https://venturebeat.com/2019/01/08/baidu-announces-apollo-3-5-and-apollo-enterprise-says-it-has- over-130-partners/ 预览版202112 1.5 深度学习框架 13 是一个基于 Python 语言、定位底层运算的计算库，Theano 同时支持 GPU 和 CPU 运 算。由于 Theano

planned post 2.0 No support yet Supported Estimator API Limited Support Not supported Limited Support Limited Support Limited Support Limited Support SavedModel：生产级 TensorFlow 模型格式 TensorFlow 2 vs TensorFlow

in NLP, vision, speech or other domains. However, owing to their incremental nature, they offer limited gains. Sometimes, it can be rewarding to go back to the drawing board and experiment with another Hence the name Bag of Words for this family of model architectures. In practice, you need not be limited to this architecture for solving the CBOW (or Skipgram) task. 12 The Illustrated Word2vec - https://jalammar IoT devices, etc.), where transmitting the model to the device is limited by the user’s bandwidth, and the memory available might be limited too. Let’s see what our options are: 1. The embedding table is

involvement, and for that reason can be expensive and slow. Many organizations and AI labs can only do limited data labeling because it is expensive. Moreover, the labelers need to be trained for the task such humpback whales from the pictures of their flukes2. The primary challenge with that dataset is the limited number of sample pictures for each whale. The dataset contains over 5000 individuals with more than weights and training checkpoints. A sample text is represented with a sequence of words. We have limited the sequence length to 500 words. If a sample is longer, it is truncated. A shorter sample is padded

practitioners to choose specific values to try out. The maximum choices for such parameters are limited to the chosen set. Alternatively, we can specify ranges for such hyperparameters and let the search be and for 7 trials, one possible set can be . The total number of trials in Random Search are limited by the available computational budget. They can be increased as more resources become available or that have sufficient compute resources at their disposal. However, on mobile and edge devices with limited compute capabilities, inference latencies become an important concern. Hence, the reward signal needed

数据增强 主讲人：龙良曲 Big Data ▪ The key to prevent Overfitting Sample more data? Limited Data ▪ Small network capacity ▪ Regularization ▪ Data argumentation Recap Data argumentation ▪ Flip ▪ Rotate ▪ Random Move & Crop ▪ GAN https://medium.com/nanonets/how-to-use-deep-learning-when-you-have-limited-data- part-2-data-augmentation-c26971dc8ced Flip Rotate Rotate Scale Crop Part Noise ▪ Data

available technology. This creates an interesting problem, where the spread of these models is rate-limited by their efficiency. While efficiency can be an overloaded term, let us investigate two primary the prediction faster. Similarly, if you are training a large model from scratch on either with limited or costly training resources, developing models that are designed for Training Efficiency would help

from scratch for every slightly different task is not efficient either. In many cases we might be limited by our training compute budget, so this approach is a non-starter. While techniques like data-augmentation using just the labeled data. However, with such a general model our hope is that we can use these limited number of labeled examples for fine-tuning since the model already knows the general concepts about

error (i.e., the cost function) with respect to one training sample only. Hence, it entails very limited cost. We summarize the SGD method in Algorithm 2. In each iteration, we first randomly shu✏e the

instead of underexposed photos, and contains a small number of underexposed images that cover limited lighting conditions. Our Dataset Quantitative Comparison: Our Dataset Method PSNR SSIM HDRNet