Updates for online

Author: jeffheaton

README.md

@@ -28,14 +28,14 @@ Module|Content
 [Module 3](t81_558_class_03_1_neural_net.ipynb)<br>Week of 09/28/2020 | **Module 3: TensorFlow and Keras for Neural Networks**<ul><li>Part 3.1: Deep Learning and Neural Network Introduction<li>Part 3.2: Introduction to Tensorflow & Keras<li>Part 3.3: Saving and Loading a Keras Neural Network<li>Part 3.4: Early Stopping in Keras to Prevent Overfitting<li>Part 3.5: Extracting Keras Weights and Manual Neural Network Calculation<li>[Module 2: Program](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class2.ipynb) due: 09/29/2020</ul>
 [Module 4](t81_558_class_04_1_feature_encode.ipynb)<br>Week of 10/05/2020 |**Module 4: Training for Tabular Data**<ul><li>Part 4.1: Encoding a Feature Vector for Keras Deep Learning<li>Part 4.2: Keras Multiclass Classification for Deep Neural Networks with ROC and AUC<li>Part 4.3: Keras Regression for Deep Neural Networks with RMSE<li>Part 4.4: Backpropagation, Nesterov Momentum, and ADAM Training<li>Part 4.5: Neural Network RMSE and Log Loss Error Calculation from Scratch<li>[Module 3 Program](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class3.ipynb) due: 10/06/2020</ul>
 [Module 5](t81_558_class_05_1_reg_ridge_lasso.ipynb)<br>Week of 10/12/2020 | **Module 5: Regularization and Dropout**<ul><li>Part 5.1: Introduction to Regularization: Ridge and Lasso<li>Part 5.2: Using K-Fold Cross Validation with Keras<li>Part 5.3: Using L1 and L2 Regularization with Keras to Decrease Overfitting<li>Part 5.4: Drop Out for Keras to Decrease Overfitting<li>Part 5.5: Bootstrapping and Benchmarking Hyperparameters<li>[Module 4 Program](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class4.ipynb) due: 10/13/2020<li> Group Selection due: 10/13/2020</ul>
-[Module 6](t81_558_class_06_1_python_images.ipynb)<br>**Extended Sync on 10/19/2020** | **Module 6: CNN for Vision**<ul>	Part 6.1: Image Processing in Python<li>Part 6.2: Keras Neural Networks for MINST and Fashion MINST<li>Part 6.3: Implementing a ResNet in Keras<li>Part 6.4: Computer Vision with OpenCV<li>Part 6.5: Recognizing Multiple Images with Darknet<li>[Module 5 Program](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class5.ipynb) due: 10/20/2020<li>**We will meet online this week!** (2nd Online Meeting)</ul>
+[Module 6](t81_558_class_06_1_python_images.ipynb)<br>**Meet online on 10/19/2020** | **Module 6: CNN for Vision**<ul>	Part 6.1: Image Processing in Python<li>Part 6.2: Keras Neural Networks for MINST and Fashion MINST<li>Part 6.3: Implementing a ResNet in Keras<li>Part 6.4: Computer Vision with OpenCV<li>Part 6.5: Recognizing Multiple Images with Darknet<li>[Module 5 Program](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class5.ipynb) due: 10/20/2020<li>**We will meet online this week!** (2nd Online Meeting)</ul>
 [Module 7](t81_558_class_07_1_gan_intro.ipynb)<br>Week of 10/26/2020 | **Module 7: Generative Adversarial Networks (GANs)**<ul><li>Part 7.1: Introduction to GANS for Image and Data Generation<li>Part 7.2: Implementing a GAN in Keras<li>Part 7.3: Face Generation with StyleGAN and Python<li>Part 7.4: GANS for Semi-Supervised Learning in Keras<li>Part 7.5: An Overview of GAN Research<li>[Module 6 Assignment](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class6.ipynb) due: 10/27/2020</ul>
 [Module 8](t81_558_class_08_1_kaggle_intro.ipynb)<br>Week of 11/02/2020 | **Module 8: Kaggle**<ul><li>Part 8.1: Introduction to Kaggle<li>Part 8.2: Building Ensembles with Scikit-Learn and Keras<li>Part 8.3: How Should you Architect Your Keras Neural Network: Hyperparameters<li>Part 8.4: Bayesian Hyperparameter Optimization for Keras<li>Part 8.5: Current Semester's Kaggle<li>[Module 7 Assignment](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class7.ipynb) due: 11/03/2020</ul>
 [Module 9](t81_558_class_09_1_keras_transfer.ipynb)<br>**Meet Online on 11/09/2020** | **Module 9: Transfer Learning**<ul><li>Part 9.1: Introduction to Keras Transfer Learning<li>Part 9.2: Popular Pretrained Neural Networks for Keras. <li>Part 9.3: Transfer Learning for Computer Vision and Keras<li>Part 9.4: Transfer Learning for Languages and Keras<li>Part 9.5: Transfer Learning for Keras Feature Engineering<li>[Module 8 Assignment](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class8.ipynb) due: 11/10/2020<li>**We will meet online this week!** (3rd Meeting)</ul>
 [Module 10](t81_558_class_10_1_timeseries.ipynb)<br>Week of 11/16/2020 | **Module 10: Time Series in Keras**<ul><li>Part 10.1: Time Series Data Encoding for Deep Learning, TensorFlow and Keras<li>Part 10.2: Programming LSTM with Keras and TensorFlow<li>Part 10.3: Image Captioning with Keras and TensorFlow<li>Part 10.4: Temporal CNN in Keras and TensorFlow<li>Part 10.5: Predicting the Stock Market with Keras and TensorFlow<li>[Module 9 Assignment](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class9.ipynb) due: 11/27/2020</ul>
 [Module 11](t81_558_class_11_01_spacy.ipynb)<br>Week of 11/23/2020 | **Module 11: Natural Language Processing**<ul><li>Part 11.1: Getting Started with Spacy in Python<li>Part 11.2: Word2Vec and Text Classification<li>Part 11.3: Natural Language Processing with Spacy and Keras<li>Part 11.4: What are Embedding Layers in Keras<li>Part 11.5: Learning English from Scratch with Keras and TensorFlow<li>[Module 10 Assignment](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class10.ipynb) due: 11/24/2020</ul>
 [Module 12](t81_558_class_12_01_ai_gym.ipynb)<br>Week of 11/30/2020 | **Module 12: Reinforcement Learning**<ul><li>Kaggle Assignment due: 12/01/2020 (approx 4-6PM, due to Kaggle GMT timezone)<li>Part 12.1: Introduction to the OpenAI Gym<li>Part 12.2: Introduction to Q-Learning for Keras<li>Part 12.3: Keras Q-Learning in the OpenAI Gym<li>Part 12.4: Atari Games with Keras Neural Networks<li>Part 12.5: How Alpha Zero used Reinforcement Learning to Master Chess</ul>
-[Module 13](t81_558_class_13_01_flask.ipynb)<br>**Meet on 12/07/2020** | **Module 13: Deployment and Monitoring**<ul><li>Part 13.1: Deploying a Model to AWS<li>Part 13.2: Flask and Deep Learning Web Services<li>Part 13.3: AI at the Edge: Using Keras on a Mobile Device<li>Part 13.4: When to Retrain Your Neural Network<li>Part 13.5: Using a Keras Deep Neural Network with a Web Application<li>**We will meet online this week!** (4th Meeting)</ul>
+[Module 13](t81_558_class_13_01_flask.ipynb)<br>**Meet on Online 12/07/2020** | **Module 13: Deployment and Monitoring**<ul><li>Part 13.1: Deploying a Model to AWS<li>Part 13.2: Flask and Deep Learning Web Services<li>Part 13.3: AI at the Edge: Using Keras on a Mobile Device<li>Part 13.4: When to Retrain Your Neural Network<li>Part 13.5: Using a Keras Deep Neural Network with a Web Application<li>**We will meet online this week!** (4th Meeting)</ul>
 [Module 14](t81_558_class_14_01_automl.ipynb)<br>Week of 12/14/2020 | **Module 14: Other Neural Network Techniques**<ul><li>Part 14.1: What is AutoML<li>Part 14.2: Using Denoising AutoEncoders in Keras<li>Part 14.3: Training an Intrusion Detection System with KDD99<li>Part 14.4: Anomaly Detection in Keras<li>Part 14.5: New Technology in Deep Learning<li>Final Project due 12/08/2020</ul>
 
 # Datasets