Fully Connected Neural Networks with Keras by Chris Achard

• Create a Fully Connected TensorFlow Neural Network with Keras
• Train a Sequential Keras Model with Sample Data
• Separate Training and Validation Data Automatically in Keras with validation_split
• Manually Set Validation Data While Training a Keras Model
• Evaluate a Keras Model with Test Data
• Testing Different Neural Network Topologies
• Understand the Structure of a Keras Model by Viewing the Model Summary
• Make Predictions on New Data with a Trained Keras Models
• Save a Trained Keras Model Weights and Topology to a File
• Load and Use a Saved Keras Model
• Create a Neural Network for Two Category Classification with Keras
• Import Data From a CSV to Use with a Keras Model Using NumPy’s genfromtxt Method
• Make Binary Class Predictions with Keras Using predict and predict_classes
• Create a Dense Neural Network for Multi Category Classification with Keras
• Change the Learning Rate of the Adam Optimizer on a Keras Network
• Change the Optimizer Learning Rate During Keras Model Training
• Continue to Train an Already Trained Keras Model with New Data
• Course Introduction: Fully Connected Neural Networks with Keras
• Make Predictions on New Data with a Multi Category Classification Network