data for aligning to human preferences. Qwen is capable of natural language understanding, text generation, vision understanding, audio understanding, tool use, role play, playing as AI agent, etc. 最新版本 AutoTokenizer device = "cuda" # the device to load the model onto # Now you do not need to add "trust_remote_code=True" model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat", torch_dtype="auto", device_map="auto" "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) # Directly use

models. Over the years, a wide range of techniques have been developed to facilitate input data generation and transformation. These techniques help to overcome dataset shortcomings like: small size, skewed categories: label invariant transformations, label mixing transformations and synthetic sample generation. The label invariant techniques transform the input samples. The transformed samples are labeled predict a ‘dog’ with a probability of 30% and a ‘hamster’ with a probability of 70%. The sample generation techniques use models to generate samples for labels. Consider a training sample for English to

abstract concept). Here is a quick recipe to train embedding-based models: 1. Embedding Table Generation: Generate the embeddings for the inputs using machine learning algorithms of your choice. 2. Embedding 4-4: A high-level visualization of the embedding-based model training lifecycle. We start with generation of the embedding table, followed by looking up the embeddings for the inputs. Finally, we train train a model that takes the embeddings as input. In our petting zoo example: ● Embedding Table Generation: We have generated the embedding table. ● Embedding Lookup: For each input example, we will look

sequence of tokens, and the output is a sequence of tokens too). It excels in natural language generation and hence has been 8 BERT model on Tensorflow-Hub: https://tfhub.dev/tensorflow/bert_en_uncas deployment of such models is the GitHub’s Copilot software9 where GPT-3 is used for auto-completing code snippets with an IDE. End-users can also use GPT-3 API10 to build their own applications. Given the Project: Using Pre-trained Language Models for News Classification That was a lot of talk without any code. Our overarching goal with self-supervised learning is to be more efficient in the number of labels

 Core Layers  Convolution Layers  Pooling Layers  Recurrent Layers A simple python code to represent a neural network model using sequential model is as follows: from keras.models import your root directory under .keras/keras.json file. Keras backend module can be imported using below code: >>> from keras import backend as k If we are using default backend TensorFlow, then the below call and compute_output_shape completes the creating a customized layer. The final and complete code is as follows: from keras import backend as K from keras.layers import Layer Keras

作为序列决策过程进行优化：增强学习 Milica Gašić (2014) 语言生成 Natural Language Generation (NLG) • 把结构化的系统动作翻译成人类的语言 Steve Young (2016) 语言生成 Natural Language Generation (NLG) • 把结构化的系统动作翻译成人类的语言 • Semantically Conditioned Conditioned LSTM (SC-LSTM) Tsung-Hsien Wen (2016) 语言生成 Natural Language Generation (NLG) • 把结构化的系统动作翻译成人类的语言 • Semantically Conditioned LSTM (SC-LSTM) Tsung-Hsien Wen (2016) Task-Bot: 其他框架 • Microsoft: 让产生的答复与之前的不同 • 语义要连贯 • 加入互信息：同时考虑从answer到question的概率 Deep Reinforcement Learning for Dialogue Generation 闲聊机器人：其他因素 • 小心你的训练数据 • 如何引入上下文信息 • 如何加入外部信息 • 如何产生个性化答复 总结：三个Bot框架 • IR-Bot（成熟度： ） • 基于检索/排序的流程，历史悠久，技术成熟

Applications HD video generation from low-res sources Motivation 35 Old and Fundamental Several decades ago [Huang et al, 1984] → near recent Many Applications HD video generation from low-res sources and Fundamental Several decades ago [Huang et al, 1984] → near recent Many Applications HD video generation from low-res sources Video enhancement with details Text/object recognition in surveillance videos

Capital One “A highly scalable solution, it also offers potential to speed time to market for a new generation of voice and text interactions such as our recently launched Capital One skill for Alexa.” “As

CrossEntropy SmoothL1 DiceLoss Contrasive RCNNHead MaskHead SeqHead Vit Swin Retrieval Image Generation Video Caption EasyVision: 图像视频算法库 Bert TextInput Optim izer 性能优越:  分布式存储  分布式查询 功能完备: 

果，具有时间维度信息的 3D 视频理解任务受到越来越多的关注。常见的视频理解任务有 视频分类、行为检测、视频主体抽取等。常用的模型有 C3D、TSN、DOVF、TS_LSTM 等。 图片生成(Image Generation) 是指通过学习真实图片的分布，并从学习到的分布中采样 而获得逼真度较高的生成图片。目前常见的生成模型有 VAE 系列、GAN 系列等。其中 GAN 系列算法近年来取得了巨大的进展，最新 GAN Anaconda to my PATH environment variable”一项，这样可以通过命令行方式调用 Anaconda 程序。如图 1.23 所示，安装程序 询问是否连带安装 VS Code 软件，选择 Skip 即可。整个安装流程约持续 5 分钟，具体时间 预览版202112 第 1 章 人工智能绪论 18 需依据计算机性能而定。 图 1.22 Anaconda 语言编写程序的方式非常多，可以使用 ipython 或者 ipython notebook 方式 交互式编写代码，也可以利用 Sublime Text、PyCharm 和 VS Code 等综合 IDE 开发中大型 项目。本书推荐使用 PyCharm 编写和调试，使用 VS Code 交互式开发，这两者都可以免费 使用，用户自行下载安装，并配置好 Python 解释器即可。限于篇幅，这里不再赘述。 预览版202112