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In this course, you will :
- Learn the fundamentals required to build your own neural networks.
- Investigate the fundamental principles of how machine learning enables us to create models that learn from data.
- Learn how to apply these principles to neural networks and build a model that predicts the type of clothing in an image.
- Investigate how TensorFlow's built-in tools, such as TensorBoard, make it simple to evaluate and improve the performance of neural networks.
- Learn how to deploy your neural network and make the predictive power of your network available to client applications.
- To create, train, and deploy a predictive neural network, you must be familiar with machine learning and TensorFlow.
Syllabus :
1. Why Learn TensorFlow?
- What Is TensorFlow?
- Why TensorFlow?
- TensorFlow Tools and Languages
- Required Skills
2. Setting up the TensorFlow Environment
- TensorFlow Development Environment
- What Is Google Colaboratory (aka Colab)?
- Getting Started with Colab
- Importing TensorFlow
- Sequencing Code Execution
3. AI and Machine Learning Concepts
- The Relationship of AI to ML
- Implementing Machine Learning
- Creating the Model
- Training the Model
- Reducing Loss
- Evaluating the Trained Model
- What Is a Tensor?
4. Applying the Machine Learning Workflow with TensorFlow
- Defining the Problem and Getting Data
- Exploring the Data
- Preparing the Data
- Creating the Model
- Training the Model
- Improving Performance
- Evaluating Model Performance
5. Understanding Neural Networks
- Machine Learning with Neural Networks
- How Neurons Work
- Neuron Architecture
- Activation Functions
- From Neurons to Neural Networks
- Predicting with an Untrained Neural Network
- Training a Neural Network
6. Building and Training Your First Neural Network
- Building a Neural Network in TensorFlow
- Getting and Preparing the Data
- Creating the Model
- Demo: Creating the Model
- Compiling the Model
- Training and Evaluating the Model
7. Monitoring and Improving Neural Network Performance
- Understanding the Problem with Your Model
- TensorBoard Setup
- Monitoring Your Trained Model’s Performance
- Reducing Training Data Overfitting
- Randomly Dropping out Neuron Output
- Early Stopping
- Saving Your Trained Model
8. Deploying Your Neural Network
- What Is Deploying a Neural Network?
- Installing TensorFlow ModelServer
- Understanding TensorFlow Model Serving
- Using TensorFlow Model Serving
