Working with TensorFlow Distributions for Probabilistic Modeling

Working with TensorFlow Distributions for Probabilistic Modeling

Unlock the potential of probabilistic modeling with TensorFlow Distributions. This powerful framework enhances statistical analysis by enabling efficient manipulation of various probability distributions, facilitating uncertainty representation, sampling, and inference for statisticians and machine learning experts alike.
Training Models in TensorFlow with tf.keras.Model.fit

Training Models in TensorFlow with tf.keras.Model.fit

Train machine learning models in TensorFlow using tf.keras.Model.fit. This essential function simplifies the training process by handling data preprocessing, gradient computation, and model parameter updates. Control training with arguments like epochs and batch size, and monitor progress with feedback on loss and metrics.
Custom Layers and Models in TensorFlow with tf.keras.layers.Layer

Custom Layers and Models in TensorFlow with tf.keras.layers.Layer

Create unique custom layers and models in TensorFlow with tf.keras.layers.Layer. Customize neural networks to fit specific project needs by defining computation, weights, and trainable parameters. Experiment with novel techniques not yet available in the core library for advanced deep learning research and development.