Advanced Deep Learning with Keras
4.8(236)34k studentsINTERMEDIATEEnglish
Korshub's take
Master Complex Neural Architectures
If you have mastered basic sequential models and are ready to tackle sophisticated data flows, this intermediate course is your next logical step. It moves beyond simple stacks of layers to explore the true flexibility of the Keras functional API.
Key Learning Objectives
- Complex Topologies: Learn to design networks with multiple inputs and multiple outputs, including models that handle regression and classification simultaneously.
- Advanced Data Flows: Master the use of categorical embeddings, shared layers, and merge layers to manage intricate data structures.
- Model Diagnostics: Utilize Keras' summary and plot functions to visualize and understand your network's parameters and topology.
This curriculum is specifically designed for aspiring data scientists looking to move from basic implementations to building high-level, professional-grade deep learning models capable of solving multi-target problems.
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 networks
As 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 networks
In 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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