plethora of choices that we face when training a deep learning model in the computer vision domain. A Search Space for n parameters is a n-dimensional region such that a point in such a region is a set of each of those parameters. The parameters can take discrete or continuous values. It is called a "search" space because we are searching for a point in which minimizes (or maximizes) an Evaluation Function example for choosing quantization and/or clustering techniques for model optimization. We have a search space which has two boolean valued parameters: quantization and clustering. A $$True$$ value means

accompanying the given prompts. Both these models have been deployed in production. BERT is used in Google Search to improve relevance of results, and GPT-3 is available as an API for interested users to consume hyper-parameters like learning rate, regularization, dropout, etc. This relies on search methods that can range from Random Search to methods that smartly allocate resources to promising ranges of hyper-parameters Architecture Search (NAS) can help go beyond just learning hyper-parameters, and instead search for efficient architectures (layers, blocks, end-to-end models) automatically. A simplistic architecture search could

score_threshold = 0 def similarity_search_with_score_by_vector( self, embedding: List[float], k: int = 4 ) -> List[Tuple[Document, float]]: scores, indices = self.index.search(np.array([embedding], dtype=np when not enough docs are returned. continue _id = self.index_to_docstore_id[i] doc = self.docstore.search(_id) if not self.chunk_conent: if not isinstance(doc, Document): raise ValueError(f"Could not find 0 <= l < len(self.index_to_docstore_id): _id0 = self.index_to_docstore_id[l] doc0 = self.docstore.search(_id0) if docs_len + len(doc0.page_content) > self.chunk_size: break_flag = True break elif doc0

params = {‘kernel’:[‘linear’, ‘rbf’], ‘C’:[1, 10]} grid_search = GridSearchCV(svc, params, cv=5) grid_search.fit(X_train, y_train) grid_search.best_params_ 在参数网格上进行穷举搜索，方法简单但是搜索速度慢（超参数较多时），且不 容易找到参数空间中的局部最优 {‘kernel’:[‘linear’, ‘rbf’], ‘C’:randint(1, 20)} random_search = RandomizedSearchCV(svc, param_dist, n_iter=10) random_search.fit(X_train, y_train) random_search.best_params_ 在参数子空间中进行随机搜索，选取空间中的100个点进行建模（可从

analysis, network/tribe analysis Netflix • Recommendation engine Pinterest • Image recognition search Fraud.net • Detect online payment fraud DataXu • Leverage automated & unattended ML at large scale compile on … Amalgamation Runs in browser with Javascript The first image for search “dog” at images.google.com Outputs “beagle” with prob = 73% within 1 sec Deep RL | Playing

PS的参数放置策略 • Ps分布式分片的均衡，避免分片大小不一致  NN网络矩阵按行切分，解决请求包不均衡问题  特征按照Hash方式分布式存储 • 模型并行调超参  grid search  random search PS的多模型训练 • 提高内存使用效率  model group内共享特征key的存储 • 超大规模模型 -> 高扇出的分布式PS • 长尾效应：单个分片的抖动（网络、CPU）对请求影响变大

guidance. Feng Li (SDU) Overview September 6, 2023 15 / 57 Applications of Machine Learning Document Search Given counts of words in a document, determine what its topic is. Group documents by topic without interaction (with environment) Goal-directed learning Learning what to do and its effect Trial-and-error search and delayed reward Feng Li (SDU) Overview September 6, 2023 34 / 57 Reinforcement Learning (Contd

import text_to_word_sequence # NLTK Import try: from nltk.corpus import wordnet # Placeholder search to ensure wordnet data is available. wordnet.synsets('hello') except LookupError as e: import implements random shuffling: # NLTK Import try: from nltk.tokenize import sent_tokenize # Placeholder search to ensure wordnet data is available. sent_tokenize('hello') except LookupError as e: import nltk

? How to optimize ▪ ???? = σ? ? ∗ ?? + ? − ?? 2 ▪ Minimize ???? ▪ ?′ ∗ ? + ?′ → ? Heuristic Search Convex Optimization https://spin.atomicobject.com/2014/06/24/gradient-descent-linear-regression/

Clarification ▪ 导数, derivate ▪ 偏微分, partial derivate ▪ 梯度, gradient What does grad mean? How to search for minima? http://mccormickml.com/2014/03/04/gradient-descent-derivation/ Learning process-1