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!**