Program Structure

Module 1: Introduction to Keras

• What is Keras? Overview, key features, and integration with TensorFlow.
• Installing Keras and setting up the environment for building deep learning models.
• Understanding Keras components: Models, layers, activations, loss functions, and optimizers.

Module 2: Building Feedforward Neural Networks with Keras

• Introduction to feedforward neural networks (FNNs).
• Building and training FNNs in Keras: Layers, activation functions, and optimization techniques.
• Evaluating model performance: Loss functions, accuracy, and validation.

Module 3: Convolutional Neural Networks (CNNs) with Keras

• Understanding CNNs and their importance in image recognition and computer vision.
• Building a simple CNN using Keras for image classification tasks.
• Optimizing CNN models with techniques such as dropout, batch normalization, and early stopping.

Module 4: Recurrent Neural Networks (RNNs) and LSTMs with Keras

• Understanding RNNs and LSTMs for sequence modeling, time-series forecasting, and NLP.
• Building and training an RNN using Keras.
• Implementing Long Short-Term Memory (LSTM) networks for improved performance on sequential tasks.

Module 5: Transfer Learning with Keras

• What is transfer learning and its application in Keras?
• Using pre-trained models such as VGG16, ResNet, and Inception in Keras for transfer learning tasks.
• Fine-tuning pre-trained models for specific datasets and applications.

Module 6: Model Optimization and Regularization

• Techniques for optimizing model performance: Hyperparameter tuning, grid search, and random search.
• Regularization techniques: Dropout, L2 regularization, and batch normalization.
• Using callbacks for early stopping and model checkpointing to improve training efficiency.

Module 7: Working with NLP Tasks Using Keras

• Text preprocessing and tokenization techniques in Keras.
• Building an NLP model using Keras and embedding layers for text data.
• Implementing Recurrent Neural Networks (RNNs) and LSTMs for text generation and sequence modeling.

Module 8: Model Evaluation and Deployment

• Evaluating deep learning models using Keras: Cross-validation, confusion matrix, and performance metrics.
• Deploying Keras models for real-world applications using Flask or FastAPI for serving models via REST APIs.
• Integrating models with web applications or cloud-based platforms.

Final Project

• Develop a real-world application using Keras for an industry problem (e.g., image classification, text classification, or time-series forecasting).
• Optimize and deploy the model for practical use.
• Example projects: AI-powered medical image classifier, time-series prediction model, or customer sentiment analysis tool.