Machine Learning Exposed - Deep and Reinforcement Learning

Explore deep and reinforcement learning concepts in this comprehensive conference talk. Gain a gentle introduction to machine learning topics before delving into the intricacies of deep learning and reinforcement learning. Develop an intuition for the underlying mathematics without requiring advanced calculus knowledge. Begin with fundamental concepts and progressively build up to more complex techniques, ensuring an approachable learning experience. Cover topics such as sigmoid functions, vectorization, bias units, linear relationships, output propagation, cost functions, optimization, convergence, backpropagation, and various data sets. Examine real-world applications through examples like speed dating. Suitable for both beginners and those with some prior machine learning knowledge.

Intro
Example
Sigmoid
Vectorization
Bias Units
Linear Relationships
Bias Term
Output
Propagation
Cost Function
Optimization
Convergence
Backpropagation
Graphs
Back propagation
Different data sets
Input features
Speeddating example
Seed number