examples, see: ‣ PyTorch website ‣ PyTorch project This document provides information about the key features, software enhancements and improvements, known issues, and how to run this container. PyTorch RN-08516-001_v23 details, see Deep Learning Frameworks Support Matrix. Key Features and Enhancements This PyTorch release includes the following key features and enhancements. ‣ PyTorch container image version 23.07 GitHub and NGC. ‣ BERT model: Bidirectional Encoder Representations from Transformers (BERT) is a new method of pretraining language representations which obtains state-of-the-art results on a wide array

Convolutional Neural Nets (CNNs) were another important breakthrough that enabled learning spatial features in the input. Recurrent Neural Nets (RNNs) facilitated learning from the sequences and temporal having an algorithmic way to meaningfully represent these inputs using a small number of numerical features, will help us solve tasks related to these inputs. Ideally this representation is such that similar similar representations. We will call this representation an Embedding. An embedding is a vector of features that represent aspects of an input numerically. It must fulfill the following goals: a) To compress

........................................................................................... 1 Features ............................................................................................... learning applications. Features Keras leverages various optimization techniques to make high level neural network API easier and more performant. It supports the following features:  Consistent, simple choose download based on your OS. Create a new conda environment Launch anaconda prompt, this will open base Anaconda environment. Let us create a new conda environment. This process is similar to

layers import Embedding from keras.layers import LSTM model = Sequential() model.add(Embedding(max_features, output_dim=256)) model.add(LSTM(128)) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid')) = Sequential() model.add(Dense(2, input_dim=3, name='dense_1')) # 将被加载 model.add(Dense(10, name='new_dense')) # 将不被加载 # 从第一个模型加载权重；只会影响第一层，dense_1 model.load_weights(fname, by_name=True) 3.3.6.4 处 整数张量，表示将与输入相乘的二进制 dropout 掩层的形状。例如，如果 你的输入尺寸为 (batch_size, timesteps, features)，然后你希望 dropout 掩层在所有 时间步都是一样的，你可以使用 noise_shape=(batch_size, 1, features)。 • seed: 一个作为随机种子的 Python 整数。 参考文献 • Dropout: A Simple

for a new task: 1. Data Efficiency: It relies heavily on labeled data, and hence achieving a high performance on a new task requires a large number of labels. 2. Compute Efficiency: Training for new tasks tasks requires new models to be trained from scratch. For models that share the same domain, it is likely that the first few layers learn similar features. Hence training new models from scratch for these across specific tasks in that domain. They can be adapted to solve the target task by: 1. Adding a new prediction head to the pre-trained model which can translate the general representations to the task

and identically distributed, i.i.d.)。样本有时也叫做数据点 （data point）或者数据实例（data instance），通常每个样本由一组称为特征（features，或协变量（covariates）） 的属性组成。机器学习模型会根据这些属性进行预测。在上面的监督学习问题中，要预测的是一个特殊的属 性，它被称为标签（label，或目标（target））。 true_b = 4.2 features, labels = synthetic_data(true_w, true_b, 1000) 47 https://discuss.d2l.ai/t/1775 3.2. 线性回归的从零开始实现 95 注意，features中的每一行都包含一个二维数据样本，labels中的每一行都包含一维标签值（一个标量）。 print('features:', features[0] '\nlabel:', labels[0]) features: tensor([1.4632, 0.5511]) label: tensor([5.2498]) 通过生成第二个特征features[:, 1]和labels的散点图，可以直观观察到两者之间的线性关系。 d2l.set_figsize() d2l.plt.scatter(features[:, (1)].detach().numpy()

mapping data to higher dimensions where it exhibits linear patterns Apply the linear model in the new input space Mapping is equivalent to changing the feature representation Feng Li (SDU) SVM December each example as x → {x, x2} Each example now has two features (“derived” from the old representa- tion) Data now becomes linearly separable in the new representation Feng Li (SDU) SVM December 28, 2021 {x2 1, √ 2x1x2, x2 2} Each example now has three features (“derived” from the old represen- tation) Data now becomes linearly separable in the new representation Feng Li (SDU) SVM December 28, 2021

feature space where it exhibits linear patterns, we can employ the linear classification model in the new feature space. 8 Figure 3: Non-linear data v.s. linear classifier We take the following binary classification {x, x2}, such that each sample now has two features (“derived” from the old representation). As shown in Fig. 4 (b), data become linearly separable in the new higher-dimensional feature space (a) (b) x1xn, · · · , xn−1xn} where each new feature uses a pair of the original features. It can be observed that, the feature mapping leads to a huge number number of new features, such that i) computing the mapping

regression model using gradient descent algorithm, based on which, we can predict the height given a new age value. In Matlab/Octave, you can load the training set using the commands x = load ( ’ ex1x . with n = 1 features ( in addition to the usual x0 = 1, so x ∈ R2 ). If you’re using Mat- lab/Octave, run the following commands to plot your training set (and label the axes): figure % open a new f i g u training data according to θ. The plotting commands will look something like this: hold on % Plot new data without c l e a r i n g old p l o t plot ( x ( : , 2 ) , x∗ theta , ’− ’ ) % remember that x

performance threshold (in terms of accuracy, precision, recall or other performance metrics). We designate a new model training setup to be more sample efficient, if it achieves similar or better performance with the highest possible accuracy with the original training costs: We can let the model train with the new learning techniques. In many cases, this will improve performance. Let’s say that the 300 KB model work on the model. We use a pre-trained ResNet50 model with the top (softmax) layer replaced with a new softmax layer with 102 units (one unit for each class). Additionally, we add the recommended resnet