Introduction to Deep Learning

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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