Illustrated Word2vec - https://jalammar.github.io/illustrated-word2vec/ The nifty embedding projector tool visualizes embeddings in three dimensions and enables to see which embeddings lie close to a given the sanity check. You can further play with this tool to visualize the embeddings for different words. Figure 4-10: Using the embedding projector tool to visualize the word2vec embeddings in 3-D. Now you as an exercise. Tell us how well it works! Summary This chapter was focussed on two different sets of architectures. The first set of architectures which includes embeddings and attention leverage

this approach, we can process huge amount of features, which makes deep learning a very powerful tool. Deep learning algorithms are also useful for the analysis of unstructured data. Let us go through self.output_dim = output_dim super(MyCustomLayer, self).__init__(**kwargs) Here,  Line 2 sets the output dimension.  Line 3 calls the base or super layer’s init function. Step 4: Implement

The oxford_flowers102 dataset contains 1020 labeled examples each in the training and the validation sets. It is a small sample to train a good quality model. So, we use a pre-trained ResNet50 model and fine The code is available here as a Jupyter notebook for you to experiment. The following code snippet sets up the modules, functions and variables that will be used later on. It initializes the Natural Language project, we start with setting up the required libraries, and loading the training and validation sets. We leverage the nlpaug library to perform the augmentations. It provides a simple 5 Maas, Andrew

observations X = {x1, x2, · · · , xN} (xi ∈ RD), partition the N observations into K sets (K ≤ N) {Ck}k=1,··· ,K such that the sets minimize the within-cluster sum of squares: arg min {Ck} K � i=1 � x∈Ci

next section. A Mental Model of Efficient Deep Learning Before we dive deeper, let’s visualize two sets of closely connected metrics that we care about. First, we have quality metrics like accuracy, precision network. Avoiding over-parameterization further helps in making the networks more robust. Within these sets of techniques, we would look at layers and architectures that have been designed specifically with

capable of natural language understanding, text generation, vision understanding, audio understanding, tool use, role play, playing as AI agent, etc. 最新版本 Qwen1.5 有以下特点： • 6 种模型规模，包括 0.5B、1.8B、4B、7B、14B 和 { "from": "gpt", "value": "model response" } ], "system": "system prompt (optional)", "tools": "tool description (optional)" } ] 2. 在 data/dataset_info.json 文件中提供您的数据集定义，并采用以下格式： 1.12. 有监督微调 35 Qwen parse from qwen_agent.agents import Assistant from qwen_agent.tools.base import BaseTool, register_tool llm_cfg = { # Use the model service provided by DashScope: 'model': 'qwen-max', 'model_server':

choosing values for hyperparameters that lead to an optimal 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 now ready to start the search. The search() method of tuner takes the training and the validation sets to run the search. tds = train_ds.batch(32) vds = val_ds.batch(256) tuner.search(tds, validation_data=vds)

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.

Expectation-Maximization Algorithm We hereby look at Expectation-Maximization (EM) algorithm. 6.1 Convex Sets and Convex Functions A set C is convex if the line segment between any two points in C lies in C

j=1 pj(xj | y) Feng Li (SDU) GDA, NB and EM September 27, 2023 65 / 122 Naive Bayes (Contd.) Two sets of parameters (denoted by Ω) Probability mass function of Y p(y) = P(Y = y) where ∀y ∈ {0, 1} Conditional