Deep learning has brought significant changes or revolution in the field of machine learning and data science. The concept of a complex neural network (CNN) is the main center of attention for data scientists. It is widely taken because of its advantages in performing next-level machine learning operations. The advantages of deep learning also include the process of clarifying and simplifying issues based on an algorithm due to its utmost flexible and adaptable nature. It is one of the rare procedures which allow the movement of data in independent pathways. Most of the data scientists are viewing this particular medium as an advanced additive and extended way to the existing process of machine learning and utilizing the same for solving complex day to day issues.
Posted Date:- 2022-02-15 13:26:33
What are the unsupervised learning algorithms in Deep learning?
What are the supervised learning algorithms in Deep learning?
What are the prerequisites for starting in Deep Learning?
Why is zero initialization not a good weight initialization process?
What is the use of Deep learning in today's age, and how is it adding data scientists?
How is the transformer architecture better than RNNs in Deep Learning?
What are some of the applications of transfer learning in Deep Learning?
How Does RBM differ from Autoencoders?
What is the meaning of valid padding and same padding in CNN?
Can we initialize the weights of a network to start from zero?
Explain the architecture of an Autoencoder.
what are the different layers of Autoencoders?
Give some real-life examples where autoencoders can be applied.
Explain Autoencoders and it’s uses.
Why is the Leaky ReLU function used in Deep Learning?
Why is mini-batch gradient descent so popular?
What are the variants of gradient descent?
What are some of the limitations of Deep Learning?
What is a Restricted Boltzmann Machine?
What are the types of autoencoders?
What is exploding gradient descent in Deep Learning?
What is a vanishing gradient when using RNNs?
What is an RNN in Deep Learning?
What are the various layers present in a CNN?
What is a computational graph in Deep Learning?
What are some of the advantages of using TensorFlow?
What is the meaning of model capacity in Deep Learning?
What is the meaning of dropout in Deep Learning?
How can hyperparameters be trained in neural networks?
What are hyperparameters in Deep Learning?
What is data normalization in Deep Learning?
What are the steps to be followed to use the gradient descent algorithm?
What is the use of the swish function?
What are some of the Deep Learning frameworks or tools that you have used?
What is the use of the loss function?
What are the steps involved in training a perception in Deep Learning?
Why is Fourier transform used in Deep Learning?
What are activation functions?
What is the meaning of overfitting?
What are some of the most used applications of Deep Learning?
How is Deep Learning better than Machine Learning?
What is the difference between Machine Learning and Deep Learning?