Framework containers are no longer tested on Pascal GPU architectures. ‣ Transformer Engine is a library for accelerating Transformer models on NVIDIA GPUs. It includes support for 8-bit floating point introduced in the Aggregated Residual Transformations for Deep Neural Networks paper. It is based on the regular ResNet model, which substitutes 3x3 convolutions in the bottleneck block for 3x3 grouped convolutions Framework containers are no longer tested on Pascal GPU architectures. ‣ Transformer Engine is a library for accelerating Transformer models on NVIDIA GPUs. It includes support for 8-bit floating point

matrix size. As shown in the output below, the sparsified compressed matrix is smaller than the regular compressed matrix by nearly 50%. weights = np.random.normal(size=(100, 100)).astype(np.float32) https://github.com/google/XNNPACK Project: Lightweight model for pet filters application Recall that our regular CNN model in the pet filters project consisted of thirteen convolution blocks and five deconvolution model and wraps the prunable blocks for sparse training using TFMOT (Tensorflow Model Optimization) library. In this case, we prune the 50% of the weights in each prunable block using magnitude-based pruning

Theano, TensorFlow, Caffe, Mxnet etc., Keras is one of the most powerful and easy to use python library, which is built on top of popular deep learning libraries like TensorFlow, Theano, etc., for creating or Cognitive Toolkit (CNTK). Theano is a python library used for fast numerical computation tasks. TensorFlow is the most famous symbolic math library used for creating neural networks and deep learning Linux or Mac)  Python version 3.5 or higher. Python Keras is python based neural network library so python must be installed on your machine. If python is properly installed on your machine, then

Keras: 基于 Python 的深度学习库 Keras: The Python Deep Learning library* Author: Keras-Team Contributor: 万 震 (WAN Zhen) � wanzhenchn � wanzhen@cqu.edu.cn 2018 年 12 月 24 日 *Copyright © 2018 by Keras-Team Chinese Keras Markdown is that it is easy to read locally when learning the Keras Deep Learning Library. For the latest PDF version, please visit https://github.com/wanzhenchn/keras-docs-zh. Thanks for kernel 权值矩阵的正则化函数 (详见regularizer)。 • recurrent_regularizer: 运用到 recurrent_kernel 权值矩阵的正则化函数 (详见 regular- izer)。 • bias_regularizer: 运用到偏置向量的正则化函数 (详见 regularizer)。 • activity_regularizer: 运用到层输出（它的激活值）的正则化函数

up the required libraries, and loading the training and validation sets. We leverage the nlpaug library to perform the augmentations. It provides a simple 5 Maas, Andrew, et al. "Learning word vectors technologies. 2011. mechanism to chain multiple augmentations. It can be replaced with any other library per individual preference. %%capture # We will use nlpaug to augment the text samples. !pip install function. Now, let’s add some text augmentations to the mix and see if that helps. The nlpaug python library offers concise ways to apply sentence, word and character augmentations. We shuffle sentences, substitute

Trademark Policy at 
 Linuxfoundation.org/trademark-usage/ 1 Brand Guidelines PyTorch Symbol Our expression is best communicated when it is supported by the Symbol — a simple graphic that adds intrigue itself. It’s important for the wordmark to always be displayed in the typeface Freight Sans Regular, and to maintain the spacing and proportions shown here. Choose the appropriate lockup depending

lines to code in comparison. • It is easy to debug and understand the code. • Python usage − This library is considered to be Pythonic which smoothly integrates with the Python data science stack. • It functions to choose from • L1, MSE, Cross Entropy …... Model • In PyTorch, a model is represented by a regular Python class that inherits from the Module class. • Two components • __init__(self): it defines modules • 'Sequential' layer modules Dataset • Dataset • In PyTorch, a dataset is represented by a regular Python class that inherits from the Dataset class. You can think of it as a kind of a Python list

test_dataset.shuffle(test_dataset.cardinality()).batch(BATCH_SIZE) We will import the tensorflow_text library so that we can use the BERT model which relies on certain tensorflow ops. import os # tensorflow_text ops used in our model. import tensorflow_text as tf_text Next we will import the tensorflow_hub library so that we can import pre-trained BERT models directly from Tensorflow Hub. import tensorflow_hub also went over a collection of a few other learning techniques that you can incorporate in your regular model training. The goal was to provide an introduction to the broad themes that you can explore

(1) Matlab/Octave does not have a library function for the sigmoid, so you will have to define it yourself. The easiest way to do this is through an inline expression: g = i n l i n e ( ’ 1.0 ./ ( 1

and so on. Step 2: Dataset Preparation and Vectorization The tensorflow vectorization_layer is a regular layer which can be invoked with a model as well as independently. The output of this layer is a vector Convolution28 (DSC) is a two-step convolution designed to reduce the computational complexity of regular convolution. Its output shape is identical to a convolution layer which makes it an attractive choice footprint than a regular convolution network. Howard et. al. demonstrated that their proposed MobileNets29 (DSC model family for mobile vision applications) perform at par with the regular convolution as