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Lectures
Lecture 1: Floating-Point Numbers
Lecture 2: Optimization & Gradient Descent
Lecture 3: Stochastic Gradient Descent and Introduction to Neural Networks
Lecture 4: Introduction to Pytorch & Neural Networks
Lecture 5: Training Neural Networks
Lecture 6: Introduction to Convolutional Neural Networks
Lecture 7: CNNs in Practice
Lecture 8: Advanced Convolutional Models
Appendices
Appendix A: Gradients Review
Appendix B: Logistic Loss
Appendix C: Computing Derivatives
Appendix D: Creating a CNN to Predict Bitmojis
Attribution
Attributions
LICENSE
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