Tag: deep learning
Difference between perceptron and neuron?
Perceptrons are a type of artificial neural network that can be used for classification and regression. They are supervised learning algorithms, meaning they need labeled input data in order to learn. how to map inputs to outputs. What independent variables do perceptrons need? Perceptrons require at least one input and one output. What are the…
Read MoreWhat is perceptron?
The perceptron is a type of artificial neural network (ANN) that is designed to recognize patterns in data. It can be used to identify objects, classify images, and detect changes in the environment. The perceptron was invented by Frank Rosenblatt in 1957 while he was working at Cornell Aeronautical Laboratory as part of a research…
Read MoreWhat is L1 and L2 regularization in Deep Learning?
In deep learning, L1 and L2 regularization are regularization techniques used to penalize the model’s weights during the training process. This penalty discourages the model from assigning excessive importance to certain features, thereby reducing the risk of overfitting. L1 Regularization L1 regularization, also known as Lasso regularization, adds a penalty proportional to the absolute value…
Read MoreWhat is the difference between LSTM and GRU?
Introduction to LSTM (Long Short-Term Memory)Imagine you’re at a murder mystery dinner. At the very beginning, the Lord of the Manor suddenly collapses, and your task is to figure out, who done it? It could be the maid or the butler. However, there’s a problem: your short-term memory is not working. You can’t recall any…
Read MoreDifference between Artificial Intelligence, Machine Learning and Deep Learning?
Artificial intelligence (AI) is a term that encompasses computer systems designed to imitate human intelligence. It is an exciting field that has attracted considerable attention in many industries, including finance, hospitality, education and entertainment. Artificial intelligence is planned to simulate human behavior and thought processes, making it one of the most important trends of this…
Read MoreWhat is semi-supervised Learning?
Semi-supervised learning is a technique in between supervised and unsupervised learning. Arguably, it should not be a category of machine learning but only a generalization of supervised learning, but it’s useful to introduce the concept separately. Its aim is to reduce the cost of gathering labelled data by extending a few labels to similar unlabeled…
Read MoreWhat is LSTM and How does it work?
One of the most common problems in RNNs is called gradient vanishing. LSTM architectures help you with this. A very common type of RNN is LSTM. This type of network is much better at capturing long-term dependencies than simple RNNs. The only unusual thing about LSTMs is the way that they compute the hidden state.…
Read MoreWhat is Recurrent Neural Network and how does it work?
Recurrent neural network is a type of deep learning algorithm which is used to process sequential data. The main idea of recurrent neural network is that it can learn from previous information and then use that information to predict the next one. That’s why it’s called a recurrent neural network, because it can go back…
Read MoreWhat are Convolutional Neural Networks? What can They do?
Convolutional Neural Networks were originally developed by Yann Lecun, who was working at Bell Labs in New Jersey, and Geoffrey Hinton, who was working at the University of Toronto. Convolutional neural networks are a type of deep learning model that is used for image recognition. They use the convolution operation to identify features in images…
Read MoreHow to Solve Underfitting in Machine Learning Models
Underfitting is a common problem in machine learning models. This happens when the model is too simple to capture the complexity of the real data, resulting in poor performance on the training and testing datasets. In this article, we will explore what underfitting is and how to solve it using different techniques. What is Underfitting?…
Read More