Deep learning is a subset of machine learning that utilizes artificial neural networks with multiple layers (hence “deep”) to learn and extract features from data. It has gained significant attention and popularity due to its ability to automatically learn hierarchical representations of data, which allows for more effective feature extraction and modeling of complex relationships…
In neural networks, there are so many hyper-parameters that you can play around with and tune the network to get the best results. Some of them are: Here’s an example of building a neural network model with two hidden layers using the Sequential API in TensorFlow/Keras: Once we are done with the model architecture, we…
Gradient Descent is an optimization algorithm commonly used in machine learning and deep learning to minimize the loss function and find the optimal parameters (weights and biases) of a model. It’s based on the principle of iteratively moving in the direction of the steepest descent of the loss function with respect to the model parameters.…
TensorFlow is an open-source machine learning library developed by Google Brain team. It is one of the most popular frameworks for building and training machine learning and deep learning models. TensorFlow provides a comprehensive ecosystem of tools, libraries, and community resources to facilitate the development and deployment of various types of machine learning models. Key…
