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In the realm of artificial intelligence (AI), the adage “garbage in, garbage out” couldn’t ring truer—especially when it comes to the pioneering field of generative AI. The bedrock upon which the marvels of generative models stand is not just the algorithms that drive them but the quality and preparation of the data they’re trained on. Data preparation, often overshadowed by the more glamorous aspects of AI, is in fact an art form that demands attention,

In the constellation of artificial intelligence (AI), generative models shine brightly, illuminating new possibilities for creating content that’s both innovative and realistic. Among these, Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) represent two of the most exciting and widely discussed stars. This exploration will take you on a journey through the universe of generative models, highlighting the mechanisms, applications, and potential of GANs, VAEs, and the emerging frontiers beyond. Table of Contents Toggle The

In the realm of artificial intelligence (AI), neural networks are akin to the intricate web of neurons in the human brain, driving the intelligence and creativity of machines. These networks form the foundation of generative AI, a groundbreaking technology that’s pushing the boundaries of what machines can create. This blog delves into the fascinating world of neural networks, unraveling how they work and why they are pivotal in the evolution of generative AI. Table of

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, and why is it garnering so much attention? This primer aims to demystify generative AI for beginners, offering a foundational understanding of its mechanisms, applications, and potential. Table of Contents Toggle Understanding Generative

Table of Contents Toggle Advanced Level:1. Custom Training Loops:2. TensorFlow Serving:3. TensorFlow Extended (TFX): 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 an instance of the Sparse Categorical Crossentropy loss function. This loss function is commonly used for classification problems when