Introduction to TensorFlow and Neural Networks for Beginners

Overview

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

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

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

Normalization and One Hot Encoding
- Convert data into numerical form and scale it between 0 and 1
Training and Testing Data Split
- 80% for training, 20% for testing
Creating and Compiling the Model
- Layers and activation functions
- Mean Absolute Error (MAE) as the evaluation metric
- Learning Rate for optimizing weights
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

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

# 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)