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54 changes: 29 additions & 25 deletions Scalable-Deep-Learning-with-Tensorflow-and-Apache-Spark/AGENDA.py
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# MAGIC %md
# MAGIC # Agenda
# MAGIC ## Scalable Deep Learning with TensorFlow and Apache Spark™
# MAGIC
# MAGIC **Cluster Requirements:**
# MAGIC * See your instructor for specific requirements

# COMMAND ----------

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# MAGIC | Time | Lesson                                                                       | Description                                                                                                                                                                                                                                                   |
# MAGIC |:----:|-------|-------------|
# MAGIC | 30m | **Introductions & Setup** | *Registration, Courseware & Q&As* |
# MAGIC | 25m | **[Spark Review]($./DL 00 - Spark Review) (optional)** | Review core concepts of Apache Spark|
# MAGIC | 25m | **[Linear Regression]($./DL 01 - Linear Regression)** | Build a linear regression model using Sklearn and reimplement it in Keras </br> Modify # of epochs </br> Visualize loss |
# MAGIC | 10m | **Break** ||
# MAGIC | 35m | **[Linear Regression]($./DL 01 - Linear Regression)** | Build a linear regression model using Sklearn and reimplement it in Keras </br> Modify # of epochs </br> Visualize loss |
# MAGIC | 30m | **[Keras]($./DL 02 - Keras)** | Modify these parameters for increased model performance: activation functions, loss functions, optimizer, batch size </br> Save and load models |
# MAGIC | 10m | **Break** ||
# MAGIC | 25m | **[Keras Lab]($./Labs/DL 02L - Keras Lab)** | Build and evaluate your first Keras model! |
# MAGIC | 10m | **Break** ||
# MAGIC | 50m | **[Advanced Keras]($./DL 03 - Advanced Keras)** | Perform data standardization for better model convergence </br> Add validation data </br> Generate model checkpointing/callbacks </br> Use TensorBoard </br> Apply dropout regularization |
# MAGIC | 10m | **Break** ||
# MAGIC | 35m | **[Advanced Keras Lab]($./Labs/DL 03L - Advanced Keras Lab)** | Practice using checkpoints and callbacks
# MAGIC | 20m |**Introduction to [MLflow]($./DL 04 - MLflow)**| MLflow introduction |

# COMMAND ----------

# MAGIC %md
# MAGIC ## Day 1 PM
# MAGIC | Time | Lesson &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | Description &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |
# MAGIC |:----:|-------|-------------|
# MAGIC | 65m | **[Advanced Keras]($./DL 03 - Advanced Keras)** & **[Lab]($./Labs/DL 03L - Advanced Keras Lab)** | Perform data standardization for better model convergence </br> Add validation data </br> Generate model checkpointing/callbacks </br> Use TensorBoard </br> Apply dropout regularization |
# MAGIC | 20m | **Review** | *Review of Day 1 AM* |
# MAGIC | 35m |**[MLflow]($./DL 04 - MLflow)** | Log experiments with MLflow</br> View MLflow UI</br> Generate a UDF with MLflow and apply to a Spark DataFrame |
# MAGIC | 10m | **Break** ||
# MAGIC | 50m |**[MLflow]($./DL 04 - MLflow)** & **[Lab]($./Labs/DL 04L - MLflow Lab)**| Log experiments with MLflow</br> View MLflow UI</br> Generate a UDF with MLflow and apply to a Spark DataFrame |
# MAGIC | 25m | **[MLflow Lab]($./Labs/DL 04L - MLflow Lab)**| Log experiments with MLflow</br> View MLflow UI</br> Generate a UDF with MLflow and apply to a Spark DataFrame |
# MAGIC | 30m | **[HyperOpt]($./DL 05 - Hyperopt)** | Use HyperOpt with SparkTrials to perform distributed hyperparameter search |
# MAGIC | 10m | **Break** ||
# MAGIC | 40m | **[HyperOpt]($./DL 05 - Hyperopt)** & **[Lab]($./Labs/DL 05L - Hyperopt Lab)** | Use HyperOpt with SparkTrials to perform distributed hyperparameter search |
# MAGIC | 30m | **[HyperOpt Lab]($./Labs/DL 05L - Hyperopt Lab)** | Use HyperOpt with SparkTrials to perform distributed hyperparameter search |
# MAGIC | 25m | **Introduction to [Horovod]($./DL 06 - Horovod)** | Horovod concept |
# MAGIC | 10m | **Break** ||
# MAGIC | 35m | **[Horovod]($./DL 06 - Horovod)** | Use Horovod to train a distributed neural network </br> Distributed Deep Learning best practices |
# MAGIC | 45m | **[Horovod]($./DL 06 - Horovod)** | Use Horovod to train a distributed neural network </br> Distributed Deep Learning best practices |

# COMMAND ----------

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# MAGIC | 30m | **[Horovod Petastorm]($./DL 06a - Horovod Petastorm)** | Use Horovod to train a distributed neural network using Parquet files + Petastorm|
# MAGIC | 10m | **Break** ||
# MAGIC | 35m | **[Horovod Lab]($./Labs/DL 06L - Horovod Lab)** | Prepare your data for use with Horovod</br> Distribute the training of our model using HorovodRunner</br> Use Parquet files as input data for our distributed deep learning model with Petastorm + Horovod |
# MAGIC | 35m | **[Model Interpretability]($./DL 07 - Model Interpretability)** | Use LIME and SHAP to understand which features are most important in the model's prediction for that data point |
# MAGIC | 25m | **[Model Interpretability]($./DL 07 - Model Interpretability)** | Use LIME and SHAP to understand which features are most important in the model's prediction for that data point |
# MAGIC | 10m | **Break** ||
# MAGIC | 40m | **[CNNs]($./DL 08 - Distributed Inference with CNNs)** | Analyze popular CNN architectures </br> Apply pre-trained CNNs to images using Pandas Scalar Iterator UDF |
# MAGIC | 30m | **[Distributed Inference with CNNs]($./DL 08 - Distributed Inference with CNNs)** | Analyze popular CNN architectures </br> Apply pre-trained CNNs to images using Pandas Scalar Iterator UDF |
# MAGIC | 30m | **[Shap for CNN Lab]($./Labs/DL 08L - SHAP for CNNs Lab)** | Use SHAP to generate explanation behind a model's predictions |
# MAGIC | 10m | **Break** ||
# MAGIC | 35m | **[Transfer Learning for CNNs]($./DL 09 - Transfer Learning for CNNs)** | Perform transfer learning to create a cat vs dog classifier |

# COMMAND ----------

# MAGIC %md
# MAGIC ## Day 2 PM
# MAGIC | Time | Lesson &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | Description &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |
# MAGIC |:----:|-------|-------------|
# MAGIC | 20m | **[SHAP for CNNs]($./Labs/DL 08L - SHAP for CNNs Lab)** | Use SHAP to visualize how the CNN makes predictions |
# MAGIC | 30m | **[Model Serving]($./DL 09 - Model Serving)** | Real time deployment of a convolutional neural network using REST and Databricks MLflow Model Serving |
# MAGIC | 10m | **Break** ||
# MAGIC | 30m | **[Transfer Learning]($./DL 10 - CNN Focus/DL 10a - Transfer Learning for CNNs)** | Perform transfer learning to create a cat vs dog classifier |
# MAGIC | 25m | **[Data Augmentation]($./DL 10 - CNN Focus/DL 10b - Data Augmentation)** | Apply data augmentation to improve transfer learning performance |
# MAGIC | 10m | **Break** ||
# MAGIC | 30m | **[Transfer Learning Lab]($./DL 10 - CNN Focus/Labs/DL 10L - Transfer Learning Lab)** | Build a model to predict if a patient has pneumonia using transfer learning on chest X-rays |
# MAGIC | 25m | **[Generative Adversarial Networks]($./DL 10 - CNN Focus/DL 10c - Generative Adversarial Networks)** | Understand Generative and discriminative models </br> Build GANs |
# MAGIC | 10m | **Break** ||
# MAGIC | 25m | **[Best Practices]($./Reference/Best Practices)** | Discuss DL best practices, state of the art, and new research areas |
# MAGIC | 20m | **Review** | *Review of Day 2 AM* |
# MAGIC | 30m | **[Model Serving]($./DL 10 - Model Serving)** | Real time deployment of a convolutional neural network using REST and Databricks MLflow Model Serving |
# MAGIC | 10m | **Break** | ||
# MAGIC | 50m | **[Embeddings]($./DL 11 - Embeddings)** | Understand what embeddings are and how to use them |
# MAGIC | 10m | **Break** | ||
# MAGIC | 50m | **[Transfer Learning for NER]($./DL 12 - Transfer Learning for NER)** | Fine-tune a pretrained model to solve named entity recognition|
# MAGIC | 10m | **Break** | ||
# MAGIC | 30m | **[CNN Lab]($./Labs/DL 09L - Transfer Learning for CNNs Lab) or [NLP Lab]($./Labs/DL 12L - Transfer Learning for Document Classification)** | Apply transfer learning to classify pneumonial X-ray images or tweet sentiments|

# COMMAND ----------

# MAGIC %md-sandbox
# MAGIC &copy; 2021 Databricks, Inc. All rights reserved.<br/>
# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the <a href="http://www.apache.org/">Apache Software Foundation</a>.<br/>
# MAGIC &copy; 2022 Databricks, Inc. All rights reserved.<br/>
# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the <a href="https://www.apache.org/">Apache Software Foundation</a>.<br/>
# MAGIC <br/>
# MAGIC <a href="https://databricks.com/privacy-policy">Privacy Policy</a> | <a href="https://databricks.com/terms-of-use">Terms of Use</a> | <a href="http://help.databricks.com/">Support</a>
# MAGIC <a href="https://databricks.com/privacy-policy">Privacy Policy</a> | <a href="https://databricks.com/terms-of-use">Terms of Use</a> | <a href="https://help.databricks.com/">Support</a>
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# COMMAND ----------

# MAGIC %md-sandbox
# MAGIC &copy; 2021 Databricks, Inc. All rights reserved.<br/>
# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the <a href="http://www.apache.org/">Apache Software Foundation</a>.<br/>
# MAGIC &copy; 2022 Databricks, Inc. All rights reserved.<br/>
# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the <a href="https://www.apache.org/">Apache Software Foundation</a>.<br/>
# MAGIC <br/>
# MAGIC <a href="https://databricks.com/privacy-policy">Privacy Policy</a> | <a href="https://databricks.com/terms-of-use">Terms of Use</a> | <a href="http://help.databricks.com/">Support</a>
# MAGIC <a href="https://databricks.com/privacy-policy">Privacy Policy</a> | <a href="https://databricks.com/terms-of-use">Terms of Use</a> | <a href="https://help.databricks.com/">Support</a>

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# COMMAND ----------

# MAGIC %md-sandbox
# MAGIC &copy; 2021 Databricks, Inc. All rights reserved.<br/>
# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the <a href="http://www.apache.org/">Apache Software Foundation</a>.<br/>
# MAGIC &copy; 2022 Databricks, Inc. All rights reserved.<br/>
# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the <a href="https://www.apache.org/">Apache Software Foundation</a>.<br/>
# MAGIC <br/>
# MAGIC <a href="https://databricks.com/privacy-policy">Privacy Policy</a> | <a href="https://databricks.com/terms-of-use">Terms of Use</a> | <a href="http://help.databricks.com/">Support</a>
# MAGIC <a href="https://databricks.com/privacy-policy">Privacy Policy</a> | <a href="https://databricks.com/terms-of-use">Terms of Use</a> | <a href="https://help.databricks.com/">Support</a>
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# COMMAND ----------

# MAGIC %md-sandbox
# MAGIC &copy; 2021 Databricks, Inc. All rights reserved.<br/>
# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the <a href="http://www.apache.org/">Apache Software Foundation</a>.<br/>
# MAGIC &copy; 2022 Databricks, Inc. All rights reserved.<br/>
# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the <a href="https://www.apache.org/">Apache Software Foundation</a>.<br/>
# MAGIC <br/>
# MAGIC <a href="https://databricks.com/privacy-policy">Privacy Policy</a> | <a href="https://databricks.com/terms-of-use">Terms of Use</a> | <a href="http://help.databricks.com/">Support</a>
# MAGIC <a href="https://databricks.com/privacy-policy">Privacy Policy</a> | <a href="https://databricks.com/terms-of-use">Terms of Use</a> | <a href="https://help.databricks.com/">Support</a>
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# COMMAND ----------

# MAGIC %md-sandbox
# MAGIC &copy; 2021 Databricks, Inc. All rights reserved.<br/>
# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the <a href="http://www.apache.org/">Apache Software Foundation</a>.<br/>
# MAGIC &copy; 2022 Databricks, Inc. All rights reserved.<br/>
# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the <a href="https://www.apache.org/">Apache Software Foundation</a>.<br/>
# MAGIC <br/>
# MAGIC <a href="https://databricks.com/privacy-policy">Privacy Policy</a> | <a href="https://databricks.com/terms-of-use">Terms of Use</a> | <a href="http://help.databricks.com/">Support</a>
# MAGIC <a href="https://databricks.com/privacy-policy">Privacy Policy</a> | <a href="https://databricks.com/terms-of-use">Terms of Use</a> | <a href="https://help.databricks.com/">Support</a>
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