Advanced Level: 1. Custom Training Loops: Understand and implement custom training loops for greater control. python code loss_object = tf.keras.losses.SparseCategoricalCrossentropy() def train_step(inputs, targets): with tf.GradientTape() as tape: predictions = model(inputs) loss = loss_object(targets, predictions) gradients = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(gradients, model.trainable_variables)) Explanation: Creates…
read moreGenerative Adversarial Networks (GANs) have emerged as a groundbreaking technology in the field of artificial intelligence, enabling the generation of realistic and high-quality synthetic data. GANs consist of two neural networks, a generator, and a discriminator, engaged in a continuous adversarial training…
read moreIntermediate Level: 1. Customizing Models with the Functional API: Explore the Functional API for more complex model architectures. python code inputs = tf.keras.Input(shape=(input_size,)) x = tf.keras.layers.Dense(64, activation=’relu’)(inputs) outputs = tf.keras.layers.Dense(10, activation=’softmax’)(x) model = tf.keras.Model(inputs=inputs, outputs=outputs) Explanation: Creates an input layer with a…
read moreTitle: Understanding the Step-by-Step Architecture of Generative Adversarial Networks (GANs) Introduction: Generative Adversarial Networks (GANs) have gained significant attention for their ability to generate realistic data through an adversarial training process. In this article, we will delve into the step-by-step architecture of…
read more1. What is Keras? Keras is an open-source high-level neural networks API written in Python. It acts as an interface for deep learning libraries, providing a user-friendly way to define and train deep learning models. Developed with a focus on simplicity and…
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