Introduction to TensorFlow - Hands-on Workshop

Overview

Explore TensorFlow through hands-on practice, completing an end-to-end tutorial to gain practical skills in building and deploying machine learning models.

Syllabus

• Introduction to TensorFlow
Overview of TensorFlow and its applications
Installation and setup
• Basic TensorFlow Concepts
Tensors and operations
Graphs and sessions (eager execution mode)
Data types, shapes, and broadcasting
• Building Machine Learning Models with TensorFlow
Loading and preprocessing data
Building a simple linear model
Implementing a feed-forward neural network
• Training and Optimization
Loss functions and optimization algorithms
Backpropagation and gradient descent
Monitoring training with TensorBoard
• Model Evaluation
Splitting data into training, validation, and test sets
Evaluating model performance
Understanding overfitting and regularization techniques
• Advanced TensorFlow Techniques
Working with datasets and data pipelines
Using tf.data for efficient data loading
Implementing callbacks for training control
• Deploying TensorFlow Models
Saving and loading models
Exporting models for deployment
Basics of TensorFlow Serving and TensorFlow Lite
• Hands-on Project: End-to-End Model Development
Project outline and dataset introduction
Building, training, and evaluating a custom model
Deploying the model and making predictions
• Conclusion and Next Steps
Recap of key concepts
Resources for further learning
Q&A session and final project showcase

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Subjects

Data Science