T81 558:Applications of Deep Neural Networks

Fall 2016, Mondays, 6 - 8:30 PM, Seigle, #109

Course Description

Deep learning is a group of exciting new technologies for neural networks. By using a combination of advanced training techniques and neural network architectural components, it is now possible to train neural networks of much greater complexity. This course will introduce the student to deep belief neural networks, regularization units (ReLU), convolution neural networks and recurrent neural networks. High performance computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Deep learning allows a model to learn hierarchies of information in a way that is similar to the function of the human brain. Focus will be primarily upon the application of deep learning, with some introduction to the mathematical foundations of deep learning. Students will use the Python programming language to architect a deep learning model for several of real-world data sets and interpret the results of these networks.

Objectives

Explain how neural networks (deep and otherwise) compare to other machine learning models.
Determine when a deep neural network would be a good choice for a particular problem.
Demonstrate your understanding of the material through a final project uploaded to GitHub.

Syllabus

This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

Class	Content
Class 1 08/29/2016	Python for Machine Learning
Labor Day 09/05/2016	No class session
Class 2 09/12/2016	Neural Network & Machine Learning Basics Introduction to TensorFlow Assignment: Read Chapter 1
Class 3 09/19/2016	Training a Neural Network Assignment: Read Chapters 4 & 5
Class 4 09/26/2016	Classification & Regression Assignment: Program 1 Due
Class 5 10/03/2016	Backpropagation Assignment: Read Chapter 6
Class 6 10/10/2016	Preprocessing Assignment: Program 2 Due
Fall Break 10/17/2016	No class session
NO CLASS 10/24/2016	No class session
Class 7 10/31/2016	Kaggle Datasets Evaluating Neural Network Performance Assignment: Take Home Midterm Due
Class 8 11/07/2016	Convolutional Neural Networks
Class 9 11/14/2016	Regularization and Dropout Assignment: Read Chapter 12
Class 10 11/21/2016	Timeseries and Recurrent Neural Networks Assignment: Read Chapter 13, Program 3 Due (submitted on Kaggle.com)
Class 11 11/28/2016	Natural Language Processing Assignment: Read Chapter 14
Class 12 12/05/2016	Applications of Neural Networks Assignment: Program 4 Due
Class 13 12/12/2016	Advanced Deep Learning Topics
Class 14 12/19/2016	GPU, HPC and Cloud Assignment: Final Project Due

Datasets

Iris - Classify between 3 iris species.
Auto MPG - Regression to determine MPG.
WC Breast Cancer - Binary classification: malignant or benign.
toy1 - The toy1 dataset, regression for weights of geometric solids.

*Note: Other datasets will be added as the class progresses.

Assignments

Other Information

Helpful Functions - Helpful Python functions for this class.
KDD99 Example
Video Tutorial on Using Data Scientist Workbench - See how to add data to Data Scientist Workbench
TensorFlow Versions - How to install the version of TensorFlow used in this class.
Care and Feeding of Python - Some useful commands for a local Python install. Not needed if you are using Data Scientist Workbench.

Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
data		data
Allstate-GpLearn.ipynb		Allstate-GpLearn.ipynb
Allstate.ipynb		Allstate.ipynb
AllstateVw.ipynb		AllstateVw.ipynb
Allstatexgboost.ipynb		Allstatexgboost.ipynb
HyperoptTuning-AllState-Keras.ipynb		HyperoptTuning-AllState-Keras.ipynb
HyperoptTuning-AllState-TFDNN.ipynb		HyperoptTuning-AllState-TFDNN.ipynb
HyperoptTuning-AllState-XGBNative.ipynb		HyperoptTuning-AllState-XGBNative.ipynb
HyperoptTuning-AllState-XGBScikit.ipynb		HyperoptTuning-AllState-XGBScikit.ipynb
HyperoptTuning.ipynb		HyperoptTuning.ipynb
HyperoptTuningH2o.ipynb		HyperoptTuningH2o.ipynb
LICENSE		LICENSE
Oronite.ipynb		Oronite.ipynb
README.md		README.md
RandomizedSearchCV.ipynb		RandomizedSearchCV.ipynb
SigoptParameterTuner.ipynb		SigoptParameterTuner.ipynb
T81_DNN_Kaggle.ipynb		T81_DNN_Kaggle.ipynb
TestRegression.ipynb		TestRegression.ipynb
Untitled.ipynb		Untitled.ipynb
Untitled1.ipynb		Untitled1.ipynb
Untitled2.ipynb		Untitled2.ipynb
Untitled3.ipynb		Untitled3.ipynb
XGBoostmodels.ipynb		XGBoostmodels.ipynb
allstate_bayesian.ipynb		allstate_bayesian.ipynb
allstateh2o.ipynb		allstateh2o.ipynb
bayesian_optimization.ipynb		bayesian_optimization.ipynb
datasets_iris.ipynb		datasets_iris.ipynb
datasets_mpg.ipynb		datasets_mpg.ipynb
datasets_toy1.ipynb		datasets_toy1.ipynb
datasets_wcbc.ipynb		datasets_wcbc.ipynb
jeffs_helpful.ipynb		jeffs_helpful.ipynb
kerasbagger.py		kerasbagger.py
o.ipynb		o.ipynb
sigoptexperimentcreator.ipynb		sigoptexperimentcreator.ipynb
stackregression.py		stackregression.py
sub_allstate_20folds.csv		sub_allstate_20folds.csv
svmtovw.py		svmtovw.py
t81_558_class1_intro_python.ipynb		t81_558_class1_intro_python.ipynb
t81_558_class2_tensor_flow.ipynb		t81_558_class2_tensor_flow.ipynb
t81_558_class3_training.ipynb		t81_558_class3_training.ipynb
t81_558_class4_class_reg.ipynb		t81_558_class4_class_reg.ipynb
t81_558_class5_backpropagation.ipynb		t81_558_class5_backpropagation.ipynb
t81_558_class6_preprocessing.ipynb		t81_558_class6_preprocessing.ipynb
t81_558_class7_kaggle.ipynb		t81_558_class7_kaggle.ipynb
t81_558_class8_cnn.ipynb		t81_558_class8_cnn.ipynb
temp_scores.txt		temp_scores.txt
tf_kdd99.ipynb		tf_kdd99.ipynb
tf_versions.md		tf_versions.md
utils.py		utils.py

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jbliss1234/ML

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T81 558:Applications of Deep Neural Networks

Course Description

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Assignments

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