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Home Become a Neural Networks for Machine Learning Specialist Echo State Networks

Course Lessons

Become a Neural Networks for Machine Learning Specialist

1 Why do We Need Machine Learning

2 What are Neural Networks

3 Some Simple Models of Neurons

4 A Simple Example of Learning

5 Three Types of Learning

6 Types of Neural Network Architectures

7 Perceptrons the First Generation of Neural Networks

8 A Geometrical View of Perceptrons

9 Why the Learning Works

10 What Perceptrons Can't Do

11 Learning the Weights of a Linear Neuron

12 The Error Surface for a Linear Neuron

13 Learning the Weights of a Logistic Output Neuron

14 The Backpropagation Algorithm

15 Using the Derivatives Computed by Backpropagation

16 Learning to Predict the Next Word

17 A Brief Diversion into Cognitive science

18 Another Diversion the Softmax Output Function

19 Neuro Probabilistic Language Models

20 Ways to Deal with the Large Number of Possible Outputs

21 Why Object Recognition is Difficult

22 Achieving Viewpoint Invariance

23 Convolutional Nets for Digit Recognition

24 Convolutional Nets for Object Recognition

25 Overview of Mini Batch Gradient Descent

« ‹ › » page 1 / 3

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