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 paper. This model script is available on GitHub. ‣ TransformerXL model: This transformer-based language model has a segment-level recurrence and a novel relative positional encoding. The enhancements

chapter by presenting self-supervised learning which has been instrumental in the success of natural language models like BERT. Self-Supervised learning helps models to quickly achieve impressive quality with We will describe the general principles of Self-Supervised learning which are applicable to both language and vision. We will also demonstrate its efficacy through a colab. Finally, we introduce miscellaneous 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

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

Model quality is an important benchmark to evaluate the performance of a deep learning model. A language translation application that uses a low quality model would struggle with consumer adoption because 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

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()

........................................................................................... 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

NumPy will also be useful! What is PyTorch? ● An machine learning framework in Python. ● Two main features: ○ N-dimensional Tensor computation (like NumPy) on GPUs ○ Automatic differentiation for training torch.Size([2, 3]) 1 2 3 2 3 (dim = 0) Tensors – Common Operations ● Unsqueeze: expand a new dimension >>> x = torch.zeros([2, 3]) >>> x.shape torch.Size([2, 3]) >>> x = x.unsqueeze(1) >>> Load Data torch.nn – Network Layers ● Linear Layer (Fully-connected Layer) nn.Linear(in_features, out_features) Input Tensor * x 32 Output Tensor * x 64 nn.Linear(32, 64) can be any shape (but

components”节点下，比对目前计算机已经安装的显卡驱动“Display Driver”的版本号 “Current Version”和 CUDA 自带的显卡驱动版本号“New Version”，如果“Current Version”大于“New Version”，则需要取消“Display Driver”的勾，如果小于或等于，则 默认勾选即可，如图 1.27 所示。设置完成后即可正常安装。 图 b_current) - y) # 根据梯度下降算法更新 w',b',其中 lr 为学习率 new_b = b_current - (lr * b_gradient) new_w = w_current - (lr * w_gradient) return [new_b, new_w] 4. 梯度更新 在计算出误差函数在?和?处的梯度后，可以根据式(2.1)来更新 PyTorch 基础 14 其中?表示序列信号的数量，?seq表示序列信号在时间维度上的采样点数或步数，?feat表示 每个点的特征长度。 考虑自然语言处理(Natural Language Processing，简称 NLP)中句子的表示，如评价句 子的是否为正面情绪的情感分类任务网络，如图 4.3 所示。为了能够方便字符串被神经网 络处理，一般将单词通过嵌入层(Embedding

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

extended to recurrent neural networks (RNN) by involving feedback connections, which power many natural language applications 2 / 19 Neuron 3 / 19 Neuron (Contd.) • Neuron activated when the correlation between is a suffcient number of training examples (x(i), y(i)) • It can be diffcult to understand the features a neural network has invented; therefore, people refer to neural networks as a black box 8 / 19