1
Course Introduction: Fully Connected Neural Networks with Keras
1m 54s
2
Create a Fully Connected TensorFlow Neural Network with Keras
4m 31s
3
Train a Sequential Keras Model with Sample Data
2m 34s
4
Separate Training and Validation Data Automatically in Keras with validation_split
2m 37s
5
Manually Set Validation Data While Training a Keras Model
1m 35s
6
Evaluate a Keras Model with Test Data
1m 41s
7
Testing Different Neural Network Topologies
1m 55s
8
Understand the Structure of a Keras Model by Viewing the Model Summary
1m 2s
9
Make Predictions on New Data with a Trained Keras Models
2m 7s
10
Save a Trained Keras Model Weights and Topology to a File
1m 4s
11
Load and Use a Saved Keras Model
1m 7s
12
Create a Neural Network for Two Category Classification with Keras
2m 42s
13
Import Data From a CSV to Use with a Keras Model Using NumPy’s genfromtxt Method
2m
14
Make Binary Class Predictions with Keras Using predict and predict_classes
1m 5s
15
Create a Dense Neural Network for Multi Category Classification with Keras
3m 40s
16
Make Predictions on New Data with a Multi Category Classification Network
2m 49s
17
Change the Learning Rate of the Adam Optimizer on a Keras Network
1m 32s
18
Change the Optimizer Learning Rate During Keras Model Training
1m 17s
19
Continue to Train an Already Trained Keras Model with New Data
1m 31s

Create a Neural Network for Two Category Classification with Keras

python^3.0.0

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We’ll take a Keras network designed for continuous (linear) output, and convert it into a network for binary classification, which can divide data into two classes (for example: “dog” vs “cat”), and can be used for things like sentiment analysis ("positive" vs "negative").

Create a Neural Network for Two Category Classification with Keras

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Course

Fully Connected Neural Networks with Keras