Introduction to TensorFlow and Neural Networks for Beginners

Overview

• Platform: Simultaneous live coding on YouTube and Twitch
• Focus: Understanding TensorFlow from the basics
• Prerequisites: Basic knowledge of Python or any other programming language
• Follow-up: Link to all code on GitHub after the session
• Resolution Tip: If resolution issues arise, switch between YouTube and Twitch
• Note: Comments from YouTube won't show up on the coding screen

What is TensorFlow?

• Used for deep learning and machine learning applications
• Simplifies the prediction process by using numbers to predict other numbers
• Examples include predicting house prices using features like the number of bathrooms, bedrooms, and square footage

Neural Networks Basics

• Input Neurons: Features (e.g., number of bathrooms, bedrooms)
• Hidden Layers: Apply different weightings to features to estimate the outcome
• Output Neurons: Predictions made by the neural network
• Types of Learning: Regression line fitting in the beginning

Tools and Libraries Used

• TensorFlow
• Keras
• NumPy
• Pandas
• Seaborn
• Matplotlib
• Scikit-Learn

Google Colab Overview

• Allows use of Google’s servers for processing
• Markdown and code cells for organizing work
• Setup for changing to GPU if required

Data Preparation and Cleaning

• Inspecting and cleaning data for missing values
• Deleting unneeded columns like 'First Name' and 'Last Name'
• Importance of numeric data in TensorFlow
• Data cleaning includes removing unwanted characters like dollar signs and commas
• Normalization and One Hot Encoding

Data Analysis with Box Plots and Histograms

• Box plots help identify outliers that might affect the analysis
• Histograms show the distribution of data
• Example: NBA data showing player statistics for salary prediction

Correlation Matrix

• Used to determine how different features are related to each other
• Example analysis showed features like free throws and points have higher correlation to salary

Building a TensorFlow Model

1. Normalization and One Hot Encoding
   • Convert data into numerical form and scale it between 0 and 1
2. Training and Testing Data Split
   • 80% for training, 20% for testing
3. Creating and Compiling the Model
   • Layers and activation functions
   • Mean Absolute Error (MAE) as the evaluation metric
   • Learning Rate for optimizing weights
4. Fitting and Evaluating the Model
   • Use epochs to iterate over the training data
   • Evaluate model performance using test data
   • Compare different features, activation functions, and learning rates

Practical Tips and Insights

• Improving prediction accuracy by adjusting the number of epochs and learning rate
• Comparing different sets of features to find the optimal model
• General advice on data science includes practicing with various datasets and features

Closing Remarks

• Planned future topics: Multi-label classification, computer vision
• Importance of practical application and using tools like Google Colab

Next Steps

• Future live coding sessions every other Wednesday
• Feedback and community engagement encouraged

Example Code Snippets

# Create a TensorFlow constant
scalar = tf.constant(5)
print(scalar)

# Model compilation
model.compile(
    loss='mae',
    optimizer=tf.optimizers.Adam(learning_rate=0.01),
    metrics=['mae']
)

# Model evaluation
model.evaluate(X_test_norm, y_test)