Overview
In this course, you will delve into advanced topics related to machine learning, focusing on enhancing the accuracy of neural network predictive models. Explore the different types of neural networks and their implementations using the open-source machine learning framework ENCOG.
Are you concerned about your neural network model's prediction accuracy? Unsure about selecting the right neural network model for your machine learning problems? This advanced course builds upon the foundational concepts introduced in the "Introduction to Machine Learning with ENCOG 3" course, guiding you through more complex implementations in machine learning.
In this comprehensive course, learn about various optimization techniques to tackle underfitting and overfitting issues, thereby creating more precise predictive models. Gain insights into the diverse architectures of neural networks and understand the rationale behind their designs.
The primary focus will be on the implementation of various supervised feed-forward and feedback networks. Throughout the course, we will utilize the open-source ENCOG framework to demonstrate these concepts, though the principles covered are applicable across other frameworks and custom development scenarios.
Provider: Pluralsight
Categories: Machine Learning Courses, Neural Networks Courses, Supervised Learning Courses
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