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By:
Josh Davidson
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Decks in this class (16)
Week 2 - Search with A* & MCTS
Intelligent agents,
Static agents vs learning,
Breadth first
25
cards
3 - Neural Net for modern AI
Action potential,
Perceptron,
Perceptron learning rule
15
cards
3 - Backpropagation in computation graphs
Computational graph,
Partial derivative,
Chain rule
13
cards
4/5 - Deep Convolutional Neural Networks
Deep learning,
Overfitting in deep learning,
Shape analogy for layers abstraction
46
cards
5/6 - Unsupervised Learning in the era of generative AI
Unsupervised learning,
Unsupervised v supervised,
Clustering
18
cards
6 - How smart is deep learning
What might happen when you add ra...,
Clever hans
2
cards
6 - Probability and Bayes Theorem
Consider r and b as boxes r has 6...,
Consider r and b as boxes r has 6...,
Consider r and b as boxes r has 6...
17
cards
7 - Reinforcement Learning
Rl is built on,
Policy,
Reward signal
44
cards
8/9 - Approximate Methods and Deep RL
Problem with tabular approaches q...,
Approximate methods seek to appro...,
How can approximation be improved
11
cards
8 - GPU Acceleration and AI HW
Why is ai computationally expensive,
Flops,
Tops
18
cards
9 - Quantifying intelligence in modern AI
Gofai,
Weaknesses of gofai,
Digital twinning
5
cards
10 - Evolutionary Computation Review
Evolutionary algorithms,
In this module assume that the di...,
Main idea of evo algos
34
cards
10 - Neuroevolution
Evolving weights,
Advantages of neuroevolution vs b...,
Evolving architectures
20
cards
11 - LLMs
What is attention,
Attention is all you need,
How do neural nets process words ...
21
cards
11- bayesian networks and causality
Bayesian network defintion,
How are bayesian networks represe...,
Bayesian network example in medicine
17
cards
12 - Continual/Lifelong Machine Learning
Traditional machine learning dev ...,
Problem with traditional ai syste...,
Catastrophic forgetting
9
cards
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advanced ai
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