Learn more: http://oracle.com/analytics
Discover what Oracle augmented analytics is and how it helps businesses analyze all their data for better decisions.

Augmented Analytics can provide you with critical intelligence on your marketing data, beyond that provided by conventional visualization tools. Automatically analyze your marketing data with AI to detect trends and anomalies to skyrocket your marketing campaigns. Want to learn how you can take advantage of augmented analytics today? Book a free demo with us now: https://info.adverity.com/get-started-ai-powered-marketing?utm_campaign=Augmented%20Analytics%20Re-Launch&utm_source=youtube&utm_medium=social&utm_content=augmented-launch-explainer

Recorded on Mar 24 2016 at GCP NEXT 2016 in San Francisco.

Visual effects rendering is a computationally intensive process where one second of screen-time can require thousands of cores and terabytes of frame data. Learn how Academy Award-winning and recognized studios take advantage of cloud economics and Google’s on-demand computing to realize their creative visions and expand this digital medium for storytelling.

Speakers: Julia Ferraioli, Google & David Zuckerman, Wix

In the video: 10. Blockchain and Analytics in the Cloud | Tech2Teach

Blockchain is a secure, distributed, open technology that can help speed up
processes, lower costs, and build transparency and traceability in
transactional applications. It is an immutable Network allowing members to
view only those transactions that are relevant to them. The more open, diverse,
and distributed the network, the stronger the trust and transparency in the data
and transactions. 85% of businesses today rely on multiple clouds to meet their IT
needs, with more than 70% using more than three. These businesses need to be able
to move applications and data across multiple clouds easily and securely,
leading to the emerging demand to build and manage business applications such as
blockchain for the multi cloud environment. Blockchain and AI, much like
IoT and AI, powered by the cloud, also have a three-way relationship.

Other videos related to it on You Tube:

Data Driven #5: Blockchain and Big Data
Lecture 54: Blockchain for Data Analytics – I (Blockchain for Big Data)
Understanding blockchain analytics
How does a blockchain work – Simply Explained
The Convergence of Blockchain, Machine Learning, and the Cloud | Steve Lund | TEDxBYU
Blockchain & Internet of Things
Blockchain and Analytics
Blockchain for Big Data, Storage and Analytics
Blockchain and Google: How Blockchain Technology Will Impact Google

#Blockchain​ #Technology​ #Coding #bigdata #Blockchain​ + #OracleAnalytics​ ! A presentation by #Analytics​ #Rockstar​ Gary Crisci that you’re not going to want to miss! Here is a quick preview. Register for the full #OASummit​ session for free at
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#OverviewofCloudComputing #Introduction #Definition #Essential #CharacteristicsofCloudComputing #BriefHistory #EvolutionofCloud #KeyConsiderationsforCloudComputing #CloudServiceProviders #CloudServices #CreateCloudAccount
#CloudAdoption #EmergingTechnologies #Cloud #Introduction #CloudAdoption #NoLongerChoice #SomeCaseStudies #IOT #InternetofThings #AI #ArtificialIntelligence #Blockchain #Analytics #CloudComputingService #DeploymentModels
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Over 3 billion photos are uploaded and shared on the internet every day. Visual content is 40x more likely to be shared on social media than text. What are your customers sharing on social media? And what are they tagging your brand in? And what impact do these visual conversations drive for your brand? You need comprehensive visual analytics to understand what’s really happening on social media.

Join us to learn about NetBase’s next-generation artificial intelligence for image analysis capabilities. We’ll explain how it analyzes visual posts to identify brand logos and keywords to give the most comprehensive view of a brand’s performance. The platform also accurately classifies images by facial emotions, common scenery, and everyday objects to provide the greatest depth of insight.

– More accurately track share of voice and protect your brands reputation by including the analysis of visual brand mentions and understanding their context
– Discover creative inspiration and improve campaign performance through learning what is driving visual engagement and inspiring your customers
– Understand more accurately when, where and how your customers are using your products and services

Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics).

Machine Learning and Predictive Analytics. #MachineLearning

Intro to Predictive Analytics is the second video in this machine learning course. This video explains how machine learning algorithms are used in the field of data analytics to create models of reality.

This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1


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In today’s digital age, “Data” is the new oil. There is a huge amount of data around us, and it’s expanding at an exponential rate. The challenge is that this big data set (Big Data) is noisy and heterogeneous. So, it’s very important to extract knowledge or insights from the data around us.

The field of data science explores the patterns within large data sets and aims to drive meaningful actionable decisions.

Data science is an umbrella term that encompasses data analytics, data mining, machine learning (ML), artificial intelligence (AI), and several other related disciplines.

In this video, learn about the top 5 countries to study data science, ML, AI & big data analytics abroad. Additionally, get to know the top universities, job prospects, and average salaries in those countries.

Related Blogs:

Top Universities for Masters (MS) in Data Science in USA

Top Masters in Data Science and Analytics Programs in Canada

Masters in Data Science, ML and Analytics in UK: Top Universities, Costs, Job Prospects, Salaries

MS Machine Learning / AI vs MS Data Science vs MS Business/Data Analytics – How to Choose the Right Program

Masters in Data Science and Analytics in Germany – Top Universities, Jobs & Salaries

Masters in Data Science and Business Analytics in France: Best Programs, Jobs & Salaries

Masters (MS) Business Analytics in Canada: Top Schools, Skills, Jobs and Salaries

Best Master’s (MS) in Analytics Programs in USA

For personalized career and study abroad guidance, please visit https://www.stoodnt.com/

You can also book a 1-on-1 counselling session at https://www.stoodnt.com/career-college-admission-counselling

#DataScience #BigData #BusinessAnalytics #MachineLearning #ArtificialIntelligence

Some machine learning proponents claim you only have to provide data to get value. However, reality is a bit more complex. On the way to active analytics for business, we have to answer two big questions: What must happen to data before running machine learning algorithms, and how should machine learning output be used to generate actual business value?

Jean-François Puget demonstrates the vital role of human context in answering those questions. You’ll discover why human context should be embraced as a guide to building better, smarter systems that people will use, trust, and love.

This keynote is sponsored by IBM.

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This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.

In June 2017, Amplify brought 42 of the world’s boldest thinkers to AMP offices in Melbourne, Sydney, and Auckland to explore ‘the edge of the possible’ across emerging global trends.

In this Amplify Talk, hear from Richard Socher (Salesforce – Chief Data Scientist) as he discusses the edge of customer experience through data and analytics.

Deep Learning has revolutionized several industries with its state of the art results in speech recognition, image classification and natural language understanding.

In this keynote, Socher introduces solutions for visual object classification in images, sentiment classification, automated question answering and marketing analysis, and how these processes can be used to enrich the customer experience.

Discussion with panelists from CA Technologies, A Broadcom Company

“There’s a proliferation of unstructured data. Companies collect massive amounts of news feed, emails, social media, and other text-based information to get to know their customers better or to comply with regulations. However, most of this data is unused and untouched. Natural language processing (NLP) holds the key to unlocking business value within these huge data sets, by turning free text into data that can be analyzed and acted upon. Join this tech talk and learn how you can get started mining text data effectively and extracting the rich insights it can bring. We will also demonstrate how you can build a text analytics solution with Amazon Comprehend and Amazon Relational Database Service.

Learning Objectives:
– Get an introduction to Natural Language Processing (NLP)
– Learn benefits of new approaches to analytics and technologies that help empower better decisions, e.g., NLP, data prep
– Build a text analytics solution with Amazon Comprehend and Amazon Relational Database Service in a step by step demo”

Natural Language Processing (NLP) using NLTK and Python to perform basic text analytics such as Word and Sentense Tokenizing, Parts of Speech POS tagging, extracting Named Entities

Video covers:
Word and Sentense Tokenizer, Parts of Speech POS tokenizer, Named Entities

Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics.

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Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency.

Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words.

One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word.

The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector.

Two popular tools:
Word2Vec: https://code.google.com/archive/p/word2vec/
Glove: http://nlp.stanford.edu/projects/glove/

Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse.

Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language.

Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis:

“He turned around a team otherwise known for overall bad temperament”

In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive.

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Data, analytics and machine learning are the foundation for AI (artificial intelligence). The challenge with data is the variety across locations (cloud, on-prem, private cloud), types (structured, unstructured), and platforms (operational database, data warehouse, hadoop, fast data platforms, etc). Once we deal with our data management, we are able to move on to analytics, which lets us extract insight from our data. Predictive analytics leads us to machine learning. Once we develop enough machine learning models, we are beginning to reach AI.

IBM’s solution to managing big data is the Hybrid Data Management Platform. Check it out: https://ibm.co/2JNMyKp

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“Apache Spark is a powerful, scalable real-time data analytics engine that is fast becoming the de facto hub for data science and big data. However, in parallel, GPU clusters are fast becoming the default way to quickly develop and train deep learning models. As data science teams and data savvy companies mature, they will need to invest in both platforms if they intend to leverage both big data and artificial intelligence for competitive advantage.

This session will cover:
– How to leverage Spark and TensorFlow for hyperparameter tuning and for deploying trained models
– DeepLearning4J, CaffeOnSpark, IBM’s SystemML and Intel’s BigDL
– Sidecar GPU cluster architecture and Spark-GPU data reading patterns
– The pros, cons and performance characteristics of various approaches

You’ll leave the session better informed about the available architectures for Spark and deep learning, and Spark with and without GPUs for deep learning. You’ll also learn about the pros and cons of deep learning software frameworks for various use cases, and discover a practical, applied methodology and technical examples for tackling big data deep learning.

Session hashtag: #SFds14″

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