Coursera
SubscriptionAdvanced Deep Learning with Keras
4.8(236)INTERMEDIATEEnglish
Korshub's take
Master Complex Architectures
While standard sequential models are great for basics, real-world data often requires more sophisticated structures. This intermediate-level course focuses on the Keras functional API, moving beyond simple stacks to create intricate neural network topologies.
Advanced Network Design
You will move from building simple functional networks to designing complex systems featuring:
- Multiple Inputs: Learn to use categorical embeddings and merge layers to handle diverse data streams.
- Multiple Outputs: Master networks that can tackle regression and classification tasks simultaneously.
- Weight Sharing: Understand how to share weights between layers for more efficient learning.
This is an essential deep dive for aspiring data scientists looking to move past basic models and master the parameters and topology of advanced deep learning architectures.
About this course
Keras functional API In this course, you will learn how to solve complex problems using the Keras functional API. Beginning with an introduction, you will build simple functional networks, fit them to data, and make predictions. You will also learn how to construct models with multiple inputs and a single output and share weights between layers. Multiple-input networksAs you progress, explore building two-input networks using categorical embeddings, shared layers, and merge layers. These are the foundational building blocks for designing neural networks with complex data flows. It extends these concepts to models with three or more inputs, helping you understand the parameters and topology of your neural networks using Keras' summary and plot functions.Multiple-output networksIn the final interactive exercises, you'll work with multiple-output networks, which can solve regression problems with multiple targets and even handle both regression and classification tasks simultaneously. By the end of the course, you'll have practical experience with advanced deep learning techniques to advance your career as a data scientist, including evaluating your models on new data using multiple metrics.
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