AI Saturdays - Boise

All materials (presentations and tutorials) from AI Saturdays - Boise Chapter, organized by AI Developers, Boise!

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Study and Learning Resources

• Week 0 (March 5, 2018) - AI6 Orientation
• Week 1 (March 12, 2018) [old link] [Preliminary Readings]
  • Python, Installation and Environment Setup
  • Introduction to Python
  • Numpy, Scipy and Matplotlib
• Week 2 (March 19, 2018) [old link] [Preliminary Readings]
  • Beginners level tutorial on Numpy
  • Beginners level tutorial on Pandas
  • Tutorial on Linear Regression (Naive Implementation and Intuition)
  • Linera Regression Intuition
  • Univariate Linear Regression
  • Linear regression using Matrix Multiplication
  • Linear Algebra - data generation simulation and demonstration
• Week 3 (June 2, 2018) [old link] [Preliminary Readings]
  • Titanic: Machine Learning from Disaster (Kaggle) and Logistic Regression
  • Probability Distributions
  • Data Science in Agriculture
• Week 4 (June 9, 2018) [Preliminary Readings]
  • Tensorflow, GCP and ML Engine
  • Convex Optimization (General Intuition)
  • Gradient Descent ( Naive implementation from scratch to train a simple linear regression)
• Week 5 (June 16, 2018)[Preliminary Readings]
  • Introduction to Support Vector Machines
  • Simple plots to get a glimpse of Loss Function in Logistic Regression
  • Demonstration (muffins vs cupcake classification using SVM)
• Week 6 (June 30, 2018)[Preliminary Study: CS221n Chapter 2, CS221n Chapter 3]
  • Chapter 4 - Introduction to Neural Networks
  • Chapter 5 - Convolutional Neural Networks (CNNs)
  • Notes on lecture 2 and simple code implementation of K-NN algorithm
• Week 7 (July 7, 2018)[Preliminary Readings]
  • Chapter 6, Chapter 7 - Training Neural Networks
• Week 8 (July 14, 2018)[Preliminary Readings]
  • Chapter 9 - Convolutional Neural Networks Architectures (Alexnet, Googlenet, VGGNet, etc.)
• Week 9 (July 21, 2018)[Preliminary Readings]
  • Chapter 10) - Recurrent Neural Networks (Sequence Models, eg. Long Short Term Memory networks
  • Chapter 8) - Deep Learning Frameworks (Tensorflow, PyTorch, Keras, etc.
• Week 10 (July 28, 2018)[Preliminary Readings] <-- We're here
  • Overview of concepts from Chapter 11 and Chapter 13 (Object Detection, Segmentation and Generative NN Models)
  • Project presentations on results and findings from participants
  • Concluding the first cohort of AI6 Saturdays. :)