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Baeldung Author
Thomas Carr
Thomas Carr is a data-scientist with 7years experience in machine learning and artificial intelligence. He holds a BSc from University College Cork, an MSc from the University of Edinburgh and a PhD from Aston University. His PhD work focused on deep reinforcement learning and domain adaptation in that context. This work further explores generalisation and extrapolation arising from domain adaptation. He has also worked across a range of other modelling problems and has worked with tree based methods, deep visual recommender systems and large language models.
Here's what I've written (so far):
Baeldung on Computer Science
- All
- Deep Learning (5)
- Machine Learning (3)
- Math and Logic (1)
- Artificial Intelligence (1)
- Algorithms (1)
Multi-Headed Networks
Filed under Machine Learning
Learn about multi-headed models that have the same backbone and for the same input stream branch into multiple “heads” trained on diverse tasks.
Value Iteration vs. Q-Learning
Filed under Machine Learning
Value-Iteration learns the value function per state and Q-learning learns the action-value function. Explore these two methods.
Attention vs. Self-Attention
Filed under Deep Learning
Explore the distinctions between the Badhanau attention and self-attention mechanisms.
GELU Explained
Filed under Machine Learning
Learn what distinguishies GELU from other activation functions we use in neural networks.
Saturating Non-Linearities
Filed under Deep Learning
Explore non-linearities – a key component of neural networks.
Introduction to AI Ethics
Filed under Artificial Intelligence
Explore many practical and theoretical considerations when dealing with AI ethics.
Deterministic vs. Stochastic Policies in Reinforcement Learning
Filed under Deep Learning
Explore a deterministic policy that maps each state to a single action and a stochastic policy that maps each state to a probability distribution over actions.
Epoch or Episode: Understanding Terms in Deep Reinforcement Learning
Filed under Deep Learning
Learn the difference between episodes and epochs.