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Deep Learning Prerequisites: Logistic Regression in Python
Introductgion and Outline
Introduction and Outline (7:22)
Special Offer! Get the VIP version of this course (1:14)
How to Succeed in this Course (3:13)
Review of the classification problem (1:53)
Introduction to the E-Commerce Course Project (8:53)
What can classification be used for?
Basics: What is linear classification? What's the relation to neural networks?
Linear Classification (4:50)
Biological inspiration - the neuron (3:36)
How do we calculate the output of a neuron / logistic classifier? - Theory (4:18)
How do we calculate the output of a neuron / logistic classifier? - Code (4:30)
E-Commerce Course Project: Pre-Processing the Data (5:24)
E-Commerce Course Project: Making Predictions (3:01)
Feedforward
Solving for the optimal weights
A closed-form solution to the Bayes classifier (5:59)
What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc. (3:38)
The cross-entropy error function - Theory (2:46)
The cross-entropy error function - Code (4:53)
Visualizing the linear discriminant / Bayes classifier / Gaussian clouds (2:28)
Can we use squared error instead of cross-entropy for the error if we're doing classification?
Maximizing the likelihood (6:34)
Updating the weights using gradient descent - Theory (6:20)
Updating the weights using gradient descent - Code (3:09)
E-Commerce Course Project: Training the Logistic Model (6:47)
Softmax
Practical concerns
Interpreting the Weights (4:07)
L2 Regularization - Theory (8:38)
L2 Regularization - Code (1:43)
L1 Regularization - Theory (2:53)
L1 Regularization - Code (6:13)
L1 vs L2 Regularization (3:06)
The donut problem (10:01)
The XOR Problem (6:12)
Neural Networks
Checkpoint and applications: How to make sure you know your stuff
Sentiment Analysis (5:13)
Exercises + how to get good at this (2:48)
Project: Facial Expression Recognition
Facial Expression Recognition Problem Description (12:21)
The class imbalance problem (6:01)
Utilities walkthrough (5:45)
Facial Expression Recognition in Code (10:41)
Appendix
Gradient Descent Tutorial (4:30)
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:22)
How to Code by Yourself (part 1) (15:54)
How to Code by Yourself (part 2) (9:23)
How to Succeed in this Course (Long Version) (10:24)
BONUS: Where to get discount coupons and FREE deep learning material (5:31)
What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc.
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