Human Feedback was the key to making it so fluent in responses. Since then I’m studying AI and Data Science for a Masters so with that I’m stepping back to understand the domain in greater detail.
What is Off-Policy learning?
Friday, January 31, 2025
I’ve recently dug into Temporal Difference algorithms for Reinforcement Learning. The field of study has been a ride, from Animals in the late 1890s to Control Theory, Agents and back to Animals in the 1990s (and on). It’s accumulated in me developing a Q-Learning agent, and learning about hyperparameter sweeps and statistical significance, all relevant to the efficiency of Off-policy learning but topics for another day. I write this as it took a moment for me to realise what off-policy learning actually is.
Dopamine as temporal difference errors !! 🤯
Wednesday, January 15, 2025
I expect I’m sharing a dopamine burst that I experienced! 🤓 I’m listening to The Alignment Problem by Brian Christian 📚 and it’s explaining how Dayan, Montague, and Sejnowski* connected Wolfram Schultz’s work to the Temporal Difference algorithm (iirc that’s, of course!, from Sutton and Barto). A quick search returns these to add to my maybe reading list: Dopamine and Temporal Differences Learning (Montague, Dayan & Sejnowski, 1996) Dopamine and temporal difference learning: A fruitful relationship between neuroscience and AI (Deepmind 2020)