Square, Netflix, Huawei and Uber are currently using Keras. This tutorial walks through the installation of Keras, basics of deep learning, Keras models, Keras layers, Keras modules and finally conclude .............................................................................. 1 2. Keras ― Installation ............................................................................................. .. 3 Keras Installation Steps ................................................................................................................................... 3 Keras Installation Using Python

Getting Started Guide. ‣ For non-DGX users, see NVIDIA ® GPU Cloud ™ (NGC) container registry installation documentation based on your platform. ‣ Ensure that you have access and can log in to the NGC information, refer to the nvidia-docker documentation. Note: Starting in Docker 19.03, complete the steps below. The method implemented in your system depends on the DGX OS version that you installed (for browser or by analyzing text reports. DL Prof is available on NGC or through a Python PIP wheel installation. ‣ The TensorCore example models are no longer provided in the core PyTorch container (previously

username . Since /rwproject/kdd-db/ is a remote folder, it may take several minutes for the installation to finish. 3. Add the code that initializes Miniconda to your shell initialization script. environment) or a new environment. If you want to install PyTorch to the default environment, skip Steps 1. 1. Create a new conda environment. pytorch is the name of the environment to be created. You

class account. • Miniconda is highly recommended, because: • It lets you manage your own Python installation • It installs locally; no admin privileges required • It’s lightweight and fits within your disk com/pytorch/pytorch • Good Blogs: (with examples and code) • https://lelon.io/blog/2018/02/08/pytorch-with-baby-steps • https://www.tutorialspoint.com/pytorch/index.htm • https://github.com/hunkim/PyTorchZeroToAll •

模型文档。 或 者， 你 可 以 编 写 一 个 生 成 批 处 理 训 练 数 据 的 生 成 器， 然 后 使 用 model.fit_generator(data_generator，steps_per_epoch，epochs) 方法。 你可以在 CIFAR10 example 中找到实践代码。 3.3.10 在验证集的误差不再下降时，如何中断训练？ 你可以使用 EarlyStopping validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None) 以固定数量的轮次（数据集上的迭代）训练模型。 参数 • x: 训练数据的 Numpy 数组。如果模型中的输入层被命名，你也可以传递一个字典，将输 模型 43 • shuffle: 布尔值（是否在每轮迭代之前混洗数据）或者字符串 (batch)。batch 是处理 HDF5 数据限制的特殊选项，它对一个 batch 内部的数据进行混洗。当 steps_per_epoch 非 None 时，这个参数无效。 • class_weight: 可选的字典，用来映射类索引（整数）到权重（浮点）值，用于加权损失函 数（仅在训练期间）。这可能有助于告

64，通过对整个序列预测的计算，让我们更仔细地看一下k步预测的困难。 max_steps = 64 features = torch.zeros((T - tau - max_steps + 1, tau + max_steps)) # 列i（isteps+1） for i in range(tau): features[: features[:, i] = x[i: i + T - tau - max_steps + 1] # 列i（i>=tau）是来自（i-tau+1）步的预测，其时间步从（i）到（i+T-tau-max_steps+1） for i in range(tau, tau + max_steps): (continues on next page) 296 8. 循环神经网络 (continued from previous i - tau:i]).reshape(-1) steps = (1, 4, 16, 64) d2l.plot([time[tau + i - 1: T - max_steps + i] for i in steps], [features[:, (tau + i - 1)].detach().numpy() for i in steps], 'time', 'x', legend=[f'{i}-step

step approach for pruning. The three steps are: Train Connectivity, Prune Connections, and Train Weights. The algorithm in figure 5-2 is based on these three steps. Their approach is called iterative pruning Compute the new centroids by computing the mean of all the points assigned to each cluster. 4. Run steps (2) & (3) until convergence. Notice that this algorithm’s runtime is not deterministic and depends want to create (num_clusters), the number of steps we want to run this algorithm for, and finally the learning rate. def get_centroids(x, num_clusters, num_steps=10, learning_rate=1e-3, verbose=1): """Get

--num_train_epochs: the number of training epochs. • --gradient_accumulation_steps: the number of gradient accumulation steps. • --per_device_train_batch_size: the batch size per GPU for training, and per_device_train_batch_size × number_of_gpus × gradient_accumulation_steps. • --learning_rate: the learning rate. • --warmup_steps: the number of warmup steps. • --lr_scheduler_type: the type of learning rate scheduler --warmup_steps 100 \ --weight_decay 0.1 \ --per_device_train_batch_size 4 \ --gradient_accumulation_steps 4 \ --ddp_timeout 9000 \ --learning_rate 5e-6 \ --lr_scheduler_type cosine \ --logging_steps 1 \ --cutoff_len

as population) and trains them to convergence. Each trial is trained for a predetermined number of steps with random values for the hyperparameters. Then, every trial’s weights and hyperparameters are replaced next action and so on. We can design a recurrent model with a fixed or a variable number of time steps. Figure 7-5 shows a general architecture of the NAS recurrent model. The time step at takes the output units stacked (vertical stack) on top of each other to learn complex relationships between the time steps. Zoph et. al. formulated the architectural search as an expectation maximization problem. Given a

as introduced in chapter 3 do help us achieve better quality with fewer labels and fewer training steps required for convergence, they do not alleviate the concerns completely. What should we do to get Self-Supervised Learning Recipe We can break-down common self-supervised learning into two broad steps: 1. Pre-training: This step teaches the model about the world it is operating in (language, vision task is, given two sentences and , predict if follows . Figure 6-3: Pre-training and Fine-tuning steps for BERT. Source: Develin et al. For BERT, the pre-training loss is the mean of the losses for the