parameters of large NLP models15. In this situation, embedding-free approaches like pQRNN16 are a viable alternative. pQRNN uses the projection operation which maps a given input token to a B-bit fingerprint

penalties on the weights during optimization process. To summarise, Keras layer requires below minimum details to create a complete layer.  Shape of the input data  Number of neurons / units in dimension and 2 denotes third dimension MinMaxNorm Constrains weights to be norm between specified minimum and maximum values. from keras.models import Sequential from keras.layers import Activation, Dense used to merge a list of inputs. It supports add(), subtract(), multiply(), average(), maximum(), minimum(), concatenate() and dot() functionalities. Adding a layer It is used to add two layers. Syntax

four trials with four pairs of hyperparameter values. The hyperparameter values which achieve the minimum loss are the winners. Let's start by importing the relevant libraries and creating a random classification from the trial set. Each model is trained for 2000 iterations. At the end of a trial, we record the minimum loss achieved with the associated hyperparameters. search_results = [] for trial_id, (layer_size Loss: 0.11825279891490936 As we can see from the trial results, the last trial #3 achieves the minimum loss value. This exercise demonstrates the essence of HPO which is to perform trials with different

symbol, never exceed a minimum of 24 pixels for screen or 10mm for print. This ensures consistency and legibility of the symbol. Minimum Screen Size: 24px Minimum Print Size: 10mm 5 Brand

decreases fastest if one goes from θ in the direction of the negative gradient of J at θ Find a local minimum of a differentiable function using gradient descent Algorithm 1 Gradient Descent 1: Given a starting decrease for each iteration Usually, SGD has θ approaching the minimum much faster than batch GD SGD may never converge to the minimum, and oscillating may happen A variants: Mini-batch, say pick up

given vector x.""" def quantize(x, x_min, x_max, b): # Clamp x to lie in [x_min, x_max]. x = np.minimum(x, x_max) x = np.maximum(x, x_min) # Compute scale as discussed. scale = get_scale(x_min, x_max floor((x - x_min) / scale) # Clamping the quantized value to be less than (2^b - 1). x_q = np.minimum(x_q, 2**b - 1) # Return x_q as an unsigned integer. 1 Deep Learning with Python by Francois Chollet number of dimensions of that tensor. 1. Given a 32-bit floating-point weight matrix, we can map the minimum weight value xmin to 0, and the maximum weight value xmax to 2b-1 (b is the number of bits of precision

Gradient Descent (GD) method is a first-order iterative optimization algorithm for finding the minimum of a function. If the multi-variable function J(✓) is di↵erentiable in a neighborhood of a point Fig. 2. The colored contours represent the objective function, and GD algorithm converges into the minimum step-by- step. The choice of the step size ↵ actually has a very important influence on the convergence

in [-4, 4]. However quantization linearly assigns precision to all ranges equally based on the minimum and maximum values it observes for . Each quantization bin boundary is denoted by a cross. This a regularizing effect18 due to dropping spurious connections. 18 https://en.wikipedia.org/wiki/Minimum_description_length Apart from the most commonly used magnitude-based pruning, there are other heuristics

gradient descent method has a very slow convergence rate and may take a long while to achieve the minimum. 2. What values of θ did you get after achieving the convergence? 3. Calculate L(θ) in each iteration

decreases fastest if one goes from θ in the direction of the negative gradient of L at θ • Find a local minimum of a differentiable function using gradient descent θj ← θj − α∂L(θ) ∂θj , ∀j where α is so-called