While the name “DataOps” implies that it borrows most heavily from DevOps, it is all three of these methodologies — Agile, DevOps and statistical process control — that comprise the intellectual heritage of DataOps.
Organizations need people who can talk to both people and machines and they need people in their upper echelons who specialize in talking to machines.
Overall, we found that Giraph was better able to handle production-scale workloads, while GraphX offered a number of features that enabled easier development.
The two reasons to use XGBoost are also the two goals of the project:
1. Execution Speed.
2. Model Performance.
我们的Serverless AI应用用到了两种技术。首先使用了公共云提供的对象存储和数据库服务，统称为BaaS（Backend as a Service，后端即服务）。其次用了Lambda框架，称为FaaS(Functions as a Service，函数即服务)。
The stochastic gradient descent method and its variants are algorithms of choice for many Deep Learning tasks. These methods operate in a small-batch regime wherein a fraction of the training data, usually 32--512 data points, is sampled to compute an approximation to the gradient. It has been observed in practice that when using a larger batch there is a significant degradation in the quality of the model, as measured by its ability to generalize.
If the sequence_length vector is provided, dynamic calculation is performed.
This method of calculation does not compute the RNN steps past the maximum
sequence length of the minibatch (thus saving computational time).
Recurrent neural networks (RNNs) offer several advantages for our product.
1. Most prominently, they operate directly on sequences of data and thus are a perfect fit for modeling consumer histories.
2. Time-intensive human feature engineering is no longer required. In general, learning from raw data can help to avoid limitations when placing too much confidence in human domain modeling.
3. Furthermore, demand for explaining the predictions of machine learning models is increasing strongly. RNNs can be helpful in providing explanations as they make it easy to directly relate event sequences to predicted probabilities.
CRISPR is the most powerful genetic engineering tool ever created.
But CRISPR only allows us to modify one gene at a time, one organism at a time. To make species-level changes, CRISPR must be amplified by another powerful phenomenon: gene drive.
Gene drive is any mechanism that makes a gene particularly “selfish” in that it increases the probability that that particular gene will be inherited above 50%, regardless of any selection pressure.
Why did Deep Learning only take off in the 2010s and not earlier?
1. Some important discoveries in the 2000s made training deep neural nets feasible.
2. Computing power and the amount of data required to train deep neural nets was not available until recently.
Stemming = heuristically removing the affixes of a word, to get its stem (root).
Lemmatization = morphological analysis of a word that returns its lemma, which is a normalized form of a set of morphologically related forms, chosen by convention (nominative singular for nouns, infinitive for verbs, etc.) to represent that set. This is the form in which a word appears in the dictionary.
WordNet is a semantically-oriented dictionary of English, consisting of synonym sets — or synsets — and organized into a network.
Negative sampling is one of the ways of addressing this problem- just select a couple of contexts c1 at random. The end result is that if cat appears in the context of food, then the vector of food is more similar to the vector of cat (as measures by their dot product) than the vectors of several other randomly chosen words (e.g. democracy, greed,freddy), instead of all other words in language.
To summarize - you first reduce dimensionality to a small set of numbers.
Then by randomly choosing numbers in this small set, and decompressing, you get a set of images similar to what you had in your training set.
A word embedding is a representation of a word, just like an oil painting might be a representation of a sunflower. Word embeddings use numbers to represent words. For a neural network like word2vec, they may use 300-500 numbers. Each of those numbers is in a dimension, and each locates a word in, say, 300-dimensional space.