⚜️Neural network course session two::
2️⃣Neuron Model and Network

🔵Explore neuron models and neural network architectures in this comprehensive session. Understand the mathematical foundations of these computational models. Study single and multiple-input neuron models, transfer functions, and how neurons form network building blocks. Discover single-layer, multi-layer, and recurrent network architectures designed for various problem complexities. Learn about feedback loops enabling temporal behavior in recurrent networks.

✅Neuron Model
✅Transfer Functions
✅Network Architectures
✅Recurrent Networks
🔻YouTube: third session
https://youtu.be/DvaMtUP095Q
Download file and codes (in comment)::
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🔰Linear Control Training Workshop - Session 2

🔹Partial Fraction Expansion: Learn how to use the residue() command to easily perform partial fraction expansion on transfer functions. See examples of expanding proper and improper rational functions.
🔸Transforming Mathematical Models: Discover how to convert between different representations of dynamic systems using commands like tf2ss, ss2tf, zp2tf, etc. Examples show conversions between transfer functions, state-space models, pole-zero form, and discrete-time systems.
🔹Block Diagram Modeling: Master the techniques for representing interconnected systems with transfer function or state-space blocks. Learn the MATLAB syntax for series, parallel, and feedback connections. See how to extract the overall transfer function or state-space model.
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