proceeding with the various types of concepts given in this tutorial, we assume that the readers have basic understanding of deep learning framework. In addition to this, it will be very helpful, if the readers ........................................................................................... 26 Basic Concept of Layers ............................................................................... about how to install Keras on your machine. Before moving to installation, let us go through the basic requirements of Keras. Prerequisites You must satisfy the following requirements:  Any kind

what we learnt for quantizing deep learning models. Looking Under the Hood As we know, one of the basic neural network operation is as follows: f(X; W, b) = σ(XW + b) Here, X, W and b are tensors (mathematical sounds challenging because the human handwriting varies from person-to-person. However, there is some basic structure in handwritten digits that a neural network should be able to learn. MNIST (Modified NIST) parameters. The fit() method also prints out the training progress per epoch as shown below. def train_basic_model(): model = get_compiled_model() model_history = model.fit( train_x, train_y, batch_size=128

following: ‣ Ubuntu 16.04 including Python 3.6 environment ‣ NVIDIA CUDA 10.0.130 including CUDA ® Basic Linear Algebra Subroutines library ™ (cuBLAS) 10.0.130 ‣ NVIDIA CUDA ® Deep Neural Network library following: ‣ Ubuntu 16.04 including Python 3.6 environment ‣ NVIDIA CUDA 10.0.130 including CUDA ® Basic Linear Algebra Subroutines library ™ (cuBLAS) 10.0.130 ‣ NVIDIA CUDA ® Deep Neural Network library following: ‣ Ubuntu 16.04 including Python 3.6 environment ‣ NVIDIA CUDA 10.0.130 including CUDA ® Basic Linear Algebra Subroutines library ™ (cuBLAS) 10.0.130 ‣ NVIDIA CUDA ® Deep Neural Network library

255 0 MNIST 手写体数字介绍 下载和读取 MNIST 数据集 一个曾广泛使用（如 chapter-2/basic-model.ipynb），如今被废弃的（deprecated）方法： 下载和读取 MNIST 数据集 一个曾广泛使用（如 chapter-2/basic-model.ipynb），如今被废弃的（deprecated）方法： tf.contrib.learn 模块已被废弃

1 About the Course 2 Machine Learning: What and Why? 3 Categories of Machine Learning 4 Some Basic Concepts of Machine Learning Feng Li (SDU) Overview September 6, 2023 2 / 57 Instructor Prof. Feng Constrained Clustering (Contd.) Feng Li (SDU) Overview September 6, 2023 45 / 57 Active Learning Basic idea: Traditional supervised learning algorithms passively accept training data. Instead, query for

must show a child before they can learn to identify them with high accuracy. All cups have the same basic shape. One possible way to teach a child is to look at the same cup from different angles and rotations that involve large text samples, such as text summarization, spam filtering, resume filtering. The basic idea is that shuffling sentences, paragraphs or sections in a text must preserve the original meaning

openai.com/generative-models/ ▪ Add/minus/multiply/divide ▪ Matmul ▪ Pow ▪ Sqrt/rsqrt ▪ Round basic matmul ▪ Torch.mm ▪ only for 2d ▪ Torch.matmul ▪ @ An example >2d tensor matmul? Power Exp

链式法则 主讲人：龙良曲 Derivative Rules Basic Rule ▪ ? + ? ▪ ? − ? Product rule ▪ ?? ′ = ?′? + ??′ ▪ ?4′ = ?2 ∗ ?2 ′ = 2? ∗ ?2 + ?2 ∗ 2? = 4?3 Quotient Rule ▪ ? ? = ?′?+??′ ?2 ▪ e.g. Softmax Chain

29 3.Scikit-learn案例 见Jupyter notebook 代码 30 参考文献 1. https://scikit-learn.org/stable/tutorial/basic/tutorial.html ，scikit-learn (sklearn) 官方文档 2. https://sklearn.apachecn.org/ ，scikit-learn (sklearn)

clustering can handle non-convex Feng Li (SDU) K-Means December 28, 2021 26 / 46 Kernel K-Means Basic idea: Replace the Euclidean distance/similarity computations in K-means by the kernelized versions