Following are the different types of deep neural networks:-
<> FeedForward Neural Network:- This is the most basic type of neural network, in which flow control starts at the input layer and moves to the output layer. These networks only have a single layer or a single hidden layer. There is no backpropagation mechanism in this network because data only flows in one way. The input layer of this network receives the sum of the weights present in the input. These networks are utilised in the computer vision-based facial recognition method.
<> Radial Basis Function Neural Network:- This type of neural network usually has more than one layer, preferably two. The relative distance from any location to the center is determined in this type of network and passed on to the next layer. In order to avoid blackouts, radial basis networks are commonly employed in power restoration systems to restore power in the shortest period possible.
<> Multi-Layer Perceptrons (MLP):- A multilayer perceptron (MLP) is a type of feedforward artificial neural network (ANN). MLPs are the simplest deep neural networks, consisting of a succession of completely linked layers. Each successive layer is made up of a collection of nonlinear functions that are the weighted sum of all the previous layer's outputs (completely linked). Speech recognition and other machine learning systems rely heavily on these networks.
Posted Date:- 2022-02-15 11:11:58
Explain the importance of LSTM.
What is Exploding Gradient Descent?
What is Vanishing Gradient? And how is this harmful?
What are some issues faced while training an RNN?
Explain the different Layers of CNN.
List a few advantages of TensorFlow?
Name a few deep learning frameworks
What are the Hperparameteres? Name a few used in any Neural Network.
What’s the difference between a feed-forward and a backpropagation neural network?
Why is Weight Initialization important in Neural Networks?
Which is Better Deep Networks or Shallow ones? and Why?
What Is Data Normalization And Why Do We Need It?
What are the different parts of a multi-layer perceptron?
What is a Multi-Layer-Perceptron
What are the shortcomings of a single layer perceptron?
What are the steps for using a gradient descent algorithm?
What are the benefits of mini-batch gradient descent?
What is the significance of a Cost/Loss function?
Explain Learning of a Perceptron.
What are the activation functions?
What is the role of weights and bias?
What is Perceptron? And How does it Work?
Do you think Deep Learning is Better than Machine Learning? If so, why?
Which deep learning algorithm is the best for face detection?
Explain Stochastic Gradient Descent. How is it different from Batch Gradient Descent ?
Explain Batch Gradient Descent.
In a Convolutional Neural Network (CNN), how can you fix the constant validation accuracy?
Explain the difference between a shallow network and a deep network.
What is a tensor in deep learning?
What are the advantages of transfer learning?
Explain transfer learning in the context of deep learning.
What do you mean by hyperparameters in the context of deep learning?
Explain Data Normalisation. What is the need for it?
Explain Forward and Back Propagation in the context of deep learning.
What do you understand about gradient clipping in the context of deep learning?
What do you mean by end-to-end learning?
What are the different types of deep neural networks?
Explain what a deep neural network is.
What are the disadvantages of neural networks?
What are the advantages of neural networks?
What are the applications of deep learning?
Differentiate between AI, Machine Learning and Deep Learning.