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Implementing Multi-layer Neural Networks with TFLearn

Implementing Multi-layer Neural Networks with TFLearn

1425/month
Start anytime
Self paced
English
Course by
PluralsightCourses from Pluralsight
Certificate awarded
Intermediate
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Map your Career

Not sure which job profiles this course will open for you? Check out our AI based tool to get a complete personalized career map.

Checklist

Certification

You will get a certificate on completing this course.

University

This course is not affiliated with any university.

Price

This course costs very less.

Edvicer's Rewards

You can get a cashback of ₹ 100 on buying this course.

Why should you choose this course?

Description

Deep learning is one of the hottest topics for machine learning engineers. In this course, you'll quickly jump into building your first neural network using TFLearn on top of Tensorflow.

Syllabus

Course Overview
Overview
Machine Learning vs. Deep Learning
Artificial Neural Networks
Deep Learning Concepts
Summary
Overview
DL Library vs. Frameworks
Tensorflow vs. TFLearn
Demo: MNIST Tensorflow and TFLearn
Demo: TFLearn Documentation
Building TFLearn Development Environments
Demo: Setting up TFLearn Development Environment
Summary
Overview
Core Layers in TFLearn
Demo: Core Layers in TFLearn
TFLearn Convolutional Layers
Demo: Convolutional Layers
TFLearn Recurrent Layers
Demo: Recurrent Layers
TFLearn Estimator
Demo: Estimator Layers
Summary
Overview
Activations in TFLearn
Activations in TFLearn
Demo: TFLearn Activation Functions
Summary
Overview
DataUtils TFLearn
Demo: DataUtils
Data Preprocessing
Real Time Processing with TFLearn
Summary
Overview
Deep Neural Networks
Generative Neural Networks
Demo: Comparing TFLearn Models
What's Next

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