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My notes for the AI MOOC, Applied AI Course.

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.xopp
 
 
.gitignore
.gitignore
 
 
11-Plotting for Exploratory Data Analysis.pdf
11-Plotting for Exploratory Data Analysis.pdf
 
 
12-Linear Algebra.pdf
12-Linear Algebra.pdf
 
 
13-Probability and Statistics.pdf
13-Probability and Statistics.pdf
 
 
14-Dimensionality Reduction and Visualization.pdf
14-Dimensionality Reduction and Visualization.pdf
 
 
15-Principal Component Analysis.pdf
15-Principal Component Analysis.pdf
 
 
16-T-distributed Stochastic Neighborhood Embedding.pdf
16-T-distributed Stochastic Neighborhood Embedding.pdf
 
 
17-Amazon Fine Food Reviews.pdf
17-Amazon Fine Food Reviews.pdf
 
 
18-Classification and Regression Models-KNN-1.pdf
18-Classification and Regression Models-KNN-1.pdf
 
 
18-Classification and Regression Models-KNN.pdf
18-Classification and Regression Models-KNN.pdf
 
 
57 - Deep Learning - Neural Networks.pdf
57 - Deep Learning - Neural Networks.pdf
 
 
58 - Deep Learning - Deep Multi-Layer Perceptrons.pdf
58 - Deep Learning - Deep Multi-Layer Perceptrons.pdf
 
 
59 - Deep Learning - Tensorflow and Keras.pdf
59 - Deep Learning - Tensorflow and Keras.pdf
 
 
60 - Deep Learning - Convolutional Neural Networks.pdf
60 - Deep Learning - Convolutional Neural Networks.pdf
 
 
61 - Deep Learning - Long Short-term Memory (LSTMs).pdf
61 - Deep Learning - Long Short-term Memory (LSTMs).pdf
 
 
62 - Deep Learning - Generative Adversarial Networks (GANs).pdf
62 - Deep Learning - Generative Adversarial Networks (GANs).pdf
 
 
63 - Deep Learning - Encoder - Decoder Models.pdf
63 - Deep Learning - Encoder - Decoder Models.pdf
 
 
64 - Deep Learning - Attention Models.pdf
64 - Deep Learning - Attention Models.pdf
 
 
9-SQL.pdf
9-SQL.pdf
 
 
Notes-Combined.pdf
Notes-Combined.pdf
 
 
README.md
README.md
 
 
Write-Test.svgz
Write-Test.svgz
 
 

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ai-notes

AAIC Notes

Notes-Combined.pdf is the up to date version with both digital and handwritten versions, bookmarked.

  • SQL

  • Plotting for Exploratory Data Analysis

  • Linear Algebra

  • Probability and Statistics

  • Dimensionality Reduction and Visualization

  • Principal Component Analysis

  • T-distributed Stochastic Neighborhood Embedding (t-SNE)

  • Amazon Fine Food Reviews

  • Classification and Regression Models

  • Deep Learning: Neural Networks

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My notes for the AI MOOC, Applied AI Course.

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