need to use tokenizer.apply_chat_template() to format your inputs as shown␣ �→below prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are "Content-Type: application/json" - �→d '{ "model": "Qwen/Qwen1.5-7B-Chat", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me something about large language messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me something about large language models."}, ] ) print("Chat response:", chat_response) 1.2.3 下一步 现在，您可以尽情探索

Supervised Learning https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d Massive Unlabeled data Unsupervised Learning https://medium.com/intuitionmachine/predictive-lear com/applied-deep-learning-part-3-autoencoders- 1c083af4d798 https://towardsdatascience.com/a-wizards-guide-to-adversarial-autoencoders-part-1- autoencoder-d9a5f8795af4 How to Train? PCA V.S. Auto-Encoders e.com/a-wizards-guide-to-adversarial-autoencoders-part-2- exploring-latent-space-with-adversarial-2d53a6f8a4f9 Adversarial AutoEncoders ▪ Give more details after GAN https://towardsdatascience.com/

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embedding for each animal, where each feature represents one dimension, we can represent the animals on a 2-D plot. The feature cute can be 2 These feature values are hand-picked based on what we thought was reasonable as intended. For example, in Figure 4-10, we visually verify that the closest points to ‘king’ in 2-D are ‘kingdom’, ‘crown’, ‘archbishop’, and so on. These are all very relevant, and it passes the sanity different words. Figure 4-10: Using the embedding projector tool to visualize the word2vec embeddings in 3-D. Now that we have trained the embeddings, in the next section, let’s learn to use them to improve deep

2016.6 – 2017.5 香港中文大学 Research Fellow 2017.5 – 现在 腾讯优图X-Lab 视觉AI负责人，专家研究员 个人主页：http://xiaoyongshen.me/ Google Scholar： https://scholar.google.com/citations?user=P eMuphgAAAAJ&hl=en 看得更清，看得更懂 目录 1. 夜景增强 ???0 ???????????? ME ????????????????????????→0 Sub-pixel Motion Compensation (SPMC) Layer Our Method 47 ???????????????????????? ???????????? ????????????0 ???????????? ME ????????????????? Detail Fusion Net Our Method 48 ???????????????????????? ???????????? ????????????0 ???????????? ME ????????????????????????→0 SPMC Encoder Decoder ConvL STM ???????????? = ???????????? − 1 ????

Chapter 6 - Advanced Learning Techniques “Tell me and I forget, teach me and I may remember, involve me and I learn.” – Benjamin Franklin This chapter is a continuation of Chapter 3, where we introduced

-step-1- convolution-operation/ Convolution Convolution CNN on feature maps https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ 下一课时 卷积神经网络 Thank You.

后续工作 结合温度预测模型对空调进行节能控制 ⚫ 利用温度预测模型实现强化学习节能控制 • 强化学习探索策略的制定 • 强化学习模拟实验环境 项目数据及源代码地址： http://uee.me/cu9GV THANK YOU momodel.ai

嵌入到Spark计算框架里，方便打通 数据流 实现了一种新的Yarn Applica\on，可 以与TensorFlow灵活整合和功能定制 代码量几百行 代码量几千行 About Me Yuance.Li 15201453364 liyuance@{360

popular with other users at that time, and so on. If you have seen ‘The Office’ many times over like me, there are chances you might like ‘Seinfeld’ too, which might be popular with other users too. If we