Introduction to Deep Learning
Description:
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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