It's a deep learning procedure in which a model is fed raw data and the entire data is trained at the same time to create the desired result with no intermediate steps. It is a deep learning method in which all of the different steps are trained simultaneously rather than sequentially. End-to-end learning has the advantage of eliminating the requirement for implicit feature engineering, which usually results in lower bias. Driverless automobiles are an excellent example that you may use in your end-to-end learning content. They are guided by human input and are programmed to learn and interpret information automatically using a CNN to fulfill tasks. Another good example is the generation of a written transcript (output) from a recorded audio clip (input). The model here skips all of the steps in the middle, focusing instead on the fact that it can manage the entire sequence of steps and tasks.
Posted Date:- 2022-02-15 11:12:21
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.