app.py

import streamlit as st

# Set the title of the page
st.title("TensorFlow and Keras Course Overview")

# Introduction section
st.header("1. Introduction to TensorFlow and Keras")
st.subheader("Example: Build a simple linear regression model to predict house prices")
st.markdown("""
**Concepts Covered:**
- Basic TensorFlow and Keras syntax
- Linear regression
- Mean squared error
""")

# Building and Training a Simple Neural Network section
st.header("2. Building and Training a Simple Neural Network")
st.subheader("Example: Create a neural network to classify handwritten digits from the MNIST dataset")
st.markdown("""
**Concepts Covered:**
- Dense layers
- Activation functions
- Training loops
- Evaluation
""")

# Convolutional Neural Networks (CNNs) section
st.header("3. Convolutional Neural Networks (CNNs)")
st.subheader("Example: Develop a CNN to classify images from the CIFAR-10 dataset")
st.markdown("""
**Concepts Covered:**
- Convolutional layers
- Pooling layers
- Data augmentation
- Dropout
""")

# Transfer Learning section
st.header("4. Transfer Learning")
st.subheader("Example: Use a pre-trained model (e.g., VGG16) for image classification on a custom dataset")
st.markdown("""
**Concepts Covered:**
- Transfer learning
- Fine-tuning
- Feature extraction
""")

# Recurrent Neural Networks (RNNs) section
st.header("5. Recurrent Neural Networks (RNNs)")
st.subheader("Example: Build an RNN to predict stock prices based on historical data")
st.markdown("""
**Concepts Covered:**
- Recurrent layers
- LSTM
- GRU
- Time series forecasting
""")

# Natural Language Processing (NLP) with Keras section
st.header("6. Natural Language Processing (NLP) with Keras")
st.subheader("Example: Create a text classification model to classify movie reviews as positive or negative")
st.markdown("""
**Concepts Covered:**
- Tokenization
- Embedding layers
- Sequence padding
- Sentiment analysis
""")

# Autoencoders for Anomaly Detection section
st.header("7. Autoencoders for Anomaly Detection")
st.subheader("Example: Implement an autoencoder to detect anomalies in credit card transactions")
st.markdown("""
**Concepts Covered:**
- Encoder-decoder architecture
- Reconstruction loss
- Anomaly detection
""")

# Generative Adversarial Networks (GANs) section
st.header("8. Generative Adversarial Networks (GANs)")
st.subheader("Example: Develop a GAN to generate synthetic images of handwritten digits")
st.markdown("""
**Concepts Covered:**
- Generator and discriminator networks
- Adversarial training
- Loss functions
""")

# Hyperparameter Tuning with Keras Tuner section
st.header("9. Hyperparameter Tuning with Keras Tuner")
st.subheader("Example: Use Keras Tuner to optimize hyperparameters for a neural network model")
st.markdown("""
**Concepts Covered:**
- Hyperparameter tuning
- Keras Tuner API
- Performance optimization
""")

# Deploying a TensorFlow Model section
st.header("10. Deploying a TensorFlow Model")
st.subheader("Example: Deploy a trained model as a web service using TensorFlow Serving and create a simple web app to interact with it")
st.markdown("""
**Concepts Covered:**
- Model saving and loading
- TensorFlow Serving
- REST API
- Deployment
""")