Contents
Is backpropagation slower than forward propagation?
We see that the learning phase (backpropagation) is slower than the inference phase (forward propagation). This is even more pronounced by the fact that gradient descent often has to be repeated many times.
What is the difference between feedforward and backpropagation processes?
Backpropagation is algorithm to train (adjust weight) of neural network. Input for backpropagation is output_vector, target_output_vector, output is adjusted_weight_vector. Feed-forward is algorithm to calculate output vector from input vector. Input for feed-forward is input_vector, output is output_vector.
What is the difference between backpropagation and forward propagation?
Forward Propagation is the way to move from the Input layer (left) to the Output layer (right) in the neural network. The process of moving from the right to left i.e backward from the Output to the Input layer is called the Backward Propagation.
What is the order of algorithm?
In general the order of an algorithm translates to the efficiency of an algorithm. Therefore, we introduce the concept of the order of an algorithm and utilize this concept to provide a qualitative measure of an algorithm’s performance. To do this we must introduce a suitable model to explain these concepts.
How is the time complexity of a neural network calculated?
First thing to remember is time-complexity is calculated for an algorithm. An algorithm takes an input and produces an output. Now in case of neural networks, your time complexity depends on what you are taking as input. Case 1: Input is just the dataset. Architecture and hyperparameters are fixed in the algorithm.
Why do we separate training and inference phases of neural networks?
In order to motivate why we separate the training and inference phases of neural networks, it can be useful to analyse the computational complexity. This essay assumes familiarity with analytical complexity analysis of algorithms, and hereunder big-O notation.
Why are learning rates important for neural networks?
Setting good learning rates for different phases of training a neural network is critical for convergence as well as to reduce training time. ( Image source) Learning rates are perhaps the most important hyper-parameters to tune for training neural networks.
How to reduce complexity in training and inference?
Thus, complexity reduction is obtained for both training and inference and can be quantified by looking at the density of weights of the sparse network relative to FC. For example, the network below has ρ₁ = 25% density in junction 1, ρ₂ = 50% in junction 2, and ρ net = 33% overall density in the whole network.