TensorFlow Basics: Build and Train AI Models with Ease
data-science.
Table of Contents
- 1. ? Structured Content:
- 2. Module 1: Introduction to TensorFlow
- 3. Module 2: Tensors and Data Structures
- 4. Module 3: Building a Computational Graph
- 5. Module 4: TensorFlow for Machine Learning
- 6. Module 5: Creating Neural Networks with Keras API
- 7. Module 6: Training and Evaluating Models
- 8. Module 7: Saving, Loading & Deployment
- 9. Module 8: Mini Project – Build a Digit Classifier
- 10. ? Tools and Technologies Used
- 11. ? Target Audience
- 12. ? Learning Outcomes
? Structured Content:
Module 1: Introduction to TensorFlow
What is TensorFlow and why it’s used
Comparison with other frameworks (PyTorch, Keras)
Installation and setup
TensorFlow ecosystem overview (TensorBoard, TFX, etc.)
Module 2: Tensors and Data Structures
What is a tensor?
Tensor shapes, ranks, and data types
Creating tensors with tf.constant, tf.zeros, and tf.random
Tensor operations and broadcasting
Module 3: Building a Computational Graph
Static vs dynamic computation
Understanding graphs and sessions (TF 1.x vs TF 2.x)
Using @tf.function for performance
Visualization with TensorBoard
Module 4: TensorFlow for Machine Learning
Loading datasets with tf.data
Preprocessing data for model training
Batch, shuffle, repeat – input pipelines explained
Working with CSV and image datasets
Module 5: Creating Neural Networks with Keras API
Introduction to tf.keras
Sequential vs Functional API
Defining layers (Dense, Dropout, Conv2D, etc.)
Compiling the model (loss, optimizer, metrics)
Module 6: Training and Evaluating Models
Fitting the model with model.fit()
Monitoring accuracy and loss
Validation and early stopping
Evaluating with model.evaluate() and predicting with model.predict()
Module 7: Saving, Loading & Deployment
Saving models (model.save(), SavedModel format)
Loading pre-trained models
Exporting models for web or mobile (TensorFlow Lite)
Intro to TensorFlow Hub
Module 8: Mini Project – Build a Digit Classifier
Use the MNIST dataset
Create a basic CNN in TensorFlow
Train, evaluate, and deploy the model
Visualize predictions with Matplotlib
? Tools and Technologies Used
TensorFlow 2.x
Python
NumPy & Pandas
Jupyter Notebooks / Google Colab
TensorBoard for visual insights
? Target Audience
Beginners in AI or machine learning
Data analysts wanting to move into AI
Python developers exploring TensorFlow
Students in computer science or engineering
Tech professionals looking to upskill
? Learning Outcomes
By the end of this lesson, learners will:
Understand TensorFlow architecture and components
Build and train machine learning models
Use TensorFlow’s Keras API for rapid development
Visualize training and debug with TensorBoard
Prepare models for deployment across platforms
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