College Level Neural Nets - Basic Nets: Math & Practice! For Free
What you’ll learn
- Step By Step Conceptual Introduction For Neural Networks And Deep Learning [Even If You Are A Beginner]
- Understanding The Basic Perceptron[Neuron] Conceptually, Graphically, And Mathematically - Perceptron Convergence Theorem Proof
- Mathematical Derivations For Deep Learning Modules
- Step-By-Step Derivation Of BackPropagation Algorithm
- Vectorization Of BackPropagation
- Different Performance Metrics Like Performance - Recall - F1 Score - ROC & AUC
- Mathematical Derivation Of Cross-Entropy Cost Function
- Mathematical Derivation Of Back-Propagation Through Batch-Normalization
- Different Solved Examples On Various Topics
Requirements
- You Should Be Familiar With College Level Linear Algebra [Advanced]
- You Should Be Familiar With Multi-Variable Calculus And Chain-Rule
- You Should Be Famililar With Basic Probability
Who this course is for:
- Deep Learning Engineers Or College Students Who Want To Gain Deep Mathematical Understanding Of The Topic