Scalable Deep Learning with TensorFlow and Apache Spark

Scalable Deep Learning with TensorFlow and Apache Spark

Summary

This course covers the fundamentals of neural networks with TensorFlow and how to scale your deep learning models with Spark.

Description

This course starts with the basics of the tf.keras API including defining model architectures, optimizers, and saving/loading models. You then learn advanced concepts such as callbacks, regularization, TensorBoard, and activation functions. After training your models, you build integrations with the MLflow tracking API to reproduce and version your experiments. You will apply model interpretability libraries such as LIME and SHAP to understand how the network generates predictions. You will also gain familiarity with Convolutional Neural Networks (CNNs) and how to perform transfer learning to reduce model training time. 

 

Substantial class time is spent on scaling your deep learning applications, from distributed inference with pandas UDFs to distributed hyperparameter search with Hyperopt to distributed model training with Horovod. This course is taught fully in Python.

Duration

2 Days

Objectives

Upon completion of the course, students should be able to:

 

  • Build deep learning models using Keras/TensorFlow

  • Tune hyperparameters at scale with Hyperopt

  • Track experiments using MLflow

  • Apply models at scale using pandas UDFs

  • Scale & train distributed models using Horovod

  • Apply model interpretability libraries to understand & visualize model predictions

  • Use CNNs (convolutional neural networks) and perform transfer learning to reduce model training time

  • Implement Generative Adversarial Networks

Audience

  • Data scientist

  • Machine learning engineer

     

Prerequisites

  • Intermediate experience with Python/pandas

  • Familiarity with machine learning concepts

  • Experience with Spark is helpful, but not required

Additional Notes

  • The appropriate, web-based programming environment will be provided to students
  • This class is taught in Python only
  • For the public classes, this course is often scheduled over two half-days

Upcoming Classes

Date
Time
Location
Price
Sep 17 - 18
9:00 AM - 5:00 PM
Pacific Daylight Time
Online - Virtual - US Pacific
$ 1500.00 USD
Oct 29 - 30
9:00 AM - 5:00 PM
Eastern Daylight Time
Online - Virtual - US Eastern
$ 1500.00 USD
Dec 17 - 18
9:00 AM - 5:00 PM
Eastern Standard Time
Online - Virtual - US Eastern
$ 1500.00 USD