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Lecture 26: Logistic Regression
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Classroom Contents
Practical Machine Learning with Tensorflow
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- 1 Lecture 1: Overview of Tensorflow
- 2 Lecture 2: Machine Learning Refresher
- 3 Lecture 3: Steps in Machine Learning Process
- 4 Lecture 4: Loss Functions in Machine Learning
- 5 Lecture 5: Gradient Descent
- 6 Lecture 6: Gradient Descent Variations
- 7 Lecture 7: Model Selection and Evaluation
- 8 Lecture 8: Machine Learning Visualization
- 9 Lecture 9: Deep Learning Refresher
- 10 Lecture 10: Introduction to Tensors
- 11 Lecture 11: Mathematical Foundations of Deep Learning - Contd.
- 12 Lecture 12A: Building Data Pipelines for Tensorflow - Part 1
- 13 Lecture 12B: Building Data Pipelines for Tensorflow - Part 2
- 14 Lecture 12C: Building Data Pipelines for Tensorflow - Part 3
- 15 Lecture 13: Text Processing with Tensorflow
- 16 Lecture 14: Classify Images
- 17 Lecture 15: Regression
- 18 Lecture 16: Classify Structured Data
- 19 Lecture 17: Text Classification
- 20 Lecture 18: Underfitting and Overfitting
- 21 Lecture 19: Save and Restore Models
- 22 Lecture 20: CNNs-Part 1
- 23 Lecture 21: CNNs-Part 2
- 24 Lecture 22: Transfer learning with pretrained CNNs
- 25 Lecture 23: Transfer learning with TF hub
- 26 Lecture 24: Image classification and Visualization
- 27 Lecture 25: Estimator API
- 28 Lecture 26: Logistic Regression
- 29 Lecture 27: Boosted Trees
- 30 Lecture 28: Introduction to word embeddings
- 31 Lecture 29: Recurrent Neural Networks Part 1
- 32 Lecture 30: Recurrent Neural Networks Part 2
- 33 Lecture 31: Time Series Forecasting with RNNs
- 34 Lecture 32: Text Generation with RNNs
