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Welcome — Theano 0.7rc1 documentation

Welcome — Theano 0.7rc1 documentation
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.transparent use of a GPU – Perform data-intensive computations much faster than on a CPU.efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs.speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.dynamic C code generation – Evaluate expressions faster.extensive unit-testing and self-verification – Detect and diagnose many types of errors. Theano has been powering large-scale computationally intensive scientific investigations since 2007. 2017/11/15: Release of Theano 1.0.0. You can watch a quick (20 minute) introduction to Theano given as a talk at SciPy 2010 via streaming (or downloaded) video: git clone How to Seek Help¶

Very Brief Introduction to Machine Learning for AI — Notes de cours IFT6266 Hiver 2010 The topics summarized here are covered in these slides. Intelligence The notion of intelligence can be defined in many ways. Here we define it as the ability to take the right decisions, according to some criterion (e.g. survival and reproduction, for most animals). Artificial Intelligence Computers already possess some intelligence thanks to all the programs that humans have crafted and which allow them to “do things” that we consider useful (and that is basically what we mean for a computer to take the right decisions). Formalization of Learning First, let us formalize the most common mathematical framework for learning. with the being examples sampled from an unknown process . which takes as argument a decision function and an example , and returns a real-valued scalar. under the unknown generating process Supervised Learning In supervised learning, each examples is an (input,target) pair: and takes an as argument. Local Generalization is close to input example , then the corresponding outputs .

machine learning - Sentiment Analysis model for Spanish By using our site, you acknowledge that you have read and understand our Cookie Policy, Privacy Policy, and our Terms of Service. Sentiment Analysis model for Spanish Ask Question up vote 3 down vote favorite I barely know about Data Analysis tools and techniques, so bare with me if I'm asking something too trivial. I'm looking for a Sentiment Analysis tool to process comments in Spanish. Is there a model/tool that already works with Spanish? I'm language agnostic so it does not matter if it's a Java, Python or even Go code. machine-learning nlp social-network-analysis sentiment-analysis share|improve this question edited May 10 '17 at 4:00 VividD asked Aug 4 '15 at 22:15 mcKain Out of curiosity, have you tried translating to English then using English sentiment analysis? add a comment | 3 Answers active oldest votes up vote 3 down vote The API supports Spanish (and Chinese (Mandarin), Japanese, Italian, French, Russian, Arabic, German, English). eg in Python: share|improve this answer A.

Caffe | Deep Learning Framework Introduction to Deep Learning Algorithms — Notes de cours IFT6266 Hiver 2010 See the following article for a recent survey of deep learning: Yoshua Bengio, Learning Deep Architectures for AI, Foundations and Trends in Machine Learning, 2(1), 2009 Depth The computations involved in producing an output from an input can be represented by a flow graph: a flow graph is a graph representing a computation, in which each node represents an elementary computation and a value (the result of the computation, applied to the values at the children of that node). The flow graph for the expression could be represented by a graph with two input nodes and , one node for the division taking as input (i.e. as children), one node for the square (taking only as input), one node for the addition (whose value would be and taking as input the nodes , and finally one output node computing the sinus, and with a single input coming from the addition node. A particular property of such flow graphs is depth: the length of the longest path from an input to an output. Insufficient depth can hurt

Machine Learning Repository Public Data Sets on AWS Click here for the detailed list of available data sets. Here are some examples of popular Public Data Sets: NASA NEX: A collection of Earth science data sets maintained by NASA, including climate change projections and satellite images of the Earth's surface Common Crawl Corpus: A corpus of web crawl data composed of over 5 billion web pages 1000 Genomes Project: A detailed map of human genetic variation Google Books Ngrams: A data set containing Google Books n-gram corpuses US Census Data: US demographic data from 1980, 1990, and 2000 US Censuses Freebase Data Dump: A data dump of all the current facts and assertions in the Freebase system, an open database covering millions of topics The data sets are hosted in two possible formats: Amazon Elastic Block Store (Amazon EBS) snapshots and/or Amazon Simple Storage Service (Amazon S3) buckets. If you have any questions or want to participate in our Public Data Sets community, please visit our Public Data Sets forum.

Datasets for Data Mining and Data Science See also Data repositories AssetMacro, historical data of Macroeconomic Indicators and Market Data. Related Large Network Dataset Collection Social networks Networks with ground-truth communities Communication networks Citation networks Collaboration networks Web graphs Product co-purchasing networks Internet peer-to-peer networks Road networks Autonomous systems graphs Signed networks Location-based online social networks Wikipedia networks, articles, and metadata Temporal networks Memetracker and Twitter Online Communities Online Reviews Face-to-Face Communication Networks Graph classification datasets Network types Directed : directed network Undirected : undirected network Bipartite : bipartite network Multigraph : network has multiple edges between a pair of nodes Temporal : for each node/edge we know the time when it appeared in the network Labeled : network contains labels (weights, attributes) on nodes and/or edges Network statistics Citing SNAP We encourage you to cite our datasets if you have used them in your work.

Cluster analysis The result of a cluster analysis shown as the coloring of the squares into three clusters. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς "grape") and typological analysis. Definition[edit] According to Vladimir Estivill-Castro, the notion of a "cluster" cannot be precisely defined, which is one of the reasons why there are so many clustering algorithms.[4] There is a common denominator: a group of data objects.

Deep Learning Deep Learning News - Deep Learning News Uncertainty in deep learning and neural network? I do not feel that way! Uncertainty in deep learning and neural network? I do not feel that way! As you might know, neural networks are often in the news these days, with many success stories. Neural networks are now the state-of-the-art algorithm to understand complex sensory data such as images, videos, speech, audio, voice, music, etc. Neural networks recently got rebranded under the name Deep Learning (DL) or deep neural networks. In 2012 they made the news when they outperformed by more than 10% any other algorithm in a industry-standard image dataset: They also had similar improvements in speech recognition, up to 20%: And in many other tasks. Yet industries and investors are wary. They often see new algorithms come and go, almost on a year-to-year basis. They say: ”why do a start-up on deep learning or neural networks? It is a legitimate doubt, however I am certain we should not worry about this anymore. Neural Networks are here to stay for many years. There are 3 strong reasons for this: Bottom line: