- 1 Is backpropagation deep learning?
- 2 What is complexity and its types?
- 3 How do we calculate complexity?
- 4 How to determine the computational complexity of a forward pass?
- 5 What is the computational complexity of a neural network?
- 6 How does forward propagation work in a neural network?
- 7 Which is the best description of a convolutional neural network?
Is backpropagation deep learning?
In this context, proper training of a Neural Network is the most important aspect of making a reliable model. This training is usually associated with the term “Back-propagation”, which is highly vague to most people getting into Deep Learning.
What is complexity and its types?
In general, the amount of resources (or cost) that an algorithm requires in order to return the expected result is called computational complexity or just complexity. The complexity of an algorithm can be measured in terms of time complexity and/or space complexity.
How do we calculate complexity?
To calculate Big O, there are five steps you should follow:
- Break your algorithm/function into individual operations.
- Calculate the Big O of each operation.
- Add up the Big O of each operation together.
- Remove the constants.
- Find the highest order term — this will be what we consider the Big O of our algorithm/function.
How to determine the computational complexity of a forward pass?
How do I determine the computational complexity (big-O notation) of the forward pass of a convolutional neural network? Let’s assume for simplicity that we use zero-padding such that the input size and the output size are the same. Thanks for contributing an answer to Artificial Intelligence Stack Exchange! Please be sure to answer the question.
What is the computational complexity of a neural network?
This essay assumes familiarity with analytical complexity analysis of algorithms, and hereunder big-O notation. If you need a recap, you should read the essay on computational complexity before continuing. Looking at inference part of a feed forward neural network, we have forward propagation.
How does forward propagation work in a neural network?
Forward propagation sequentially calculates and stores intermediate variables within the computational graph defined by the neural network. It proceeds from the input to the output layer. Backpropagation sequentially calculates and stores the gradients of intermediate variables and parameters within the neural network in the reversed order.
Which is the best description of a convolutional neural network?
Convolutional Neural Networks 6.1. From Fully-Connected Layers to Convolutions 6.2. Convolutions for Images 6.3. Padding and Stride 6.4. Multiple Input and Multiple Output Channels 6.5. Pooling 6.6. Convolutional Neural Networks (LeNet) 7. Modern Convolutional Neural Networks 7.1. Deep Convolutional Neural Networks (AlexNet) 7.2.