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.
Using tf.data for Building Efficient Data Pipelines

Using tf.data for Building Efficient Data Pipelines

Efficiently manipulate and preprocess large datasets with tf.data, a TensorFlow API. Create complex input pipelines from simple reusable pieces, including batching, shuffling, and custom preprocessing. Stream data from disk and reduce training time with prefetching techniques. Build robust data pipelines for machine learning models.