Description
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 NeuronNeural Network
· Multi-layer Neural Networks
· Softmax Activation Function
Lecture 16: Biological Neural Networks
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