Deep Learning(DL) is a subfield ofMachine Learning (ML)that uses algorithms similarly to the way neurons are used in the human brain. Deep learning creates artificial neural networks and layers based on how the
The learning rate is a hyperparameter -- a factor that defines the system or sets conditions for its operation prior to the learning process -- that controls how much change the model experiences in response to the estimated error every time the model weights are altered. Learning rates that ...
A deep learning network is a specific implementation of an architecture. It includes both theparametersinternal to the model and thehyperparametersthat are set up before training to control the machine learning process. Types of Deep Learning Models Different types of deep learning models have differe...
Deep learning is a subset ofmachine learningthat uses multilayeredneural networks, called deep neural networks, to simulate the complex decision-making power of the human brain. Some form of deep learning powers most of theartificial intelligence (AI)applications in our lives today. The chief diffe...
Hyperparameter Tuning Selecting appropriate hyperparameters, such as learning rate, batch size, and regularization strength, is crucial for successful fine-tuning. Incorrect choices can lead to suboptimal results. Applications of Fine-Tuning in Deep Learning Fine-tuning is a versatile technique that find...
In deep learning, models can have hundreds or thousands of epochs, each of which can take a significant time to complete, especially models that have hundreds or thousands of parameters. The number of epochs used in the training process is an important hyperparameter that must be carefully sel...
Machine learning is a process of learning how to predict on the basis of the data that is provided to us and adding some weights to the same. These weights or parameters are technically termed as hyper parameters which the machine learning developers
A model is trained by hyperparameters tuning using a training dataset and then tested on a separate dataset called the testing set. If a model performs well on training data, it should work well for the testing set. The scenario in which the model performs well in the training phase but ...
Large language models are trained usingunsupervised learning. With unsupervised learning, models can find previously unknown patterns in data using unlabelled datasets. This also eliminates the need for extensive data labeling, which is one of the biggest challenges in building AI models. ...
The selection of suitable algorithms or models is important to any machine learning project. This process includes selecting a suitable model architecture, adjusting hyperparameters, and verifying the model’s performance usingcross-validation techniques. Model selection varies depending on the nature of ...