Update app.py

app.py

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-# first page
+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
+""")