initial website commit

.gitignore

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+/env/
+/.quarto/
+/_book/

_quarto.yml

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+project:
+  type: book
+  output-dir: _book
+
+book:
+  title: "Embedded AI: Principles, Algorithms, and Applications"
+  reader-mode: true
+  cover-image: cover.png
+  favicon: cover.png
+  page-footer:
+    left: |
+      Embedded AI written and edited by Vijay Janapa Reddi and Song Han.
+    right: |
+      This book was built with <a href="https://quarto.org/">Quarto</a>.
+  author: "Vijay Janapa Reddi (Harvard University) and Song Han (MIT)"
+  date: "9/5/2023"
+  chapters:
+    - index.qmd
+    - about.qmd
+    - introduction.qmd
+    - tinyml.qmd
+    - primer.qmd
+    - mlworkflow.qmd
+    - datacollection.qmd
+    - preprocessing.qmd
+    - feature_engineering.qmd
+    - training.qmd
+    - optimizations.qmd
+    - deployment.qmd
+    - mlops.qmd
+    - references.qmd
+appendices:
+    - tools.qmd
+    - resources.qmd
+
+bibliography: references.bib
+
+format:
+  html:
+    theme: zephyr
+  pdf:
+    documentclass: scrreprt
+
+
+

about.qmd

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+# About Us
+
+## Who's This Book For
+
+## Course Structure
+
+## Course Requirements
+
+## Course Materials
+
+## What You'll Learn

copyright.qmd

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+# Copyright

datacollection.qmd

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+# Data Collection
+
+## Data Sources
+
+## Training Data
+
+## Training Data Splits
+
+## Data Labeling
+
+## Types of Data
+

dedication.qmd

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+# Dedication

deployment.qmd

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+# Deployment
+
+

ethics.qmd

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+# Responsible AI
+
+coming soon.
+
+# AI Safety
+
+coming soon.

feature_engineering.qmd

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+# Feature Engineering
+
+coming soon.

foreword.qmd

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+# Foreword

index.qmd

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+# Preface {.unnumbered}
+
+This is a Quarto book.
+
+To learn more about Quarto books visit <https://quarto.org/docs/books>.
+
+# The Philosophy Behind the Book
+
+# Prerequisites
+
+# Conventions Used in This Book
+
+# How to Contact Us
+
+# Contributors

intro.qmd

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+# Introduction
+
+This is a book created from markdown and executable code.
+
+See @knuth84 for additional discussion of literate programming.

introduction.qmd

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+# Introduction
+
+## AI for Beginners
+
+## Machine Learning and Deep Learning
+
+## Machine Learning
+
+## Deep Learning
+
+## Applications of Deep Learning
+
+## Quiz

mlops.qmd

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+# MLOps

mlworkflow.qmd

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+# Machine Learning Workflow
+
+## Data Collection
+
+## Pre-Processing
+
+## Training
+
+## Optimization
+
+## Deployment
+
+## Evaluation
+
+## Quiz

optimizations.qmd

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+# Optimizations
+
+## Software Optimizations
+
+### Compression
+
+### Quantization
+
+### Weight Pruning
+
+### Knowledge Distillation
+
+## Hardware Optimizations
+
+### GPUs
+
+### TPUs
+
+### NPUs

preprocessing.qmd

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+# Pre-processing
+
+## What is Data Pre-processing?
+
+## What’s Involved with Data Pre-processing?
+
+## What’s The Importance Of Data Pre-Processing?
+
+

primer.qmd

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+# Deep Learning Primer
+
+## What are Neural Networks
+
+## What is Deep Learning Training
+
+## What is Deep Learning Inference

references.bib

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+@article{knuth84,
+  author = {Knuth, Donald E.},
+  title = {Literate Programming},
+  year = {1984},
+  issue_date = {May 1984},
+  publisher = {Oxford University Press, Inc.},
+  address = {USA},
+  volume = {27},
+  number = {2},
+  issn = {0010-4620},
+  url = {https://doi.org/10.1093/comjnl/27.2.97},
+  doi = {10.1093/comjnl/27.2.97},
+  journal = {Comput. J.},
+  month = may,
+  pages = {97–111},
+  numpages = {15}
+}
+
+

references.qmd

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+# References {.unnumbered}
+
+::: {#refs}
+:::

resources.qmd

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+# Resources

summary.qmd

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+# Summary
+
+In summary, this book has no content whatsoever.

taxonomy.qmd

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+# Taxonomy of ML Algorithms
+
+## Supervised Learning
+
+## Unsupervised Learning
+
+## Reinforcement Learning
+
+## Quiz

tinyml.qmd

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+# Embedded ML
+
+## CloudML
+
+## EdgeML
+
+## TinyML
+
+### TinyML for IoT Systems
+
+### How does TinyML Work
+
+### Resources are Limited, but so is the Competition
+
+## Exercises

toc.qmd

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+# Table of Contents

tools.qmd

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+# Tools
+

training.qmd

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+# Model Training
+
+## Selecting a Training Dataset
+
+## Neural Network Architectures
+
+### Multilayer Perceptron (MLP)
+
+### Convolutional Neural Networks
+
+### Recurrent Neural Networks
+
+### Transformers
+
+## Back Propagation
+
+## Convergence
+
+## Overfitting and Underfitting
+
+## Hyperparameters
+
+### Epochs
+### Learning Rate
+
+## Transfer Learning
+
+### Optimizer
+
+## Summary
+
+## Quiz
+