2023 16 / 57 Applications of Machine Learning (Contd.) Cancer Diagnosis Given data on expression levels of genes, classify the type of tumor. Discover categories of tumors having different characteristics blood pressure of a patient, etc. To make predictions, we have various inputs, Gene expression levels for predicting tumor type, age and income for predicting amount spent, the features of images with

sparsified weight matrices (zero-d weights are dark) each with 33% sparsity at various granularity levels. It shows the parameter layout for a convolutional layer which receives a 3-channel input. Each individual of the pruned weights again, the regrowth step attempts to align the loss value to the pre-pruning levels. The regrowth step, in some cases, also redistributes the lost weight across layers such that the

configuration left. An intuitive way to think about it is to imagine a multiplayer game with multiple levels where a few best performing players are promoted to the next level until we have a winner. There

subset of training data earlier in the training than the rest. Training examples might have different levels of hardness depending on how informative the features are. 17 Lukasik, Michal, et al. "ICML'20:

with efficient self-attention mechanisms. These ideas tackle the quadratic complexity at various levels. The simplest idea is to chunk the input sequence of length n into blocks of length b where b <<<

tokenizer word2vec Elmo pb ckpt H5 (Keras) RESTful API RPC API Function test Concurrence test Security test Multi class Multi label preprocessor Traditional --->simple Char replacement Synonym

0 through v1.2.1 exposes a Regular Expression Denial of Service (ReDOS) vulnerability. ‣ Known security vulnerabilities: ‣ CVE-2022-32212, CVE-2022-43548, CVE-2023-0286, CVE-2022-32223, CVE-2023-0286 0 through v1.2.1 exposes a Regular Expression Denial of Service (ReDOS) vulnerability. ‣ Known security vulnerabilities: ‣ CVE-2022-25882 for ONNX<1.13.0 PyTorch RN-08516-001_v23.07 | 61 Chapter Tacotron2 inference performance regression of up to 15% for workloads using dynamic input shapes. Security CVEs ‣ CVE-2022-45198 - Pillow before 9.2.0 performs Improper Handling of Highly Compressed GIF

shape), 25, alpha = 1, cmap=cm.Spectral) plt.contour(XX, YY, preds.reshape(XX.shape), levels=[.5], cmap="Greys", vmin=0, vmax=.6) # 绘制散点图，根据标签区分颜色 plt.scatter(X[:, 0], X[:, 1] cmap=cm.Spectral) plt.contour(XX, YY, preds.reshape(XX.shape), levels=[.5], cmap="Greys", vmin=0, vmax=.6) # 绘制正负样本 markers = ['o'

，如 图16.3.6顶部所示。在本例中，我 们保留“3. Configure Instance”（3. 配置实例）、“5. Add Tags”（5. 添加标签）和“6. Configure Security Group”（6. 配置安全组）步骤的默认配置。点击“4.添加存储”并将默认硬盘大小增加到64GB( 图16.3.6中 的红色框标记)。请注意，CUDA本身已经占用了4GB空间。 图16