In today’s episode, Gregory Glatzer explained his machine learning project that involved the prediction of elephant movement and settlement, in a bid to limit the activities of poachers. He used two machine learning algorithms, DBSCAN and K-Means clustering at different stages of the project. Listen to learn about why these two techniques were useful and what conclusions could be drawn.
Fault Tolerant Distributed Gradient Descent
Decentralized Information Gathering
Leaderless Consensus
Automatic Summarization
Gerrymandering
Even Cooperative Chess is Hard
Consecutive Votes in Paxos
Visual Illusions Deceiving Neural Networks
Earthquake Detection with Crowd-sourced Data
Byzantine Fault Tolerant Consensus
Alpha Fold
Arrow's Impossibility Theorem
Face Mask Sentiment Analysis
Counting Briberies in Elections
Sybil Attacks on Federated Learning
Differential Privacy at the US Census
Distributed Consensus
ACID Compliance
National Popular Vote Interstate Compact
Defending the p-value
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