# College Level Neural Nets [I] – Basic Nets: Math & Practice!

Learn Concepts, Intuitions & Complex Mathematical Derivations For Neural Networks and deep learning !

Deep Learning is surely one of the hottest topics nowadays, with a tremendous amount of practical applications in many many fields.Those applications include, without being limited to, image classification, object detection, action recognition in videos, motion synthesis, machine translation, self-driving cars, speech recognition, speech and video generation, natural language processing and understanding, robotics, and many many more.

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.

Course Content

• Introduction To Machine Learning –> 2 lectures • 11min.
• The Linear Perceptron –> 11 lectures • 1hr 40min.
• Non-Linearly Separable Data And The Multi Layer Perceptron (MLP) –> 8 lectures • 1hr 38min.
• Perceptron Learning ! –> 6 lectures • 54min.
• The Gradient Descent Algorithm –> 7 lectures • 1hr 7min.
• The Back-Propagation Algorithm ! –> 8 lectures • 1hr 22min.
• Regularization ! –> 9 lectures • 1hr 27min.
• Model Performance Metrics ! –> 5 lectures • 44min.
• Improving Neural Network Performance – Part (I) –> 10 lectures • 1hr 42min.
• Maximum Likelihood Estimation Review –> 3 lectures • 22min.

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.

Deep Learning is surely one of the hottest topics nowadays, with a tremendous amount of practical applications in many many fields.Those applications include, without being limited to, image classification, object detection, action recognition in videos, motion synthesis, machine translation, self-driving cars, speech recognition, speech and video generation, natural language processing and understanding, robotics, and many many more.

Now you might be wondering :

There is a very large number of courses well-explaining deep learning, why should I prefer this specific course over them ?

The answer is : You shouldn’t ! Most of the other courses heavily focus on “Programming” deep learning applications as fast as possible, without giving detailed explanations on the underlying mathematical foundations that the field of deep learning was built upon. And this is exactly the gap that my course is designed to cover. It is designed to be used hand in hand with other programming courses, not to replace them.

Since this series is heavily mathematical, I will refer many many times during my explanations to sections from my own college level linear algebra course. In general, being quite familiar with linear algebra is a real prerequisite for this course.

Please have a look at the course syllables, and remember : This is only part (I) of the deep learning series!

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