In the ever-evolving landscape of artificial intelligence (AI), one of the most intriguing and rapidly advancing areas is generative AI. This technology is not just reshaping industries but also challenging our perceptions of creativity and automation. But what exactly is generative AI,…
read moreAdvanced 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…
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