max_value represent the upper bound  axis represent the dimension in which the constraint to be applied. e.g. in Shape (2,3,4) axis 0 denotes first dimension, 1 denotes second dimension and 2 denotes kernel_constraint=my_constrain)) where, rate represent the rate at which the weight constrain is applied. Regularizers In machine learning, regularizers are used in the optimization phase. It applies kernel_regularizer represents the regularizer function to be applied to the kernel weights matrix.  bias_regularizer represents the regularizer function to be applied to the bias vector.  activity_regularizer

single transformation on every sample results in a dataset 2x the original size. Two transformations applied separately result in a dataset 3x the original size. Can we apply N transformations to create a dataset computations. Two transformations would require 2x100x100x3 computations. When the transformations are applied during the training process, it invariably increases the model training time. A transformation also random nature of the transformation implies that a value in range [-.1, .1] is chosen randomly and applied to the sample image. The horizontal flip transformation leverages the symmetric nature of flowers

shape of the result of the operation (XW + b) is [batch size, D2]. σ is a nonlinear function that is applied element-wise to the result of (XW + b). Some examples of the nonlinear functions are ReLU (ReLU(x) fixed-point value where the latter requires a lesser number of bits. 3. This process can also be applied to signed b-bit fixed-point integers, where the output values will be in the range [- , ]. One of in the deep learning field. We will use it to demonstrate how the quantization techniques can be applied in a practical setting by leveraging the built-in support for such technologies in the real world

using smaller datasets, early stopping or low resolution inputs etc. Early Stopping can even be applied with the HyperBand to terminate the runs sooner if they do not show improvements for a number of addition to defining a smaller search space for architecture design. AmoebaNet, on the other hand, applied evolutionary search to NASNet search space to evolve novel cell configurations. It is exciting to an embedding table to transform it to hidden_size dimensions of the RNN cell. A softmax layer is applied to the cell outputs to convert cell outputs to the probabilities of choosing an element in the state

discussed generic techniques which are agnostic to the model architecture. These techniques can be applied in NLP, vision, speech or other domains. However, owing to their incremental nature, they offer limited which are used to compute the query, key and value matrices for input sequences. Then, a softmax is applied to the scaled dot product of query and key matrices to obtain a score matrix (figure 4-16). Finally for Pets Popular social media applications like Instagram or Snapchat have filters which can be applied over photos. For example, a mustache filter adds mustache to the faces in a photo. Have you ever

32-bit floating point values to 8-bit unsigned / signed integers). Quantization can generally be applied to any network which has a weight matrix. It can often help reduce the model size 2 - 8x, while also the scarcity of labeled data during training. It is a collection of transformations that can be applied on the given input such that it is trivial to compute the label for the transformed input. For example

eighteenth annual ACM-SIAM symposium on Discrete algorithms (SODA '07). Society for Industrial and Applied Mathematics, USA, 1027–1035. with tf.GradientTape() as tape: loss = get_clustering_loss(x_var, a dummy dense fully-connected layer Now that we have looked at how to compress a given tensor, applied it to the Mars Rover problem, wouldn’t it be great if we can also use clustering to compress a dense

data ▪ Compression, denoising, super-resolution … Auto-Encoders https://towardsdatascience.com/applied-deep-learning-part-3-autoencoders- 1c083af4d798 https://towardsdatascience.com/a-wizards-guide-t

to perform this transformation, since both gradient ascent algorithm and Newton’s method can be applied to resolve maximization problems. 2 One approach to minimize the above objective function is gradient

? log 1 − ℎ ? ? + ? 2? ෍ ?=1 ? ?? 2 22 参考文献 [1] HOSMER D W, LEMESHOW S, STURDIVANT R X. Applied logistic regression[M]. New Jersey: Wiley New York.2000. [2] Andrew Ng. Machine Learning[EB/OL]