Natural language Analysis
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I give an overview of current state of natural language analysis using machine learning algorithms. #naturallanguage #machinelearning #artificianintelligence
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1. Marketing- Here AI analyze buyer’s behavior and provide best products & deals to them.
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What is AI and how it works? What is early history of AI. what are risks and benefits of AI? Current status and future of AI. General perceptions about AI. Achievement of AI. Will AI be more beneficent or more destructive?
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Over the past couple of years, we found ourselves investing in 7 AI and ML enabled companies, in areas as diverse as marketing, credit scoring, recruitment, fertility tracking and so on. It appears that we’ve been among the most active European investors in what most people today still view as a “theme”. Most importantly, more and more of our other portfolio companies are starting to adopt these technologies in order to make their products better.
What follows is a presentation that we gave to our LPs at our most recent investor day in February. We tried to give them a primer on these technologies: what they are ; why we are all talking about them now ; and how we, at Sunstone, are thinking about investing in those companies.
Ai in finance
This document provides an overview of artificial intelligence (AI) and machine learning (ML) applications in the banking and finance industry. It begins with definitions of AI, ML, and different types of ML. It then discusses how data is generated globally and some key areas where AI/ML can be applied, including customer engagement, fraud analysis, risk analytics using techniques like value-at-risk and stress testing, real-time analysis, risk assessment, fraud detection and management, financial advisory services, and machine trading. Game theory and probability are also briefly introduced as relevant concepts. The overall message is that AI/ML have significant potential to transform various functions in banking and finance through applications like customer service automation, risk management, and trading.
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The class outline covers introduction to unstructured data analysis, word-level analysis using vector space model and TF-IDF, beyond word-level analysis using natural language processing, and a text mining demonstration in R mining Twitter data. The document provides background on text mining, defines what text mining is and its tasks. It discusses features of text data and methods for acquiring texts. It also covers word-level analysis methods like vector space model and TF-IDF, and applications. It discusses limitations of word-level analysis and how natural language processing can help. Finally, it demonstrates Twitter mining in R.
Our World is Socio-technical
A talk given at the annual Computer Science for High School Teachers event at Victoria University of Wellington. I presented on some basics of the World Wide Web and why it's worth to preserve it, our work on non-expert tools to populate semantically enriched content, a current project to identify NZ native birds based on their calls that involves citizen science and contemporary deep learning using TensorFlow, a project that investigates the impact of online citizen science on the development of science capabilities of primary school children, and my collaboration with Adam Grener from the School of English, Film, Theater and Media Studies at VUW with whom I am working on computational tools for the literature studies.
AI Technology Overview and Career Advice
An introductory overview of current AI technology and applications, and career advice for those who are interested in AI.
AILABS - Lecture Series - Is AI the New Electricity? - Advances In Machine Le...
Prof. Garain discusses in brief on the backgrounds of learning algorithms & major breakthroughs that have been made in the field of machine perception in the last 50 yrs. He also discusses the role of statistical algorithms like artificial neural network, support vector machines, and other concepts related to Deep Learning algorithms.
Along with the above, Prof. Garain touched upon the basics of CNN & RNN, Long Short-Term Memory Networks (LSTM) & attention network & illustrate all of these using real-life problems. Several state-of-the-art problems like image captioning, visual question answering, medical image analysis etc. were discussed to make the potential of deep learning algorithms understandable.
Prof. Utpal Garain is one of the leading minds in Kolkata in the field of Neural Networks & Artificial Intelligence. His research interest is now focused on AI research, especially exploring deep learning methods for language, image and video analysis including NLP tools, OCRs, handwriting analysis, computational forensics and the like.
An Introduction to Recent Advances in the Field of NLP
This document provides an introduction to recent advances in natural language processing (NLP). It discusses how machines can process text through preprocessing like lemmatization and stemming. Preprocessing is important because text is unstructured and language is ambiguous. Word embeddings like Word2Vec represent words as vectors to address issues with one-hot encodings. Language models now perform at human-level on tasks like language modeling by using neural networks on word embeddings to predict the next word. Large models like GPT-3 have billions of parameters and are trained on internet-scale text, achieving strong performance. The future of NLP includes reaching human-level ability in more tasks and domains, and scaling performance with more data.
Machine Learning Overview: How did we get here ?
Alan F. Smeaton
Insight@DCU Machine Learning Workshop 2017
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning tutorial 9/2019
This document provides an overview and introduction to deep learning. It discusses motivations for deep learning such as its powerful learning capabilities. It then covers deep learning basics like neural networks, neurons, training processes, and gradient descent. It also discusses different network architectures like convolutional neural networks and recurrent neural networks. Finally, it describes various deep learning applications, tools, and key researchers and companies in the field.
Deep Learning Tutorial
See hints, Ref under each slide
Deep Learning tutorial
https://www.youtube.com/watch?v=q4rZ9ujp3bw&list=PLAI6JViu7XmflH_eGgsWkwvv6lbXhYjjY
Artificial Intelligence by B. Ravikumar
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
Hacking Predictive Modeling - RoadSec 2018
This document provides an overview of machine learning and predictive modeling techniques for hackers and data scientists. It discusses foundational concepts in machine learning like functionalism, connectionism, and black box modeling. It also covers practical techniques like feature engineering, model selection, evaluation, optimization, and popular Python libraries. The document encourages an experimental approach to hacking predictive models through techniques like brute forcing hyperparameters, fuzzing with data permutations, and social engineering within data science communities.
AI for Everyone: Master the Basics
The document provides an overview of artificial intelligence (AI) concepts and applications through a 4-module online course. Module 1 defines AI and common applications like healthcare, education, and customer service. Module 2 covers machine learning, deep learning, neural networks, and their various applications. Module 3 discusses issues around AI including privacy, job disruption, bias, and ethics. Module 4 explores the future of AI and how to start a career in the field.
Data Science, Machine Learning and Neural Networks
Lecture briefly overviewing state of the art of Data Science, Machine Learning and Neural Networks. Covers main Artificial Intelligence technologies, Data Science algorithms, Neural network architectures and cloud computing facilities enabling the whole stack.
OWF14 - Big Data : The State of Machine Learning in 2014
Olivier GRISEL
A broad overview of what technologies are usable now, what are just around the corner.
Ai introduction
Artificial intelligence (AI) is intelligence exhibited by machines. It is the branch of computer science which deals with creating computers or machines that are as intelligent as humans. The document discusses the history and evolution of AI from its foundations in 1943 to modern applications. It also defines different types of AI such as narrow AI, artificial general intelligence, and artificial super intelligence. Popular AI techniques like machine learning, deep learning, computer vision and natural language processing are also summarized.
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AILABS - Lecture Series - Is AI the New Electricity? - Advances In Machine Le...
AILABS - Lecture Series - Is AI the New Electricity? - Advances In Machine Le...
An Introduction to Recent Advances in the Field of NLP
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OWF14 - Big Data : The State of Machine Learning in 2014
More from Rudradeb Mitra
Architecting IoT with Machine Learning
The document discusses how machine learning can help architect Internet of Things (IoT) systems for widespread consumer adoption. It describes three examples of using machine learning with IoT data: (1) identifying patterns of risky drivers to adjust insurance premiums, (2) predicting short-term driving behavior to improve road safety, and (3) using long-term driving history with recurrent neural networks to provide customized nudging to change driver behavior over time. The document argues that machine learning can create value from IoT data and benefit consumers by making systems safer, lowering costs, and incentivizing good behaviors.
Growth : Crossing the chasm
In this talk, I speak about how the growth strategy for every market segment (innovators, early adopters, Early Majority, Late Majority) is different. And how to grow at each stage.
Machine Learning: For the people, By the people, Of the people
In this talk, I show how Machine Learning is going to change the energy sector and make solar energy more accessible. I also give the example from the banking sector in Vietnam on how Machine Learning can help unbankable people get loans. I conclude by saying that my firm conviction is that Machine Learning has the ability to help those who have been left behind in the previous technological revolution.
Machine learning for retail banking
This is a talk given to bankers at CCX Forum where I share how Machine Learning products can be built for retail banking sector, what are the challenges and how can they be overcome.
Successful adoption of Machine Learning
In this presentation, I show through case studies what are things one need to be careful with when building ML products.
Machine Learning Adoption: Crossing the chasm for banking and insurance sector
In this talk, I answer why, what and how Machine Learning can help solve major problems in the Banking sector.
Predictive Analytics to the rescue of IoT
The document discusses how predictive analytics using neural networks, such as recurrent neural networks and long short term memory cells, can be applied to problems in industrial IoT, giving examples of how these techniques could be used to predict risky drivers from sensor data and to predict future customer purchases from shopping history data. It also outlines potential future directions for predictive analytics, such as using reinforcement learning approaches like Q-learning to develop intelligent agents.
Predictive Analytics using Neural Networks
In this presentation I explain how Neural Networks can be used to do predictive analytics. I take the use case of predicting user buying behavior and explain how word2vec and LSTM network can be used for that.
Predictive Analytics disrupting Product development
Predictive analytics can be used to disrupt product development in two key ways:
1. By analyzing past user behavior and orders, predictive models like neural networks and recurrent neural networks can predict future user behavior and needs and adapt products accordingly. This was demonstrated through a case study of order data from Instacart.
2. By analyzing attributes of users like driving behavior from a driver app and friends' networks, unsupervised neural networks can cluster users and infer new features for different groups, like incentives or gamification for improving driver safety. This was shown through a road trip tracking app case study.
3. The future of predictive analytics includes using self-organizing maps to predict bugs based on code dependencies and regions
Predictive Analytics
This document discusses predictive analytics and how it can be used to make important business predictions. It addresses why prediction is important, how predictions can be made using data, and what types of predictions are useful. Specifically, it notes that predictive analytics can help businesses understand customer equity, determine what customers are willing to pay, and inform marketing and sales decisions. Deep neural networks are presented as one technique for deriving insights from data to make predictions. Examples of using predictive analytics at Instacart and Zalando are also briefly described. The document concludes by emphasizing that predictive analytics is the future of business intelligence and businesses should focus on adding value for customers with their predictions.
Artificial Intelligence: Case studies (what can you build)
The document discusses different types of artificial intelligence algorithms like deep learning using neural networks and reinforcement learning. It provides examples of both short term and mid term projects that can be built using existing AI tools, from basic chatbots to predictive maintenance and customer behavior analysis. Long term challenges are also mentioned, like developing more intuitive algorithms through reinforcement learning and ensuring the safe and responsible development of advanced artificial intelligence.
Predictive Analytics
In this talk I explain what is predictive analytics, what are its benefits and share three use cases.
Machine learning disrupting car insurance industry
n this talk I talk about how machine learning is going to disrupt car insurance industry. #machinelearning
#artificianintelligence #carinsurance
Ethical Artificial Intelligence
This document discusses ethical issues related to artificial intelligence. It notes that nearly half of those polled oppose giving robots emotions or personalities. It also discusses using machine learning for credit scores, the lack of understanding of deep neural networks, reinforcement learning challenges like safe exploration and gaming reward functions. The document calls for ethically aligned design of AI through accountability, transparency, embedding human values, and allowing control over digital identities. However, it acknowledges that current guidelines are not possible given technology limitations.
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Natural language Analysis
- 1. Language Analysis using Machine learning: An overview Rudradeb Mitra
- 2. Bio • AI researcher published 10 research papers on topics like logical reasoning, language analysis, Semantic web. • Masters from University of Cambridge, UK. • Involved with startups since 2010. • Machine learning enthusiast.
- 3. What is machine learning? • Apply previously acquired knowledge to new or novel situation • Search tree, neural network, Bayesian reasoning, logic,… • Boom and AI winter cycle (1974-80, 1980-87) Arthur Samuel with his checker playing machine
- 4. But something is happening recently….. • AlphaGo defeated world Go champion. • AP is going to use machine created news articles for sports coverage • Deep Mind to check NHS eye scans for disease analysis • People have termed it similar to Industrial revolution
- 5. Applications • News articles (AP), robot lawyers, designers (wix), car industry (google, apple), tour guides, rockets…. • Open AI, facebook, google, microsoft, twitter… • ….machine learning will affect all domains…..
- 6. Why now? • Big data - What do we do with it? • Visualize, Analyze - Human element • Machine learning / Deep neural network - Learn from the data
- 7. Language understanding • “A computer would deserve to be called intelligent if it could deceive a human into believing that it is human.” - Alan Turing • Language is the form of communication. • Basic necessity in solving AI problems in language understanding.
- 8. Applications of NLP • Topic modelling • Text summarization • Translation • Sentiment analysis • Image captions and descriptions …
- 9. Historic approaches… • Syntax tree • Semantic - RDF, OWL • LSA - bag of words, similar words appear together. Concepts are represented as patterns of words.
- 10. Latent Dirichlet Analysis • Start with document, bag of words and K topics • Output - Documents are of what topics (in %) • Randomly/semi-randomly assign each word to a topic • All topic assignments except for the current word in question are correct • Improve by reassign ‘w’ a new topic. Choose topic t with probability p(topic t | document d) * p(word w | topic t)
- 11. Neural Networks
- 12. Back propagation
- 13. Deep Neural network • Many layers / neurons • Wide / Deep network • Data + computations power
- 14. CBOW / skip gram
- 15. word2vec
- 16. word2vec • Applications: Sentence auto fill, playlist, genes… • Issues: Local context. Missing global context. • GloVe
- 18. LSTM
- 19. Sentiment Analysis with RNN
- 22. AlphaGo - Building intuition • Took 150,000 games played by good human players and used an artificial neural network to find patterns • Learned to predict with high probability what move a human player would take • Play against itself, to get an estimate of how likely a given board position was to be a winning one - Reinforcement learning • No detailed knowledge of Go. Instead analyzed thousands of prior games and engaged in a lot of self- play.
- 23. Thank You Questions? mitra.rudradeb@gmail.com Feel free to email or add me on linkedin