tested against each NGC monthly container release to ensure consistent accuracy and performance over time. ‣ ResNeXt101-32x4d model: This model was introduced in the Aggregated Residual Transformations for language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the BERT: Pre-training of Deep Bidirectional Transformers leverages mixed precision arithmetic by using Tensor Cores on NVIDIA V100 GPUs for 1.3x faster training time while maintaining target accuracy. This model script is available on GitHub and NGC. ‣ Tacotron

recommendations that show up are based on your past interests, what is popular with other users at that time, and so on. If you have seen ‘The Office’ many times over like me, there are chances you might like number-crunching at the heart of deep learning. AlexNet1 was one of the earliest models to rely on Graphics Processing Units (GPUs) for training, which could 1 Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25 (2012): 1097-1105. do linear algebra operations such as multiplying two matrices together

downstream tasks, which gives them a boost in quality, and drastically reduces the training data size and time required. The quality of the embeddings primarily depends on the below two factors: Number of dimensions dbpedia_csv/test.csv 560000 dbpedia_csv/train.csv 70000 dbpedia_csv/test.csv It all looks good! Now, it’s time to put our theory into practice. Even though we are going to use pre-trained embeddings, we will roughly audio, and video domains that are ready-to-deploy. For instance, you should not spend resources and time training your own ResNet model. Instead, you can directly get the model architecture and weights from

basics of deep learning, Keras models, Keras layers, Keras modules and finally conclude with some real-time applications. Audience This tutorial is prepared for professionals who are aspiring to make ................................................................................ 77 14. Keras ― Time Series Prediction using LSTM RNN ................................................................ ........................................................................... 88 16. Keras ― Real Time Prediction using ResNet Model ...................................................................

is, it relies on the momentum of the weights which is an exponentially smoothed estimate of over time. For instance, the momentum of weight at training step is given by: 2 Dettmers, Tim, and Luke Zettlemoyer LeCun, Yann, John Denker, and Sara Solla. "Optimal brain damage." Advances in neural information processing systems 2 (1989). As you can deduce, the parameter changes the influence of the previous value scores, but they will all try to approximate the importance of a given weight at a certain point of time in the training process to minimize the loss function. The better we can estimate this importance

Chapter 2 - Compression Techniques “I have made this longer than usual because I have not had time to make it shorter.” Blaise Pascal In the last chapter, we discussed a few ideas to improve the deep of the simplest approaches towards efficiency is compression to reduce data size. For the longest time in the history of computing, scientists have worked tirelessly towards storing and transmitting information Footprint Metrics Quality Metrics ● Model Size ● Inference Latency on Target Device ● Training Time for Convergence ● Peak RAM Consumption ● Accuracy ● Precision ● Recall ● F1 ● AUC Table 2-1:

similar to the baseline, but does so in fewer epochs. We could ideally save an epoch’s worth of training time by terminating the training early, if we adopt this hypothetical sample efficient model training. effective utilization of the training data. Labeling data is often an expensive process both in terms of time consumption and fiscal expenditure because it involves human labelers looking at each example and the four classes, three of which are the keywords that the device will accept: hello, weather and time. The fourth class (none) indicates the absence of an acceptable keyword in the input signal. Figure

import math import os import random import re import shutil import sys import tarfile import time import zipfile from collections import defaultdict import pandas as pd import requests from IPython 了实现这一点，需要我们对计算进 行矢量化，从而利用线性代数库，而不是在Python中编写开销高昂的for循环。 %matplotlib inline import math import time import numpy as np import torch from d2l import torch as d2l 为了说明矢量化为什么如此重要，我们考虑对向量相加的两种方法。我们实例化两个全为1的10000维向量。 [] self.start() def start(self): """启动计时器""" self.tik = time.time() def stop(self): """停止计时器并将时间记录在列表中""" self.times.append(time.time() - self.tik) return self.times[-1] def avg(self): """返回平均时间"""

on any of the footprint metrics. These techniques might get superseded by other better methods over time, but again our goal is to give you a gentle introduction to this area for you to be able to research compute efficiency since only have to train the model on a small number of examples, saving training time compute too. A Typical Self-Supervised Learning Recipe We can break-down common self-supervised is also used in BERT (Devlin et al.), and other related models like GPT, RoBERTa, T5, etc. At the time of its publishing, BERT beat the state-of-the-art on eleven NLU tasks. A critical point to note is

SACC2017 图像技术的三个核心难点>>小、快、准 小模型 线上速度快 预测准 Frequent remote upgrade CPU-constrained, real-time Cloud processing SACC2017 视觉感知模型 分割 Forward Block Forward Block deconvolution deconvolution convolution SACC2017 图像技术的三个核心难点>>小、快、准 小模型 线上速度快 预测准 Frequent remote upgrade CPU-constrained, real-time Cloud processing SACC2017 图像技术的三个核心难点>>小、快、准 模型 数据 工程 模型缩减 结构演进 SACC2017 单尺度卷积核 多尺度卷积核 视觉感知的三个核心难点>>小、快、准