Predicting the NBA playoffs using neural networks (sorta)
It's my first week at W&B, here's what I learned....
Created on December 9|Last edited on March 16
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Note: I'm coming from a mar/ad-tech background, so bare with me
The project
💡
The model was trained using season data of teams over various categories between 1990 and 2020
Run set
14
After getting the notebook set up and getting the data piped in, I experimented with tinkering with the number of layers in the model. With more Time, I'd leave these number up to sweeps
def make_dnn_model(n_input_features, n_possible_outcomes):x_in = tf.keras.Input(shape=(n_input_features,))x = tf.keras.layers.Dense(128, activation='relu')(x_in)x = tf.keras.layers.Dropout(0.2)(x)x = tf.keras.layers.Dense(32, activation='relu')(x)x = tf.keras.layers.Dropout(0.2)(x)x_out = tf.keras.layers.Dense(n_possible_outcomes, activation='softmax')(x)return tf.keras.models.Model(x_in, x_out, name="DNN_regresson")
Run set
9
Run set
14
First Impressions of W&B
Things that are Awesome 🤩
- Incredibly easy to get working out of the box, really great inherent metadata pulled
- Really great docs
- Everything is incredibly snappy (although the data was small)
Things I struggled with
- Once I got in the app, lots and lots of features, it's almost overwhelming
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