Abstrast

This course conducted by Standford focus on Computer Vision and Deep Learning, and is focus from the basic to the somehow advanced topic. This notebook contain the archives of the note of me taking from the course. If you would like to contribute to the notebook, please leave a comment in the below of the notebook.
Link to youtube lecture

Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition

Brief introduction to the history of Computer Vision, Deep Learning and the problems to tackle in Computer Vision.
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Lecture 2 | Image Classification Pipelines

Introduction to the image classification pipelines and train a simple model for image classification.
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Lecture 3 | Loss Function and Optimization

Introduction to hinge loss, softmax function and optimization.
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Lecture 4 | Backpropagation and Neural Network

Introduction to Neural Network backpropagation flow, equations and intuiation.
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Lecture 5 | Convolutional Neural Network

History and modern about CNN and its basic operations.
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Lecture 6 | Training Neural Network 1

Basic Training Procedure hyperparameters optimization.
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Lecture 7 | Training Neural Network 2

Basic Training Procedure hyperparameters optimization.
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