Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Building a Neural Network in TensorFlow

Go to class Write review

Overview

Dive into TensorFlow to learn and build basic neural networks. This course covers key elements like layers, neurons, activation functions, and model training. Progress through hands-on code examples, ending with building and assessing a neural network for binary classification.

Syllabus

Unit 1: Initializing and Extending Neural Network Models in TensorFlow

Building a Sequential Model
Adjust Model Neurons and Activation
Fix the Neural Network Model
Adding Layers to Sequential Models
Creating a Model and Layers in TensorFlow

Unit 2: Creating Flexible TensorFlow Models with Model-Building Functions

Flexible Neural Network Models
Modify Neural Network Parameters
Fix TensorFlow Function Errors
Flexible Neural Network Models Task
Creating Flexible TensorFlow Models

Unit 3: Compiling and Training Neural Networks with TensorFlow

Training Neural Networks in TensorFlow
Changing Optimizer and Epochs
Fixing Model Compilation Errors
Implementing the Training Step
Compiling and Training a Neural Network

Unit 4: From Training to Prediction: TensorFlow Models for Decision Making

Making Predictions with TensorFlow
Adjusting Prediction Threshold in TensorFlow
Debugging Prediction of New Input for Trained Model
Predicting Student Outcomes with TensorFlow
Making Predictions with TensorFlow Models

Unit 5: Evaluating TensorFlow Models: From Data to Insight

Using TensorFlow to Evaluate a Model
Change the Evaluation Metric
Fix Neural Network Evaluation Code
Implement TensorFlow's Evaluate Method
Evaluating a TensorFlow Model

Reviews

Start your review of Building a Neural Network in TensorFlow