Schedule

 

Week 1

Wednesday – 17 January

• lecture: an Overview of AI and deep learning
• lecture: structure of class
• readings (these are from the popular press)
  • The AI Revolution: The Road to Superintelligence – Tim Urban
  • The AI Revolution: Our Immortality or Extinction – Tim Urban
  • The Great AI Awakening – Gideon Lewis-Kraus, NYTimes (about Deep Learning)
  • Inside The Mind That Built Google Brain: On Life, Creativity, And Failure – Nico Pitney, HuffPo

Friday – 19 January

• Today’s sole goal: to download and install the necessary software to run the digit recognition software.
• lab 1:
  1. download and Install Anaconda
  2. test that ipython works (type ipython and see if you get the python shell)
  3. install TensorFlow (conda install -c conda-forge tensorflow)
  4. clone the code repository https://github.com/martin-gorner/tensorflow-mnist-tutorial.git
  5. change into that directory   tensorflow-mnist-tutorial
  6. execute your first tensorflow program ipython mnist_1.0_softmax.py
  7. celebrate
  8. help others
  9. sympathetic joy
• problem with tensorflow version mismatch? try this
• lecture: Introduction to Anaconda
• lecture: Introduction to Python
  • Quick Intro to Python Notebook
  • Intro to Numpy Notebook
• Helpful Tutorial if unfamiliar with Python: Introduction to Python for Data Science

Week 2

Monday – 22 – January

• lecture: an insanely cool algorithm
  • Blondie24: Playing at the Edge of AI (amazon) (public library)
• lecture:  Introduction to Machine Learning
  • TensorFlow and Deep Learning without a PhD

Wednesday – 24 January

• lecture: Genetic Algorithms (pdf of Slides)

Friday – 26 January

• lecture: Entropy . (pdf of my rough notes)
• lecture: Matrix Multiplication (pdf of notes)
• lab: 8 Bullet Friday Tensorflow   (direct link to file)
• Sample Numpy Notebook  if you need it (raw file)

 

Week 3

Monday – 29 – January

• continue on the 8 bullet Friday lab
• hands-on Tensorflow graphs and Tensorboard
• if time: Gradient Descent, Delta Rule, and Learning Rates

Wednesday – 31 January

•  lecture: Activation Funcrtions (PDF)
•  lecture: Gradient Descent
• lecture: Delta Rule
• lab: A complete Tensorflow program:
  • ray.py
  • diabetes.py
  • Human readable diabetes data

Friday – 2 February

• Resources
  • But What is a Neural Network (19 minute video)
  • Neural Networks (especially §10.2) – Alexandra Shtabnaya writes: I was surfing the internet looking for a good explanation of the delta rule and I found a great website that really helped clear my confusion. Sections 10.2 to 10.4 were particularly useful, and the website also provided some code examples for implementing the rule (albeit in Java). If anyone is still confused, I would recommend going to this article
• Lab
  • Tensorboard error? Try:
    • pip install –upgrade -I setuptools
    • pip install –ignore-installed –upgrade tensorflow

Week 4

Monday – 5 – February

• review of single layer neural networks.
• Take-A-Problem (pdf of questions)

Wednesday – 7 February

• Review continued
• Test Prep

 

Friday – 9 February

• test #1

Week 5

Monday – 12 – February

• Introduction to Keras
• multilayer networks
• youtube video explaining the meaning of layers
• Keras Python Notebook
  • don’t forget to conda install keras

Wednesday – 14 February

• Deep Visual Semantic Alignments for Generating Image Descriptions
• Keras Python Notebook for Movie Reviews

Friday – 16  February

• Keras Enron Notebook – analyzing text

Week 6

Monday – 19 – February

Wednesday -21 February

• evaluation – False Positive, etc.

Friday – 23  February

• Dog and Cat Notebook – optional

Week 7

Monday – 26 – February

• Introduction to Convolutional Neural Networks – aka covnets & CNN (slides)
• Intro to CovNet Python Notebook (no xp just for reference)

Wednesday – 28 February

• covnets continued –
• lab – covnet with dogs and cats 150xp

Friday – 2 March

• class optional – open lab

Week 8

Monday – 5 – March

• spring break

Wednesday – 7 March

• spring break

Friday – 9 March

• spring break

Week 9

Monday – 12 – March

• Word Embeddings
• Word Embeddings Notebook
• 1D convolutional networks for text.

Wednesday – 14 March

• Understanding Recurrent Networks (slides)

Friday – 16 March

• Lab day

Week 10

Monday – 19 – March

• no class

Wednesday – 21 March

• Quickstart – Google Cloud Datalab
• Request GPU quota

Friday – 23 March

• LSTM (slides)

Week 11

Monday – 26 – March

Wednesday – 28 March

Friday – 30 March

Week 12

Monday – 2 April

• Running on a GPU using Colab – notebook
• AutoML-CodeLab   – this is pretty fun
  • 75 xp
  • 50 xp to train it on something other than clouds

Wednesday – 4 April

• Using a pre-existing CNN to recognize images: VGG16 notebook

Friday – 6 April

• Test Prep: Team Study Guide

Week 13

Monday – 9 April

• Smile Challenge

Wednesday – 11 April

• Test – DNN, CNN, & RNN

Friday – 13 April

Week 14

Monday – 16 April

 

Wednesday – 18 April

Friday – 20 April

Week 15

Monday – 23 April

• Reinforcement Learning

Wednesday – 25 April

Friday – 27 April