Have you ever imagined a car driving without a human? A machine will translate any language for you. A machine will diagnose your...

Data processing is a crucial part of deep learning. We can’t feed the training data into the model without data preprocessin...

A convolutional neural network(CNN) is a type of deep learning algorithm that has capable to learn the hierarchical features from...

In traditional neural network systems, inputs and outputs are independent of each other. For some situations, previous outputs are...

Long Short-Term Memory, also referred to as LSTM in artificial neural networks, is a potent and sophisticated architecture created...

Simply put, transformers are a type of neural network that operates by learning context in sequential data. It uses the concept...

Generating new data that has no exists in the world is so fascinating. With the help of deep learning, we can do it easily today b...

An autoencoder is a type of Unsupervised Learning in Deep Learning that is designed to learn the compact, lower-dimensional repres...

Autoencoders are an unsupervised learning technique that can learn the efficient representation of input data and are used for var...

Over recent years Generative Adversarial Networks(GANs), Variational Autoencoders(VAEs), and the family of flow-based generative n...

Reinforcement Learning (RL) is a machine learning type concerned with how agents learn to interact with an environment to maximize...

Optimization is a crucial part of deep learning that is used to update the parameters(weights and bias) of a neural network mode...

kThe performance of a deep learning model can be divided into three parts (Underfitting, Appropriate fitting, and Overfitting). Un...

Without performance analysis, anyone can’t grow his activities. In deep learning, model tracking, and accuracy analysis are...

Hyperparameter is a type of parameter that is external to the model and controls the learning of the model. The value of the hyper...

Put simply, transfer learning is the process where a model developed for a specific purpose is reused as the starting point for an...

The Machine learning model deployment process involves making a model trained on a dataset accessible in systems or applications u...

Deep learning uses neural networks to perform various cognitive and complex tasks. Compared to machine learning algorithms, it imp...

Learning Math is a requirement for mastering deep learning. This is due to the fact that deep learning relies heavily on mathemati...

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