Building Deep Learning Models on Databricks

سرفصل های دوره

In this course, you will learn to train neural network models using TensorFlow and PyTorch, perform distributed training using the Horovod framework, and perform hyperparameter tuning using Hyperopt.

1. Course Overview

1. Course Overview

2. Introducing MLflow on Databricks

1. Prerequisites and Course Outline

2. The Databricks Machine Learning Runtime

3. Introducing MLflow

03. Implementing Deep Learning Models Using TensorFlow and Keras

01. Quick Overview of TensorFlow and Keras

02. Demo - Setting up the Databricks Environment

03. Demo - Exploring and Preprocessing Data

04. Demo - Training a Regression Model Using TensorFlow

05. Demo - Autologging Model Parameters

06. Demo - Comparing Multiple Model Runs

07. Demo - Using Bamboolib for Loading and Exploring Data

08. Demo - Data Cleaning Using Bamboolib - I

09. Demo - Data Cleaning Using Bamboolib - II

10. Demo - Preprocessing Data for Deep Learning

11. Demo - Training Multiple Models Using Multiple Runs

12. Demo - Registering Models and Specifying Stages

13. Demo - Creating a Delta Table for Batch Inference

14. Demo - Using Model for Batch Inference

4. Implementing Deep Learning Models Using PyTorch

1. A Quick Overview of PyTorch

2. Demo - Preprocessing Data for Classification

3. Demo - Training a PyTorch Model Using MLflow Tracking

4. Demo - Use Classic Serving to Serve Model Predictions

5. Demo - Loading Image Data for Classification

6. Demo - Training a PyTorch Image Classification Model

7. Horovod for Distributed Training

8. Demo - Setting up Functions for Single Node Training

9. Demo - Performing Distributed Training Using the Horovod Runner

5. Hyperparameter Tuning Using Hyperopt

1. Understanding Hyperparameters

2. Hyperopt for Hyperparameter Tuning

3. Demo - Hyperparameter Tuning Using Hyperopt

4. Demo - Training the Model with the Best Parameters

5. Summary and Further Study

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تولید کننده: PluralSight