Machine Learning with Scala
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This document discusses machine learning concepts like neural networks, deep learning, and recurrent neural networks. It introduces Deeplearning4j, an open-source deep learning library for Java and Scala, and its commercial support arm Skymind. Specific machine learning applications mentioned include optical character recognition, Netflix recommendations, topic modeling, sentiment analysis, machine translation, and question answering. Parallelization techniques for model training like stochastic gradient descent are also covered.
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1 Functorial definition of tensors for learning non-linear models (manifolds)
2. Monads to compose of explicit kernel functions in Euclidean space
3. Implicit class to extends Scala standard library
4. Stackable traits and dependency injection to build formal models and dynamic workflows
5. Tail recursion to implementation dynamic programming techniques
6. Streaming to reduce memory consumption for big data
7. Control of back pressure in data flows
http://patricknicolas.blogspot.com
http://bit.ly/12GjRu9
Big Data with KNIME is as easy as 1, 2, 3, ...4!
This document discusses how KNIME can be used for big data analytics. It describes the challenges of variety, volume, and velocity of big data. It then provides examples of using KNIME workflows to access and analyze big data from Hadoop and Spark, including connecting to databases, performing in-database operations, importing data into KNIME, and using nodes specific to big data platforms.
Sentiment Analysis
1) The document discusses techniques for sentiment classification of text using machine learning. It examines applying Naive Bayes, Maximum Entropy, and Support Vector Machines to determine if a text has a positive, negative, or neutral sentiment.
2) It uses a dataset of movie reviews labeled as positive or negative to train and evaluate the models. Key features for the models include unigram presence in the text.
3) Evaluation results show that Naive Bayes and Support Vector Machines achieved over 82% accuracy in classifying positive and negative sentiment in the movie review texts. The best performing feature was unigram presence rather than frequency.
Scalable and Flexible Machine Learning With Scala @ LinkedIn
The presentation given by Chris Severs and myself at the Bay Area Scala Enthusiasts meetup. http://www.meetup.com/Bay-Area-Scala-Enthusiasts/events/105409962/
Using Deep Learning for Recommendation
Goes over the basics of using a deep recurrent neural network for shopping cart next item recommendation by using LSTMs and DeepLearning4J
KNIME tutorial
This document provides an overview of using the KNIME analytics platform for computational drug design. It introduces KNIME and its basic components, including nodes, workflows and dialog boxes. The document then describes a sample workflow for compound selection and focused screening, which involves reading chemical data, calculating properties, applying filters and selecting diverse molecules. The overall goal is to guide users through building a KNIME workflow to select compounds for further screening.
Text Processing with KNIME
This tutorial was presented at a Boston KNIME User Meetup in 2014 and offers a crash course in KNIME, text processing, text mining, and topic classification.
Project prSentiment Analysis of Twitter Data Using Machine Learning Approach...
This document outlines a research project on classifying user reviews for electronic gadgets using sentiment analysis. The project used Twitter data labeled as positive or negative and preprocessed, extracted features from, and trained classifiers on this data. Naive Bayes, maximum entropy, and support vector machines were evaluated, with Naive Bayes achieving the best accuracy of 88.2%. Adding semantic analysis using WordNet further improved accuracy to 89.9%. The results were analyzed and future work proposed to expand the training data and use WordNet for summarization.
Knime & bioinformatics
The document provides an overview of the KNIME analytics platform and its capabilities. It discusses:
- KNIME's origins, offices, codebase, and application areas including pharma, healthcare, finance, retail, and more.
- The key components of the KNIME platform including data access, transformation, analysis, visualization, and deployment capabilities.
- Integrations with tools like R, Weka, databases, and file formats.
- Community contributions expanding KNIME's functionality in areas like bioinformatics, chemistry, image processing, and more.
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Distributed machine learning 101 using apache spark from the browser
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Scalable and Flexible Machine Learning With Scala @ LinkedIn
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If you like what you read be sure you ♥ it below. Thank you!
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Find out more at https://madrid2018.codemotionworld.com/speakers/
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Embedded machine learning-based road conditions and driving behavior monitoring
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
Hematology Analyzer Machine - Complete Blood Count
Hematology Analyzer Machine - Complete Blood Count
Machine Learning with Scala
- 1. MACHINE LEARNING WITH SCALA SUSAN ERALY SKYMIND ANDY PETRELLA DATA FELLAS
- 2. MACHINE LEARNING Not Terminator! (despite our name) Applications are everywhere. OCR Netflix recommendations
- 3. NEURAL NETS & DEEP LEARNING A Perceptron/Neuron can be loosely compared to a NAND gate Non linear functions can be constructed With more compute and with big data…
- 4. DEEPLEARNING4J dl4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Skymind, is it’s commercial support arm
- 5. SCIENTIFIC COMPUTING & THE JVM Problems when considering HPC • Vectorization • Array indexing, 32 bit address space FULLY native backend https://github.com/deeplearning4j/libn d4j JAVACPP OpenMP CUDA
- 6. MICRO-SERVICES + ML? Kinda like micro-services Reduce lock in Take math, data cleaning, model training, choosing algorithms ... … and separate them
- 7. SGD: SERIAL VS. PARALLEL Model Training Data Worker 1 Master Partial Model Global Model Worker 2 Partial Model Worker N Partial Model Split 1 Split 2 Split 3 …
- 8. MAP REDUCE VS. PARALLEL ITERATIVE Input Output Processor Processor Processor Superstep 1 Processor Processor Superstep 2 . . . Processor
- 9. NLP AND DL • Topic Modeling/Sentiment Analysis • Machine Translation • Question Answer NLP is hard “The best part of the movie is the end credits” “It should have been a great movie…”
- 10. RECURRENT NN Loops Temporal behavior Used for temporal series
- 11. LSTMS -The solution to exploding and vanishing gradients
- 12. APPLICATIONS - Sequence to Sequence Credits, Andrej Karpathy
- 13. WORD2VEC -Word embeddings that represent meaning/context King – Man + Woman ~ Queen
- 14. WORD2VEC
- 15. APPLICATIONS - Sequence to Sequence Second Lord: They would be ruled after this chamber, and my fair nues begun out of the fact, to be conveyed, Whose noble souls I'll have the heart of the wars. Clown: Come, sir, I will make did behold your worship. VIOLA: I'll drink it. Credits, Andrej Karpathy
- 17. DATA FELLAS Spark-Notebook is the only Scala based notebook. It is scalable and enables interactive work on Spark, Akka, Cassandra, Kafka and can plot interactive plots on any Scala type. Data Fellas enables data-driven business, bringing productivity to data science in enterprise.