Category: Keras Core Concepts

Deep learning models often require days, weeks, or even months to train. They may use large datasets, GPU resources, and multiple tuning cycles before achieving the desired performance. Once a model is trained, the ability to save and reload it becomes crucial for many reasons: preserving progress, sharing results with others, deploying models into real-world…

1. Introduction Training a neural network is only half the journey. The true measure of a model’s success lies in how well it performs on unseen data—data that it did not encounter during training. In deep learning, it is very common for models to achieve excellent results on the training dataset but fail to generalize…

Training deep learning models is often a complex and delicate process. It involves tuning hyperparameters, managing learning rates, preventing overfitting, saving progress, monitoring metrics, and ensuring that training runs smoothly. Doing all of this manually is nearly impossible—especially when training large models that run for hours or days. This is where Callbacks come in. Callbacks…

Training a model is the most essential step in any deep learning workflow, and in Keras, this process is primarily handled through the fit() method. The fit() method is not just a function call; it is the central engine that drives the learning process, orchestrating all computations behind the scenes. Understanding how this method works,…

Deep learning models are powerful tools capable of solving complex problems across domains such as computer vision, natural language processing, audio recognition, healthcare analytics, and more. Building such models involves several stages, including data preprocessing, model definition, compilation, training, evaluation, and deployment. Among these steps, model compilation plays a crucial but often misunderstood role. Before…

Deep learning has rapidly evolved over the past decade, increasing in both complexity and capability. While many beginners start with simple Sequential models in Keras, real-world deep learning often demands far more advanced and flexible architectures. Applications like image recognition, natural language processing, speech modeling, recommendation systems, and generative models rarely follow a simple “stack…

1. Introduction Deep learning has become one of the most powerful technologies in modern computing, enabling sophisticated tasks such as image classification, natural language processing, speech recognition, and predictive analytics. Among the many tools available for building deep neural networks, Keras stands out as one of the most user-friendly and intuitive frameworks. At the heart…

Keras has become one of the most popular deep learning frameworks in the world because of its clarity, simplicity, and user-friendly design. Unlike many low-level machine learning frameworks that require detailed knowledge of backend operations, optimization rules, and complex mathematical functions, Keras abstracts most of these complexities behind a clean and modular API. This is…