Complete Tensorflow 2 and Keras Deep Learning Bootcamp Course Syllabus

An all-in-one deep learning bootcamp covering TensorFlow 2, Keras, and real-world AI projects—from model building to deployment. This course is structured to take you from foundational concepts to advanced architectures with hands-on coding and practical applications. Expect to spend approximately 10 hours completing all modules, including lectures, coding exercises, and project work.

Module 1: Introduction to TensorFlow and Keras

Estimated time: 0.5 hours

• Overview of deep learning and the TensorFlow ecosystem
• Installing TensorFlow 2
• Setting up the development environment

Module 2: Tensors and Basic Operations

Estimated time: 0.75 hours

• Creating and manipulating tensors
• Understanding tensor data types and shapes
• Broadcasting and reshaping tensors
• Performing tensor arithmetic operations

Module 3: Neural Networks with Keras

Estimated time: 1 hour

• Building models using the Sequential API
• Implementing models with the Functional API
• Understanding activation functions, loss functions, and optimizers
• Compiling and training neural networks

Module 4: Image Classification with CNNs

Estimated time: 1 hour

• Introduction to convolutional neural networks (CNNs)
• Building CNNs from scratch
• Training models on MNIST and CIFAR-10 datasets
• Evaluating image classification performance

Module 5: Recurrent Neural Networks and Time Series

Estimated time: 1 hour

• Building RNNs for sequential data
• Implementing LSTMs and GRUs
• Time series forecasting and pattern recognition

Module 6: Natural Language Processing (NLP) with TensorFlow

Estimated time: 1 hour

• Text tokenization and preprocessing
• Word embeddings and embedding layers
• Sentiment analysis with Keras
• Building NLP pipelines for classification

Module 7: Generative Adversarial Networks (GANs)

Estimated time: 1 hour

• Introduction to GANs and their architecture
• Understanding generator and discriminator networks
• Creating simple image generators using GANs

Module 8: TensorFlow Tools and Visualization

Estimated time: 0.75 hours

• Using TensorBoard for training visualization
• Model saving and checkpointing
• Monitoring performance metrics

Module 9: Model Deployment and TFLite

Estimated time: 0.75 hours

• Exporting models using TensorFlow Serving
• Converting models to TFLite for mobile devices
• Deploying models on embedded systems

Module 10: Capstone Projects

Estimated time: 1.25 hours

• Building an end-to-end computer vision project
• Developing an NLP-based classification model
• Training, evaluating, and deploying deep learning models

Prerequisites

• Basic knowledge of Python programming
• Familiarity with Jupyter Notebooks
• Understanding of fundamental machine learning concepts

What You'll Be Able to Do After

• Build and train neural networks using TensorFlow 2 and Keras
• Develop CNNs for image classification tasks
• Apply RNNs, LSTMs, and GRUs to time series and sequence data
• Process and analyze text using NLP techniques
• Deploy deep learning models to production environments using TFLite and TensorFlow Serving