training data is an expensive undertaking. Factoring in the costs of training human labelers on a given task, and then making sure that the labels are reliable, human labeling gets very expensive very quickly Even after that it is likely that the model might not be able to capture the intricacies of your task well. Self-Supervised learning helps to significantly improve the quality you can achieve while retaining when it comes to training a model for a new task: 1. Data Efficiency: It relies heavily on labeled data, and hence achieving a high performance on a new task requires a large number of labels. 2. Compute

rewarding to go back to the drawing board and experiment with another architecture that better suits the task. As an analogy, when renovating a house to improve the lighting, it is possible to repaint the walls https://en.wikipedia.org/wiki/Support-vector_machine 3. Train the model: Train the model for the task at hand5 with the embeddings as input. Refer to Figure 4-4 that describes the three steps visually single fully connected layer followed by a softmax activation, since it is a binary classification task. An important caveat is that the model quality naturally depends on the quality of the embedding

run this container. PyTorch RN-08516-001_v23.07 | 2 Chapter 2. Pulling A Container About this task Before you can pull a container from the NGC container registry: ‣ Install Docker. ‣ For NVIDIA command as explained in Running A Container and specify the registry, repository, and tags. About this task On a system with GPU support for NGC containers, when you run a container, the following occurs: NVIDIA GPU Cloud (NGC). ‣ Mask R-CNN model. Mask R-CNN is a convolution based neural network for the task of object instance segmentation. The paper describing the model can be found here. NVIDIA’s Mask R-CNN

ChatbotsChina发起人 • 微博：@breezedeus • 博客：breezedeus.github.io 目录 • Chatbots简史 • 三个火枪手：三个Bot框架 • IR-Bot、Task-Bot、Chitchat-Bot • 爱因互动所做的事 • 总结 Chatbots简史 1950 • 提出 “图灵 测试” 1966 •ELIZA：MIT 发展的精神 治疗师 chatbot 基于深度学习的智能问答 IR-Bot：深度学习 • 句子表示、QQ匹配 Semantic Question Matching with Deep Learning Task-Bot: 任务对话机器人 Task-Bot: task-oriented bot 用户 语音合成 (TTS) 语言产生 (NLG) 语音识别 (ASR) 语言理解 (SLU) 策略优化 (DPO) (SC-LSTM) Tsung-Hsien Wen (2016) Task-Bot: 其他框架 • Microsoft: End-to-End Task-Completion Neural Dialogue Systems • DM: DST + DPO  RL • https://github.com/MiuLab/TC-Bot Task-Bot: 其他框架 • SLU+DST+DPO+NLG

实时更新参数 Task 训练预处理 Node 实时样本拼接 Node 在线模型训练 Node 离线样本拼接 Node 在线模型评估 Node 模型上线 Node 实时特征处理 Node 离线特征处理 Task Kafka输入 input process process output WeiFlow 工作流 Task 模型训练 Task 模型训练 Task Task Metrics输出 3 在线机器学习-工作流 互动行为日志 数据处理 点击行为日志 阅读行为日志 曝光行为日志 数据过滤 样本拼接 定时轮询 Kafka Hdfs 样本输出 3 在线机器学习-实时样本生成 • 多流拼接 • 曝光，互动，点击，真实阅读等多种数据流接入并多流拼接 • 如何解决日志延时问题 • 延迟等待机制，先到先走 • 定时轮寻，最长N分钟等待

do limited data labeling because it is expensive. Moreover, the labelers need to be trained for the task such that they follow the guidelines correctly while labeling, which further adds to the costs. In plentiful to translate language X to Y but scarce in the other direction from Y to X. Consider the task of translating English language sentences to German. Assume that the training data for english-german tuned GPT-2 model generating a synthetic text with a positive sentiment for a sentiment classification task. The label at the beginning of the input sequence guides the model to generate text with positive

Feng Li (SDU) Overview September 6, 2023 8 / 57 What is Machine Learning ? (Contd.) Improve on task T, with respect to performance metric P, based on expe- rience E. Feng Li (SDU) Overview September construct manually because they require specific detailed skills or knowledge tuned to a specific task (knowledge engineering bottleneck) Develop systems that can automatically adapt and customize them- Dilemma: neither exploitation nor exploration can be pursued exclu- sively without failing at the task. Feng Li (SDU) Overview September 6, 2023 35 / 57 Reinforcement Learning (Contd.) Example (Bioreactor)

addition to deep learning. Quantization Before we jump to working with a deep learning model, we have a task for you. You have been handed the charge of the Mars Rover! The rover is transmitting images back the next exercise. Exercise: Quantize an image, then dequantize it. Let’s go back to the original task of optimizing the Mars rover’s communications! Can we quantize the transmission, and dequantize it from this dataset. Each input example is a 28x28 matrix containing values in the range [0, 255]. The task is to identify which digit class a given example belongs to. This problem can be solved with a simple

fit in memory? ● How much data would the model need to achieve the desired performance on the given task that the model is solving? For example, when a model is trained to predict if a given tweet contains training and iInference efficiency metrics. If we have two models performing equally well on a given task, we would choose the one which does better on training or inference efficiency metrics, or both (depending

this does not matter so much in our example because the x and y dimensions have the same size). Your task is to compute 16 cluster centroids from this image, with each centroid being a vector of length three