Advanced Deep Learning with Keras

via

DataCamp

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

This course will teach you how to solve many problems using the Keras functional API. Multi-layer dense networks (also known as multilayer perceptionrons) will be the first topic. The course then moves onto more complicated architectures. This course will show you how to create models with multiple inputs and only one output. This course also shows you how to distribute weights between layers. Topics such as category embeddings and multiple-output networks will be covered. This course is ideal for anyone looking to train networks that can do both classification and regression.

Course Overview

Virtual Labs

Job Assistance

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Understanding to Deep Learning in Python

What You Will Learn

Build multiple-input and multiple-output deep learning models using Keras

The course will cover how to build models with multiple inputs and a single output, as well as how to share weights between layers in a model

We will also cover advanced topics such as category embeddings and multiple-output networks

Course Content

Expand all sections

Module 1: The Keras Functional API

Module 2: Two Input Networks Using Categorical Embeddings, Shared Layers, and Merge Layers

Module 3: Multiple Inputs: 3 Inputs (and Beyond!)

Module 4: Multiple Outputs

Course Instructors

Zachary Deane-Mayer

VP, Data Science at DataRobot

Zach is a Data Scientist at DataRobot and co-author of the caret R package. He's fascinated by predicting the future and spends his free time competing in predictive modeling competitions. He's curre...