A Search Space for n parameters is a n-dimensional region such that a point in such a region is a set of well-defined values for each of those parameters. The parameters can take discrete or continuous hyperparameters to differentiate them from model parameters. The performance of deep learning relies on a set of good hyperparameters. Some of the commonly tuned hyperparameters are the learning rate and the momentum model. HPO performs trials with different sets of hyperparameters using the model as a blackbox. The set which performs the best is chosen for full training. In the next section, we'll discuss various approaches

floatx represent the default data type float32. You can also change it to float16 or float64 using set_floatx() method.  backend denotes the current backend. Suppose, if the file is not created then required information from the data.  Split data: Split the data into training and test data set. Test data will be used to evaluate the prediction of the algorithm / Model (once the machine learn)  Fit the model: The actual learning process will be done in this phase using the training data set.  Predict result for unknown value: Predict the output for the unknown input data (other than

adding only three lines of Python to an existing FP32 (default) script. AMP will select an optimal set of operations to cast to FP16. FP16 operations require 2X reduced memory bandwidth (resulting in a adding only three lines of Python to an existing FP32 (default) script. AMP will select an optimal set of operations to cast to FP16. FP16 operations require 2X reduced memory bandwidth (resulting in a adding only three lines of Python to an existing FP32 (default) script. AMP will select an optimal set of operations to cast to FP16. FP16 operations require 2X reduced memory bandwidth (resulting in a

component x(i) j ∈ {0, 1} (j = 1, · · · , n), and y(i) ∈ {1, · · · , k}. For brevity, we use [k] to denote set {1, 2, · · · k}. Therefore, we have i ∈ [m], j ∈ [n] and y ∈ [k]. In Naive Bayes (NB) model, the feature · · · , Xn = xn | Y = y)P(Y = y) = P(Y = y) n � j=1 P(Xj = xj | Y = y) 5 By now, we have two set of parameters: i) P(Y = y) = pY (y) for ∀y ∈ [k], and ii) P(Xj = xj | Y = y) = pXj|Y (xj | y) for ∀xj pj(xj | y) denotes the posterior probability of Xj = xj given Y = y. 4.2 Problem Formulation Given a set of m training data {x(i), y(i)}i∈[m], the log-likelihood function can be defined by ℓ(Ω) = log m

NB and EM September 27, 2023 3 / 122 Sample Space, Events and Probability A sample space S is the set of all possible outcomes of a (conceptual or physical) random experiment Event A is a subset of the data, but how? Feng Li (SDU) GDA, NB and EM September 27, 2023 33 / 122 Warm Up (Contd.) Given a set of training data D = {x(i), y(i)}i=1,··· ,m The training data are sampled in an i.i.d. manner The 1}x(i)/ m � i=1 1{y(i) = 1} Σ = 1 m m � i=1 (x(i) − µy(i))(x(i) − µy(i))T Proof (see Problem Set 2) Feng Li (SDU) GDA, NB and EM September 27, 2023 50 / 122 Gaussian Discriminant Analysis (Contd

language models."} ], }' 或者您可以按照下面所示的方式，使用 openai Python 包中的 Python 客户端： from openai import OpenAI # Set OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = gguf 的 Qwen 的 GGUF 文件。在第一步中，您需要创建一个名为 Modelfile 的文件。该文件的内容如下所示： FROM qwen1_5-7b-chat-q4_0.gguf # set the temperature to 1 [higher is more creative, lower is more coherent] PARAMETER temperature 0.7 PARAMETER }}<|im_end|> {{ end }}<|im_start|>user {{ .Prompt }}<|im_end|> <|im_start|>assistant {{ .Response }}""" # set the system message (续下页) 10 Chapter 1. 文档 Qwen (接上页) SYSTEM """ You are a helpful assistant. """

achieves a higher accuracy with the same number of labeled training examples. Data Augmentation is a set of techniques which leverage the original training data to generate more training examples without workflows. We start with data augmentation in the next section. Data Augmentation Data Augmentation is a set of dataset manipulation techniques to improve sample and label efficiencies of deep learning models we can kick-off the training process. The train() is simple. It takes the model, training set and validation set as parameters. It also has two hyperparameters: batch_size and epochs. We use a small batch

Step 5. Entire Procedure Load Data Neural Network Training Setup dataset = MyDataset(file) tr_set = DataLoader(dataset, 16, shuffle=True) model = MyModel().to(device) criterion = nn.MSELoss() optimizer model and move to device (cpu/cuda) set loss function set optimizer Neural Network Training Loop for epoch in range(n_epochs): model.train() for x, y in tr_set: optimizer.zero_grad() loss.backward() optimizer.step() iterate n_epochs set model to train mode iterate through the dataloader set gradient to zero move data to device (cpu/cuda) forward pass (compute

and 5 is D2. Bias is of shape [5]. The shapes are arbitrarily chosen for illustration purposes. # Set the seed so that we get the same initialization. np.random.seed(10007) def get_random_matrix(shape): technologies like the fixed-point SIMD instructions which allows data parallelism, the SSE instruction set in x86 architecture, and similar support on ARM processors as well as on specialized DSPs like the performance improvement was the availability of fixed-point SIMD instructions in Intel's SSE4 instruction set which can parallelize Multiply-Accumulate (MAC) operations. 7 Vanhoucke, Vincent, Andrew Senior, and

detect Splitting Train Set Test Set For example 60K 10K test while train train test trade-off Overfitt ing For others judge ▪ Kaggle Train Set Test Set Val Set Unavailable train-val-test train-val-test K-fold cross-validation Train Set Test Set Val Set k-fold cross validation ▪ merge train/val sets ▪ randomly sample 1/k as val set 下一课时 减轻Overfitting Thank You.