eaglelandsonce/TensorFlowClass

1import streamlit as st2 3# Set the title of the page4st.title("TensorFlow and Keras Course Overview")5 6# Embed the YouTube video7st.video("https://www.youtube.com/watch?v=FRoYik9P9Ko")8 9# Add some description or additional content if needed10st.write("""11Welcome to the TensorFlow Getting Started Class Meetup! 12In this session, we will cover the basics of TensorFlow and how you can get started with building machine learning models using prompt engineering.13""")14 15# Introduction section16st.header("1. Introduction to TensorFlow and Keras")17 18st.subheader("Example: Build a simple linear regression model to predict house prices")19st.markdown("""20**Concepts Covered:**21- Basic TensorFlow and Keras syntax22- Linear regression23- Mean squared error24""")25 26# Building and Training a Simple Neural Network section27st.header("2. Building and Training a Simple Neural Network")28st.subheader("Example: Create a neural network to classify handwritten digits from the MNIST dataset")29st.markdown("""30**Concepts Covered:**31- Dense layers32- Activation functions33- Training loops34- Evaluation35""")36 37# Convolutional Neural Networks (CNNs) section38st.header("3. Convolutional Neural Networks (CNNs)")39st.subheader("Example: Develop a CNN to classify images from the CIFAR-10 dataset")40st.markdown("""41**Concepts Covered:**42- Convolutional layers43- Pooling layers44- Data augmentation45- Dropout46""")47 48# Transfer Learning section49st.header("4. Transfer Learning")50st.subheader("Example: Use a pre-trained model (e.g., VGG16) for image classification on a custom dataset")51st.markdown("""52**Concepts Covered:**53- Transfer learning54- Fine-tuning55- Feature extraction56""")57 58# Recurrent Neural Networks (RNNs) section59st.header("5. Recurrent Neural Networks (RNNs)")60st.subheader("Example: Build an RNN to predict stock prices based on historical data")61st.markdown("""62**Concepts Covered:**63- Recurrent layers64- LSTM65- GRU66- Time series forecasting67""")68 69# Natural Language Processing (NLP) with Keras section70st.header("6. Natural Language Processing (NLP) with Keras")71st.subheader("Example: Create a text classification model to classify movie reviews as positive or negative")72st.markdown("""73**Concepts Covered:**74- Tokenization75- Embedding layers76- Sequence padding77- Sentiment analysis78""")79 80# Autoencoders for Anomaly Detection section81st.header("7. Autoencoders for Anomaly Detection")82st.subheader("Example: Implement an autoencoder to detect anomalies in credit card transactions")83st.markdown("""84**Concepts Covered:**85- Encoder-decoder architecture86- Reconstruction loss87- Anomaly detection88""")89 90# Generative Adversarial Networks (GANs) section91st.header("8. Generative Adversarial Networks (GANs)")92st.subheader("Example: Develop a GAN to generate synthetic images of handwritten digits")93st.markdown("""94**Concepts Covered:**95- Generator and discriminator networks96- Adversarial training97- Loss functions98""")99 100# Hyperparameter Tuning with Keras Tuner section101st.header("9. Hyperparameter Tuning with Keras Tuner")102st.subheader("Example: Use Keras Tuner to optimize hyperparameters for a neural network model")103st.markdown("""104**Concepts Covered:**105- Hyperparameter tuning106- Keras Tuner API107- Performance optimization108""")109 110# Deploying a TensorFlow Model section111st.header("10. Deploying a TensorFlow Model")112st.subheader("Example: Deploy a trained model as a web service using TensorFlow Serving and create a simple web app to interact with it")113st.markdown("""114**Concepts Covered:**115- Model saving and loading116- TensorFlow Serving117- REST API118- Deployment119""")120