كورس لتعليم اساسيات خوارزميات التعلم العميق والشبكات العصبية وتعلم الاله للمبتدئين وحتى المستوى المتقدم

سواء كنت طالباً فى علوم الحاسب او طالباً فى الهندسة أو مبرمجاً وتعشق مجال الذكاء الاصطناعى, فإن هذا الكورس سيساعدك علي فهم أساسيات التعلم العميق و الوصول إلى مستوى محترف

وسوف يركز هذا الكورس على الجوانب النظرية وراء الخوارزميات والنماذج المنتشره هذه الايام للتعلم العميق

This course is focus on the theoretical aspects of the recent deep learning methods.

Section 1: Introduction to Machine learning & Deep learning

Lecture 1: Introduction to Deep learning

· Brief history of Deep learning

· Motivation

Lecture 2: What is Machine Learning?

· Machine leaning Definition

· Traditional Programming vs Machine learning

· AI vs Machine learning vs Deep learning

Lecture 3: Types of Machine Learning

· Supervised, unsupervised, and reinforcement learning

· Classification vs Regression

· Clustering and dimensionality reduction

Lecture 4: Machine Learning & Deep learning Applications

Lecture 5: Steps to Build a Machine Learning System

· Data collection, feature extraction, modelling, estimation, and validation.

· for example, how to develop an image categorization system.

Lecture 6: K-Nearest Neighbors (KNN) Model

Section 2: Linear Regression

Lecture 7: Univariate Linear Regression

Lecture 8: Cost Function Intuition

Lecture 9: Gradient Descent Algorithm

Lecture 10: Linear Regression with Multiple Variables

Section 3: Logistic Regression

Lecture 11: Introduction to Logistic Regression

Lecture 12: Cost function

Lecture 13: Multi-Class Classification

Section 4: Neural Networks

Lecture 14: Introduction to Neural Networks Part 1

· Definition of Neural Networks

· Artificial Neuron

· Types of Activation Functions

Lecture 15: Introduction to Neural Networks Part 2

· Neural Network Architectures

· Capacity of Single Neuron\Neural Network

· Multi-layer Neural Networks

· Softmax Activation Function

Lecture 16: Biological Neural Networks