Multi-modal data is becoming more common than before because of big data issues. Finding the semantically equal or similar objects from different data sources(called entity resolution) is one of the heart problem of multi-modal task. Current models for solving this problem usually needs much paired data to find the latent correlation between multi-modal data, which is of high cost. A new kind latent correlation is proposed in this article. With the correlation, multi-modal objects can be uniformly represented in a commonly shard space. A classifying based model is designed for multi-modal entity resolution task. With the proposed method, the demand of training data can be decreased much.

Initially developed by Facebook, Presto is an open source, distributed ANSI SQL query engine that delivers fast analytic queries against various data sources ranging in size from gigabytes to petabytes. For data scientists, this is ideal for returning Big Data query results in seconds, accelerating the iterative nature of data science discoveries by powering dashboards, reporting and ad-hoc analysis. Presto was designed and built from scratch to be a fast SQL query engine. It follows the classic MPP SQL engine design in which query processing is parallelized over a cluster of machines. As a result, highly concurrent queries execute at interactive speeds.

We propose a simple, robust, and scalable reverse image search engine that leverages convolutional features from Keras' pre-trained neural networks and the distance metric from Scikit-Learn's K-Nearest Neighbors. We show example queries using data scraped from Google images, and dive deeper in how we use the search engine to track the proliferation of memes from the dark web.

Search advertising is the placement of ads in search engine results. Businesses pay to place these ads at the top of search results. The price paid depends on several factors, such as search term popularity, competition and website quality. Taking a scroll through Google AdWords' lessons and picking up a certificate is a great way to start yourself off with paid search. But it doesn't give you everything you need to know.

The goal is to introduce various techniques required to build an IR System. In this course we will explore various methods to solve big data problem. We will evaluate alternative solutions and trade offs. In the later part of the course we will discuss various data mining algorithms to make sense of massive data sets.

Information retrieval and binary classification can be considered equivalent problems in principle. Information retrieval means to mark documents in a set of candidate documents as relevant or non-relevant for some question, on the basis of the properties of the documents. For binary classification, the problem is to distinguish between the'positive' and'negative' instances from a dataset, based on the features of the instances. Hence, from an abstract point of view, information retrieval is a special case of binary classification, with the documents being instances, the document properties being features and'relevant' being translated as'positive'. In practice, however, the general concepts from binary classification are not always helpful for information retrieval applications. The fact that often the proportion of relevant documents in a set of documents subject to a search is small or even very small is only one of the reasons for information retrieval to be considered a field of research for its own. As a consequence, some performance measures for information retrieval methods differ from those in use for binary classifiers or are called by different names. Precision and recall are possibly the most popular performance measures(see Chapter 8 of Manning et al., 2008, for a list of performance measures) for information retrieval methods: - Precision is the proportion of documents (instances) that are truly relevant (positive) among those documents which have been predicted relevant (positive). The term precision is also commonly used (with the same meaning) in binary classification.

Being one of HubSpot's largest agency partners, we stay close to the pulse of the ever-changing platform. A few weeks back, they announced the keywords tool is being sunsetted at the end of May 2018. There was and still is a lot of backlash against this move because customers use the HubSpot Keywords tool for tracking their overall level of SEO success. Keyword rank tracking has long been a metric that marketers follow, and they will continue to do so. However, although HubSpot has continued to invest, grow, and update sales, marketing, and analytics tools within the platform, their keywords tool hasn't been updated since Google started encrypting keyword search data prior to 2013.

We prove a \emph{query complexity} lower bound for approximating the top $r$ dimensional eigenspace of a matrix. We consider an oracle model where, given a symmetric matrix $\mathbf{M} \in \mathbb{R}^{d \times d}$, an algorithm $\mathsf{Alg}$ is allowed to make $\mathsf{T}$ exact queries of the form $\mathsf{w}^{(i)} = \mathbf{M} \mathsf{v}^{(i)}$ for $i$ in $\{1,...,\mathsf{T}\}$, where $\mathsf{v}^{(i)}$ is drawn from a distribution which depends arbitrarily on the past queries and measurements $\{\mathsf{v}^{(j)},\mathsf{w}^{(i)}\}_{1 \le j \le i-1}$. We show that for every $\mathtt{gap} \in (0,1/2]$, there exists a distribution over matrices $\mathbf{M}$ for which 1) $\mathrm{gap}_r(\mathbf{M}) = \Omega(\mathtt{gap})$ (where $\mathrm{gap}_r(\mathbf{M})$ is the normalized gap between the $r$ and $r+1$-st largest-magnitude eigenvector of $\mathbf{M}$), and 2) any algorithm $\mathsf{Alg}$ which takes fewer than $\mathrm{const} \times \frac{r \log d}{\sqrt{\mathtt{gap}}}$ queries fails (with overwhelming probability) to identity a matrix $\widehat{\mathsf{V}} \in \mathbb{R}^{d \times r}$ with orthonormal columns for which $\langle \widehat{\mathsf{V}}, \mathbf{M} \widehat{\mathsf{V}}\rangle \ge (1 - \mathrm{const} \times \mathtt{gap})\sum_{i=1}^r \lambda_i(\mathbf{M})$. Our bound requires only that $d$ is a small polynomial in $1/\mathtt{gap}$ and $r$, and matches the upper bounds of Musco and Musco '15. Moreover, it establishes a strict separation between convex optimization and \emph{randomized}, "strict-saddle" non-convex optimization of which PCA is a canonical example: in the former, first-order methods can have dimension-free iteration complexity, whereas in PCA, the iteration complexity of gradient-based methods must necessarily grow with the dimension.

From the 1970s through today, there have been three waves of technology innovation in the Silicon Valley that have spawned many industries, including ours (online marketing). First, there were semiconductors, then personal computers, and most recently, the internet. Now we're on the cusp of the next wave of innovation, driven by advances in artificial intelligence (AI). Given that our industry exists thanks to these waves of innovation and that automation -- a top trend in PPC for 2017 -- can be driven by AI, how might things change for us during this next wave? And what risks do we face when we start to use far more automation without knowing the extent of its abilities?

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