app.py

import streamlit as st

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

# Embed the YouTube video
st.video("https://www.youtube.com/watch?v=FRoYik9P9Ko")

# Add some description or additional content if needed
st.write("""
Welcome to the TensorFlow Getting Started Class Meetup! 
In this session, we will cover the basics of TensorFlow and how you can get started with building machine learning models using prompt engineering.
""")

# 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
""")