Categories
Entrepeneurship
Deep Learning – Artificial Neural Networks with TensorFlow
Course Curriculum
Welcome
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Introduction
00:00 -
Outline
00:00
Machine Learning and Neurons
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What Is Machine Learning?
00:00 -
Code Preparation (Classification Theory)
00:00 -
Classification Notebook
00:00 -
Code Preparation (Regression Theory)
00:00 -
Regression Notebook
00:00 -
The Neuron
00:00 -
How Does a Model ‘Learn’?
00:00 -
Making Predictions
00:00 -
Saving and Loading a Model
00:00 -
Why Keras?
00:00 -
Suggestion Box
00:00
Feedforward Artificial Neural Networks
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Artificial Neural Networks Section Introduction
00:00 -
Forward Propagation
00:00 -
The Geometrical Picture
00:00 -
Activation Functions
00:00 -
Multiclass Classification
00:00 -
How to Represent Images
00:00 -
Code Preparation (Artificial Neural Networks)
00:00 -
ANN for Image Classification
00:00 -
ANN for Regression
00:00 -
How to Choose Hyperparameters
00:00
In-Depth: Loss Functions
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Mean Squared Error
00:00 -
Binary Cross Entropy
00:00 -
Categorical Cross Entropy
00:00
In-Depth: Gradient Descent
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Gradient Descent
00:00 -
Stochastic Gradient Descent
00:00 -
Momentum
00:00 -
Variable and Adaptive Learning Rates
00:00 -
Adam Optimization (Part 1)
00:00 -
Adam Optimization (Part 2)
00:00
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£82.99
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LevelIntermediate
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Last UpdatedOctober 22, 2024
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CertificateCertificate of completion
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