Recurrent neural networks (RNNs) are well-suited for analyzing text data because they can model sequential and structural relationships in text. RNNs use gating mechanisms like LSTMs and GRUs to address the problem of exploding or vanishing gradients when training on long sequences. Modern RNNs trained with techniques like gradient clipping, improved initialization, and optimized training algorithms like Adam can learn meaningful representations from text even with millions of training examples. RNNs may outperform conventional bag-of-words models on large datasets but require significant computational resources. The author describes an RNN library called Passage and provides an example of sentiment analysis on movie reviews to demonstrate RNNs for text analysis.
Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks can be used for sequence modeling tasks like predicting the next word. RNNs apply the same function to each element of a sequence but struggle with long-term dependencies. LSTMs address this with a gated cell that can maintain information over many time steps by optionally adding, removing, or updating cell state. LSTMs are better for tasks like language modeling since they can remember inputs from much earlier in the sequence. RNNs and LSTMs have applications in areas like music generation, machine translation, and predictive modeling.
This document discusses neural network models for natural language processing tasks like machine translation. It describes how recurrent neural networks (RNNs) were used initially but had limitations in capturing long-term dependencies and parallelization. The encoder-decoder framework addressed some issues but still lost context. Attention mechanisms allowed focusing on relevant parts of the input and using all encoded states. Transformers replaced RNNs entirely with self-attention and encoder-decoder attention, allowing parallelization while generating a richer representation capturing word relationships. This revolutionized NLP tasks like machine translation.
The document provides an overview of Long Short Term Memory (LSTM) networks. It discusses:
1) The vanishing gradient problem in traditional RNNs and how LSTMs address it through gated cells that allow information to persist without decay.
2) The key components of LSTMs - forget gates, input gates, output gates and cell states - and how they control the flow of information.
3) Common variations of LSTMs including peephole connections, coupled forget/input gates, and Gated Recurrent Units (GRUs). Applications of LSTMs in areas like speech recognition, machine translation and more are also mentioned.
This document provides an introduction to deep learning. It defines artificial intelligence, machine learning, data science, and deep learning. Machine learning is a subfield of AI that gives machines the ability to improve performance over time without explicit human intervention. Deep learning is a subfield of machine learning that builds artificial neural networks using multiple hidden layers, like the human brain. Popular deep learning techniques include convolutional neural networks, recurrent neural networks, and autoencoders. The document discusses key components and hyperparameters of deep learning models.
Recurrent Neural Network
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Mohammad Sabouri
https://sites.google.com/view/acrrl/
Recurrent Neural Networks are popular Deep Learning models that have shown great promise to achieve state-of-the-art results in many tasks like Computer Vision, NLP, Finance and much more. Although being models proposed several years ago, RNN have gained popularity recently. In this talk, we will review how these models evolved over the years, dissection of RNN, current applications and its future.
1. Reinforcement learning involves an agent learning through trial-and-error interactions with an environment. The agent learns a policy for how to act by maximizing rewards.
2. The document outlines key elements of reinforcement learning including states, actions, rewards, value functions, and explores different methods for solving reinforcement learning problems including dynamic programming, Monte Carlo methods, and temporal difference learning.
3. Temporal difference learning combines the advantages of Monte Carlo methods and dynamic programming by allowing for incremental learning through bootstrapping predictions like dynamic programming while also learning directly from experience like Monte Carlo methods.
Dr. Subrat Panda gave an introduction to reinforcement learning. He defined reinforcement learning as dealing with agents that must sense and act upon their environment to receive delayed scalar feedback in the form of rewards. He described key concepts like the Markov decision process framework, value functions, Q-functions, exploration vs exploitation, and extensions like deep reinforcement learning. He listed several real-world applications of reinforcement learning and resources for learning more.
Transformer modality is an established architecture in natural language processing that utilizes a framework of self-attention with a deep learning approach.
This presentation was delivered under the mentorship of Mr. Mukunthan Tharmakulasingam (University of Surrey, UK), as a part of the ScholarX program from Sustainable Education Foundation.
This document discusses recurrent neural networks (RNNs) and some of their applications and design patterns. RNNs are able to process sequential data like text or time series due to their ability to maintain an internal state that captures information about what has been observed in the past. The key challenges with training RNNs are vanishing and exploding gradients, which various techniques like LSTMs and GRUs aim to address. RNNs have been successfully applied to tasks involving sequential input and/or output like machine translation, image captioning, and language modeling. Memory networks extend RNNs with an external memory component that can be explicitly written to and retrieved from.
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
This Edureka Recurrent Neural Networks tutorial will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next word using a sample short story
Below are the topics covered in this tutorial:
1. Why Not Feedforward Networks?
2. What Are Recurrent Neural Networks?
3. Training A Recurrent Neural Network
4. Issues With Recurrent Neural Networks - Vanishing And Exploding Gradient
5. Long Short-Term Memory Networks (LSTMs)
6. LSTM Use-Case
The document discusses the BERT model for natural language processing. It begins with an introduction to BERT and how it achieved state-of-the-art results on 11 NLP tasks in 2018. The document then covers related work on language representation models including ELMo and GPT. It describes the key aspects of the BERT model, including its bidirectional Transformer architecture, pre-training using masked language modeling and next sentence prediction, and fine-tuning for downstream tasks. Experimental results are presented showing BERT outperforming previous models on the GLUE benchmark, SQuAD 1.1, SQuAD 2.0, and SWAG. Ablation studies examine the importance of the pre-training tasks and the effect of model size.
The document discusses recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It provides details on the architecture of RNNs including forward and back propagation. LSTMs are described as a type of RNN that can learn long-term dependencies using forget, input and output gates to control the cell state. Examples of applications for RNNs and LSTMs include language modeling, machine translation, speech recognition, and generating image descriptions.
Recurrent neural networks (RNNs) are a type of artificial neural network that can process sequential data of varying lengths. Unlike traditional neural networks, RNNs maintain an internal state that allows them to exhibit dynamic temporal behavior. RNNs take the output from the previous step and feed it as input to the current step, making the network dependent on information from earlier steps. This makes RNNs well-suited for applications like text generation, machine translation, image captioning, and more. RNNs can remember information for long periods of time but are difficult to train due to issues like vanishing gradients.
Recursive neural networks (RNNs) were developed to model recursive structures like images, sentences, and phrases. RNNs construct feature representations recursively from components. Later models like recursive autoencoders (RAEs), matrix-vector RNNs (MV-RNNs), and recursive neural tensor networks (RNTNs) improved on RNNs by handling unlabeled data, incorporating different composition rules, and reducing parameters. These recursive models achieved strong performance on tasks like image segmentation, sentiment analysis, and paraphrase detection.
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
This document discusses computational intelligence and supervised learning techniques for classification. It provides examples of applications in medical diagnosis and credit card approval. The goal of supervised learning is to learn from labeled training data to predict the class of new unlabeled examples. Decision trees and backpropagation neural networks are introduced as common supervised learning algorithms. Evaluation methods like holdout validation, cross-validation and performance metrics beyond accuracy are also summarized.
Reinforcement learning is a machine learning technique that involves trial-and-error learning. The agent learns to map situations to actions by trial interactions with an environment in order to maximize a reward signal. Deep Q-networks use reinforcement learning and deep learning to allow agents to learn complex behaviors directly from high-dimensional sensory inputs like pixels. DQN uses experience replay and target networks to stabilize learning from experiences. DQN has achieved human-level performance on many Atari 2600 games.
This document provides an outline for a presentation on machine learning and deep learning. It begins with an introduction to machine learning basics and types of learning. It then discusses what deep learning is and why it is useful. The main components and hyperparameters of deep learning models are explained, including activation functions, optimizers, cost functions, regularization methods, and tuning. Basic deep neural network architectures like convolutional and recurrent networks are described. An example application of relation extraction is provided. The document concludes by listing additional deep learning topics.
Deep learning is a subset of machine learning and AIleradiophysicien1
intelligence (AI) that focuses on using neural networks with many layers to model complex patterns in data. Inspired by the structure and function of the human brain, deep learning algorithms are designed to automatically learn representations of data at multiple levels of abstraction. This allows them to excel in tasks such as image and speech recognition, natural language processing, and autonomous driving. The rapid advancements in computational power and the availability of large datasets have significantly contributed to the success of deep learning. By leveraging massive amounts of data and powerful GPUs, deep learning models can achieve remarkable accuracy and efficiency, making them an integral part of modern AI applications.
Covers basics Artificial neural networks and motivation for deep learning and explains certain deep learning networks, including deep belief networks and autoencoders. It also details challenges of implementing a deep learning network at scale and explains how we have implemented a distributed deep learning network over Spark.
This document provides an overview of deep learning basics for natural language processing (NLP). It discusses the differences between classical machine learning and deep learning, and describes several deep learning models commonly used in NLP, including neural networks, recurrent neural networks (RNNs), encoder-decoder models, and attention models. It also provides examples of how these models can be applied to tasks like machine translation, where two RNNs are jointly trained on parallel text corpora in different languages to learn a translation model.
This document discusses different methods for document classification using natural language processing and deep learning. It presents the steps for document classification using machine learning, including data preprocessing, feature engineering, model selection and training, and testing. The document tests several models on a news article dataset, including naive bayes, logistic regression, random forest, XGBoost, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). CNNs achieved the highest accuracy at 91%, and using word embeddings provided additional improvements. While classical models provided good accuracy, neural network models improved it further.
- The document discusses deep learning and Apache MXNet. It provides an overview of neural networks and their history, the training process using backpropagation and stochastic gradient descent, and techniques like convolutional neural networks.
- It then introduces Apache MXNet, describing it as programmable, portable, high performance, supporting multiple languages and optimized for deep learning on AWS.
- Finally, it proposes some examples of coding with MXNet, from basic networks to pre-trained models, training from scratch, fine-tuning, and using MXNet as a Keras backend.
Deep Dive on Deep Learning (June 2018)Julien SIMON
This document provides a summary of a presentation on deep learning concepts, common architectures, Apache MXNet, and infrastructure for deep learning. The agenda includes an overview of deep learning concepts like neural networks and training, common architectures like convolutional neural networks and LSTMs, a demonstration of Apache MXNet's symbolic and imperative APIs, and a discussion of infrastructure for deep learning on AWS like optimized EC2 instances and Amazon SageMaker.
This document discusses different approaches for building chatbots, including retrieval-based and generative models. It describes recurrent neural networks like LSTMs and GRUs that are well-suited for natural language processing tasks. Word embedding techniques like Word2Vec are explained for representing words as vectors. Finally, sequence-to-sequence models using encoder-decoder architectures are presented as a promising approach for chatbots by using a context vector to generate responses.
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017MLconf
Corinna Cortes is a Danish computer scientist known for her contributions to machine learning. She is currently the Head of Google Research, New York. Cortes is a recipient of the Paris Kanellakis Theory and Practice Award for her work on theoretical foundations of support vector machines.
Cortes received her M.S. degree in physics from Copenhagen University in 1989. In the same year she joined AT&T Bell Labs as a researcher and remained there for about ten years. She received her Ph.D. in computer science from the University of Rochester in 1993. Cortes currently serves as the Head of Google Research, New York. She is an Editorial Board member of the journal Machine Learning.
Cortes’ research covers a wide range of topics in machine learning, including support vector machines and data mining. In 2008, she jointly with Vladimir Vapnik received the Paris Kanellakis Theory and Practice Award for the development of a highly effective algorithm for supervised learning known as support vector machines (SVM). Today, SVM is one of the most frequently used algorithms in machine learning, which is used in many practical applications, including medical diagnosis and weather forecasting.
Abstract Summary:
Harnessing Neural Networks:
Deep learning has demonstrated impressive performance gain in many machine learning applications. However, unveiling and realizing these performance gains is not always straightforward. Discovering the right network architecture is critical for accuracy and often requires a human in the loop. Some network architectures occasionally produce spurious outputs, and the outputs have to be restricted to meet the needs of an application. Finally, realizing the performance gain in a production system can be difficult because of extensive inference times.
In this talk we discuss methods for making neural networks efficient in production systems. We also discuss an efficient method for automatically learning the network architecture, called AdaNet. We provide theoretical arguments for the algorithm and present experimental evidence for its effectiveness.
Training at AI Frontiers 2018 - Lukasz Kaiser: Sequence to Sequence Learning ...AI Frontiers
This document discusses sequence to sequence learning with Tensor2Tensor (T2T) and sequence models. It provides an overview of T2T, which is a library for deep learning models and datasets. It discusses basics of sequence models including recurrent neural networks (RNNs), convolutional models, and the Transformer model based on attention. It encourages experimenting with different sequence models and datasets in T2T.
This talk was presented in Startup Master Class 2017 - http://aaiitkblr.org/smc/ 2017 @ Christ College Bangalore. Hosted by IIT Kanpur Alumni Association and co-presented by IIT KGP Alumni Association, IITACB, PanIIT, IIMA and IIMB alumni.
My co-presenter was Biswa Gourav Singh. And contributor was Navin Manaswi.
http://dataconomy.com/2017/04/history-neural-networks/ - timeline for neural networks
Accelerating stochastic gradient descent using adaptive mini batch size3muayyad alsadi
This document proposes a method called Train-Measure-Adapt-Repeat for accelerating stochastic gradient descent training of deep neural networks using adaptive mini-batch sizes. The method starts with an extremely small mini-batch size, such as 4-8 samples, to allow for faster training initially through more frequent weight updates. Accuracy is evaluated over time rather than by the number of steps, and the mini-batch size is increased adaptively when accuracy improvements stall. Experiments on image classification datasets demonstrate the method reaching higher accuracy levels faster than using fixed large mini-batch sizes.
Objective of the Project
Tweet sentiment analysis gives businesses insights into customers and competitors. In this project, we combined several text preprocessing techniques with machine learning algorithms. Neural network, Random Forest and Logistic Regression models were trained on the Sentiment140 twitter data set. We then predicted the sentiment of a hold-out test set of tweets. We used both Python and PySpark (local Spark Context) to program different parts of the pre-processing and modelling.
The document outlines an agenda for a Tensorflow basics workshop. It includes an opening speech on Tesla AI, an introduction to key concepts like neural networks and machine learning workflows. The bulk of the workshop involves coding sessions where participants will build a classification model in Tensorflow and get help from instructors. It concludes with information on continuing self-directed learning through online resources and a preview of an upcoming computer vision lesson.
BERT- Pre-training of Deep Bidirectional Transformers for Language Understand...Kyuri Kim
BERT achieves state-of-the-art results on 11 NLP tasks through pre-training a deep bidirectional transformer on two unsupervised tasks: masked language modeling and next sentence prediction. It pre-trains on a large corpus of 3.3 billion words before fine-tuning the entire model for specific downstream tasks. Experiments show that increasing the model size and pre-training data leads to improved performance, and BERT can be applied through either fine-tuning or as a feature extractor. The core idea is that pre-training a large bidirectional model on vast amounts of unlabeled text learns powerful general-purpose language representations.
Deep learning and Watson Studio can be used for various tasks including planet discoveries, particle physics experiments at CERN, and scientific publications analysis. Convolutional neural networks are commonly used for image-related tasks like cancer diagnosis, object detection, and style transfer, while recurrent neural networks with LSTM or GRU are useful for sequential data like text for machine translation, sentiment analysis, and music generation. Hybrid and complex models combine different neural network architectures for tasks such as named entity recognition, music generation, blockchain security, and lip reading. Deep learning is now implemented using frameworks like TensorFlow and Keras on GPUs and distributed systems. Transfer learning helps accelerate development by reusing pre-trained models. Watson Studio provides a platform for developing, testing, and deploy
Distributed Deep Learning on Hadoop
Deep-learning is useful in detecting anomalies like fraud, spam and money laundering; identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
Similar to Recurrent Neural Networks for Text Analysis (20)
As the importance of having a data strategy in place is sinking in, many organizations have added a chief data officer (CDO) to their executive team to help create and implement that strategy. But every organization is doing this a little bit differently. This talk will describe how a variety of industries and organizations are using CDOs and will make recommendations for best practices.
I’ll present the new knowledge discovery tools we are building at Diffeo. Unlike traditional search engines that use keywords, Diffeo provides an in-browser knowledge base that accelerates information gathering about people, companies, chemical compounds, cyber events, or other real world entities. I’ll describe how Diffeo uses active learning to encourage long and deep user interactions in order to recommend new content for in-progress articles. As you write, the search results get better and more interesting, because the system can see more precisely which entity you mean and which you don’t (disambiguation) and also what you don’t know yet about the entity (discovery).
Finally in this presentation I’ll describe our experience organizing the Text REtrieval Conference (TREC) on Knowledge Base Acceleration (KBA) and Dynamic Domain (DD) which are pushing the state of the art in knowledge discovery on large streams. I’ll show you how to access the largest corpus of streaming text data ever released for public evaluations.
An exposé on human-centered design, as related to data science and “medium data”. Examples of great API design will be showcased, as well as other end-user facing tools that can enable data scientists to share their observations with the world.
Mobile technology Usage by Humanitarian Programs: A Metadata Analysisodsc
This document summarizes an analysis of metadata from the CommCare mobile data platform, which is used by hundreds of humanitarian programs worldwide. The analysis examined CommCare usage data to better understand how frontline workers and programs utilize the technology. Key findings included: 1) Frontline workers develop proficiency quickly in their first year but then level off; 2) Worker activity levels remain relatively stable month-to-month; 3) Worker productivity does not follow a normal distribution. The metadata analysis can help programs monitor performance and identify factors that lead to improvement over time.
Productionizing Deep Learning From the Ground Upodsc
This document discusses productionizing deep learning from the ground up. It begins with an overview of deep learning and neural networks, explaining that deep learning performs pattern recognition on unlabeled and unstructured data using deep neural networks with three or more layers. It then discusses challenges like the computational intensity of deep learning models and the need for special hardware like GPUs. It also covers software engineering concerns in scaling deep learning to production, such as data pipelines, maintenance of GPU clusters, and different types of parallelism in deep learning models and data.
Big Data Infrastructure: Introduction to Hadoop with MapReduce, Pig, and Hiveodsc
The main objective of this workshop is to give the audience hands on experience with several Hadoop technologies and jump start their hadoop journey. In this workshop, you will load data and submit queries using Hadoop! Before jumping in to the technology, the Founders of DataKitchen review Hadoop and some of its technologies (MapReduce, Hive, Pig, Impala and Spark), look at performance, and present a rubric for choosing which technology to use when.
We’ve all been told to “work smarter, not harder.” But what does working smarter really mean? In the world of finance and trading, working smarter means working differently. None of us can compete against computers stacked inches away from the stock exchange or blue chip companies with multi-million dollar marketing campaigns. The key to winning is to go where the big guys haven’t and the way to do that is through diverse datasets. In this talk, you will discover the theory and tools to discover new datasets from unexpected sources in order to gain an upper-hand in both finance and business. So whether you’re a quant that trades in his bedroom or a restaurateur looking to grow his business, you’ll learn how the diversity of data can be the sharpest knife if your set.
Data Science at Dow Jones: Monetizing Data, News and Informationodsc
In this presentation I will describe the way Data Science supports the business of information and news at Dow Jones. Specifically, I will describe how we are introducing innovative and advanced large-scale information mining and analytic approaches not only into Dow Jones’ products but also into our strategy and decision making processes.Our goal is to impact every aspect of Dow Jones: from the way journalism is produced in the newsroom, to the way we create and deliver institutional products, to the way we improve retention and acquisition of subscribers. While the task seems broad and daunting, we have already achieved various successes through the application of machine learning, data mining, advanced analytics and big data approaches.In this presentation I will describe how we have achieved this, including our tools, data, approaches and mechanisms as well as describe what our plans are going forward.
Have you been in the situation where you’re about to start a new project and ask yourself, what’s the right tool for the job here? I’ve been in that situation many times and thought it might be useful to share with you a recent project we did and why we selected Spark, Python, and Parquet. My plan is take you through a use case that involves loading, transforming, aggregating, and persisting the dataset. We’ll use an open dataset consisting of full fund holdings graciously provided by Morningstar. My goal in presenting this use case are to have the audience learn about how these technologies can be applied to a real world problem and to inspire members of the audience to start learning these technologies and applying them to their own projects.
Building a Predictive Analytics Solution with Azure MLodsc
Create and operationalize a predictive model using Microsoft Azure Machine Learning.
– Perform the typical steps involved in building a predictive analytics solution such as data ingestion, data cleansing, data exploration, feature engineering, model selection and evaluation of model results
–learn how to use machine learning with big data scenarios using tools like Hadoop and SQL Server to process and work with such data.
Finding and classifying the mentions of the things named in text, often called Named Entity Recognition or NER, is a fundamental task in many search and analysis applications. Mature, robust NER technology is available for many languages and domains, from people, places, and products, to diseases, genes, and molecules. However, for emerging tasks like knowledge-base construction, mentions alone are insufficient.
In this presentation we’ll explore techniques that go beyond names to:
link mentions to one another and to rich knowledge sources like Wikidata
discover and characterise the relationships between entities that are explicit in the text
And we’ll discuss some of the most important practical implications of these advancements for open data science.
According to Credit Suisse’s Gender 3000 report, at the end of 2013, women accounted for 12.9% of top management in 3000 companies across 40 countries. However, since 2009, companies with women as 25-50% of their management team
returned 22-29%. If companies with women in management outperform so dramatically, what would happen if you invested in women-led companies? Karen Rubin will explore this question and share her findings after running a 12 year investment simulation.
Data science allows us to turn a dark forest into a world of
perpetual twilight by giving us the tools to better understand the data that surrounds us. Unfortunately, in this world of twilight we still need a flashlight to get a clean crisp image of our immediate surroundings. We will talk about how to use deep domain expertise as that flashlight shedding light on our understanding of data. Our focus will be on using text analysis as a means to examine qualitative information in a structured, quantitative way. We will draw heavily from examples in complex central bank policy and financial regulation.
Kaggle is a platform for data science competitions that has hosted hundreds of challenges on topics ranging from flight routing to predicting molecular activity. It has a community of over 320,000 data scientists who submit over 100,000 machine learning models per month. Through competitions, Kaggle has helped companies like Allstate, Merck, and Mayo Clinic solve real-world problems by crowdsourcing data science solutions.
Open Source Tools & Data Science Competitions odsc
This talk shares the presenter’s experience with open source tools in data science competitions. In the past several years Kaggle and other competitions have created a large online community of data scientists. In addition to competing with each other for fame and glory, members of this community also generously share knowledge, insights using forum and open source code. The open competition and sharing have resulted in rapid progress in the sophistication of the entire community. This presentation will briefly cover this journey from a competitor’s perspective, and share hands on tips on some open source tools proven popular and useful in recent competitions.
scikit-learn has emerged as one of the most popular open source machine learning toolkits, now widely used in academia and industry.
scikit-learn provides easy-to-use interfaces to perform advanced analysis and build powerful predictive models.
The tutorial will cover basic concepts of machine learning, such as supervised and unsupervised learning, cross validation, and model selection. We will see how to prepare data for machine learning, and go from applying a single algorithm to building a machine learning pipeline.
We will also cover how to build machine learning models on text data, and how to handle very large datasets.
Bridging the Gap Between Data and Insight using Open-Source Toolsodsc
Despite the proliferation of open-source tools for analysis (such as Python and R) and those used for visualization
(such as Javascript / D3), there often exist significant gaps between these areas, and those of us trying to navigate the complete arc from data to insight can encounter many obstacles along the way. Fortunately, in recent years there have been many efforts to fill these needs, and today distilling a meaningful visualization from raw data is faster and easier than ever before.
In this talk we will use will use examples in geospatial analysis and visualization to illustrate how to open-source tools like Python, geopandas, and TileMill work together. Using examples from the RunKeeper mobile app we will show how we currently use these tools to understand better our customers and their data, and to communicate
with our colleagues, external partners, and the data community at large.
Human-generated text may be the next frontier for big data analysis, but we humans are complicated beasts and the text we generate is messy and complicated in ways that can confound analysis. We’ll describe the top ten mistakes people make when they start doing text analysis, and hopefully save you from making a few of these mistakes yourself.
The document is a presentation by Josh Wills from Cloudera on data science. It discusses defining data science and the roles of data scientists. It also covers challenges working with big data, including data modeling and addressing the impedance mismatch between operational and analytical systems. The presentation promotes open data science and sharing examples on GitHub to push beyond the limits of current tools.
The document discusses open data science research topics presented at a conference, including opportunities and challenges with learning analytics and adaptive learning using open data. It describes how learning analytics can help achieve large improvements in student outcomes through targeted feedback and personalized learning paths. An open analytics architecture is proposed to integrate different data sources and applications using common data standards.
The Challenge of Interpretability in Generative AI Models.pdfSara Kroft
Navigating the intricacies of generative AI models reveals a pressing challenge: interpretability. Our blog delves into the complexities of understanding how these advanced models make decisions, shedding light on the mechanisms behind their outputs. Explore the latest research, practical implications, and ethical considerations, as we unravel the opaque processes that drive generative AI. Join us in this insightful journey to demystify the black box of artificial intelligence.
Dive into the complexities of generative AI with our blog on interpretability. Find out why making AI models understandable is key to trust and ethical use and discover current efforts to tackle this big challenge.
Keynote : Presentation on SASE TechnologyPriyanka Aash
Secure Access Service Edge (SASE) solutions are revolutionizing enterprise networks by integrating SD-WAN with comprehensive security services. Traditionally, enterprises managed multiple point solutions for network and security needs, leading to complexity and resource-intensive operations. SASE, as defined by Gartner, consolidates these functions into a unified cloud-based service, offering SD-WAN capabilities alongside advanced security features like secure web gateways, CASB, and remote browser isolation. This convergence not only simplifies management but also enhances security posture and application performance across global networks and cloud environments. Discover how adopting SASE can streamline operations and fortify your enterprise's digital transformation strategy.
Generative AI technology is a fascinating field that focuses on creating comp...Nohoax Kanont
Generative AI technology is a fascinating field that focuses on creating computer models capable of generating new, original content. It leverages the power of large language models, neural networks, and machine learning to produce content that can mimic human creativity. This technology has seen a surge in innovation and adoption since the introduction of ChatGPT in 2022, leading to significant productivity benefits across various industries. With its ability to generate text, images, video, and audio, generative AI is transforming how we interact with technology and the types of tasks that can be automated.
DefCamp_2016_Chemerkin_Yury-publish.pdf - Presentation by Yury Chemerkin at DefCamp 2016 discussing mobile app vulnerabilities, data protection issues, and analysis of security levels across different types of mobile applications.
Welcome to Cyberbiosecurity. Because regular cybersecurity wasn't complicated...Snarky Security
How wonderful it is that in our modern age, every bit of our biological data can be digitized, stored, and potentially pilfered by cyber thieves! Isn't it just splendid to think that while scientists are busy pushing the boundaries of biotechnology, hackers could be plotting the next big bio-data heist? This delightful scenario is brought to you by the ever-expanding digital landscape of biology and biotechnology, where the integration of computer science, engineering, and data science transforms our understanding and manipulation of biological systems.
While the fusion of technology and biology offers immense benefits, it also necessitates a careful consideration of the ethical, security, and associated social implications. But let's be honest, in the grand scheme of things, what's a little risk compared to potential scientific achievements? After all, progress in biotechnology waits for no one, and we're just along for the ride in this thrilling, slightly terrifying, adventure.
So, as we continue to navigate this complex landscape, let's not forget the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. After all, what could possibly go wrong?
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This document provides a comprehensive analysis of the security implications biological data use. The analysis explores various aspects of biological data security, including the vulnerabilities associated with data access, the potential for misuse by state and non-state actors, and the implications for national and transnational security. Key aspects considered include the impact of technological advancements on data security, the role of international policies in data governance, and the strategies for mitigating risks associated with unauthorized data access.
This view offers valuable insights for security professionals, policymakers, and industry leaders across various sectors, highlighting the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. The analysis serves as a crucial resource for understanding the complex dynamics at the intersection of biotechnology and security, providing actionable recommendations to enhance biosecurity in an digital and interconnected world.
The evolving landscape of biology and biotechnology, significantly influenced by advancements in computer science, engineering, and data science, is reshaping our understanding and manipulation of biological systems. The integration of these disciplines has led to the development of fields such as computational biology and synthetic biology, which utilize computational power and engineering principles to solve complex biological problems and innovate new biotechnological applications. This interdisciplinary approach has not only accelerated research and development but also introduced new capabilities such as gene editing and biomanufact
Increase Quality with User Access Policies - July 2024Peter Caitens
⭐️ Increase Quality with User Access Policies ⭐️, presented by Peter Caitens and Adam Best of Salesforce. View the slides from this session to hear all about “User Access Policies” and how they can help you onboard users faster with greater quality.
The Zaitechno Handheld Raman Spectrometer is a powerful and portable tool for rapid, non-destructive chemical analysis. It utilizes Raman spectroscopy, a technique that analyzes the vibrational fingerprint of molecules to identify their chemical composition. This handheld instrument allows for on-site analysis of materials, making it ideal for a variety of applications, including:
Material identification: Identify unknown materials, minerals, and contaminants.
Quality control: Ensure the quality and consistency of raw materials and finished products.
Pharmaceutical analysis: Verify the identity and purity of pharmaceutical compounds.
Food safety testing: Detect contaminants and adulterants in food products.
Field analysis: Analyze materials in the field, such as during environmental monitoring or forensic investigations.
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4. How ML
-0.15, 0.2, 0, 1.5
A, B, C, D
The cat sat on the
mat.
Numerical, great!
Categorical, great!
Uhhh…….
5. How text is dealt with
(ML perspective)
Text
Features
(bow, TFIDF, LSA, etc...)
Linear Model
(SVM, softmax)
6. Structure is important!
The cat sat on the mat.
sat the on mat cat the
● Certain tasks, structure is essential:
○ Humor
○ Sarcasm
● Certain tasks, ngrams can get you a
long way:
○ Sentiment Analysis
○ Topic detection
● Specific words can be strong indicators
○ useless, fantastic (sentiment)
○ hoop, green tea, NASDAQ (topic)
7. Structure is hard
Ngrams is typical way of preserving some structure.
sat
the on
mat
cat
the cat cat sat sat on
on thethe mat
Beyond bi or tri-grams occurrences become very rare and
dimensionality becomes huge (1, 10 million + features)
12. How an RNN works
the cat sat on the mat
input to hidden
13. How an RNN works
the cat sat on the mat
input to hidden
hidden to hidden
14. How an RNN works
the cat sat on the mat
input to hidden
hidden to hidden
15. How an RNN works
the cat sat on the mat
projections
(activities x weights)
activities
(vectors of values)
input to hidden
hidden to hidden
16. How an RNN works
the cat sat on the mat
projections
(activities x weights)
activities
(vectors of values)
Learned representation of
sequence.
input to hidden
hidden to hidden
17. How an RNN works
the cat sat on the mat
projections
(activities x weights)
activities
(vectors of values)
cat
hidden to output
input to hidden
hidden to hidden
18. From text to RNN input
the cat sat on the mat
“The cat sat on the mat.”
Tokenize .
Assign index 0 1 2 3 0 4 5
String input
Embedding lookup 2.5 0.3 -1.2 0.2 -3.3 0.7 -4.1 1.6 2.8 1.1 5.7 -0.2 2.5 0.3 -1.2 1.4 0.6 -3.9 -3.8 1.5 0.1
2.5 0.3 -1.2
0.2 -3.3 0.7
-4.1 1.6 2.8
1.1 5.7 -0.2
1.4 0.6 -3.9
-3.8 1.5 0.1
Learned matrix
19. You can stack them too
the cat sat on the mat
cat
hidden to output
input to hidden
hidden to hidden
20. But aren’t RNNs unstable?
Simple RNNs trained with SGD are unstable/difficult to learn.
But modern RNNs with various tricks blow up much less often!
● Gating Units
● Gradient Clipping
● Steeper gates
● Better initialization
● Better optimizers
● Bigger datasets
21. Simple Recurrent Unit
ht-1
xt
+ ht
xt+1
+ ht+1
+ Element wise addition
Activation function
Routes information can propagate along
Involved in modifying information flow and
values
22. ⊙
⊙⊙
Gated Recurrent Unit - GRU
xt
r
htht-1 ht
z
+
~
1-z z
+ Element wise addition
⊙ Element wise multiplication
Routes information can propagate along
Involved in modifying information flow and
values
23. Gated Recurrent Unit - GRU
⊙
⊙⊙
xt
r
htht-1
z
+
~
1-z z
⊙
⊙⊙
xt+1
r
ht+1ht
z
+
~
1-z z
ht+1
24. Gating is important
For sentiment analysis of longer
sequences of text (paragraph or so)
a simple RNN has difficulty learning
at all while a gated RNN does so
easily.
25. Which One?
There are two types of gated RNNs:
● Gated Recurrent Units (GRU) by
K. Cho, recently introduced and
used for machine translation and
speech recognition tasks.
● Long short term memory (LSTM)
by S. Hochreiter and J.
Schmidhuber has been around
since 1997 and has been used
far more. Various modifications
to it exist.
26. Which One?
GRU is simpler, faster, and optimizes
quicker (at least on sentiment).
Because it only has two gates
(compared to four) approximately 1.5-
1.75x faster for theano
implementation.
If you have a huge dataset and don’t
mind waiting LSTM may be better in
the long run due to its greater
complexity - especially if you add
peephole connections.
27. Exploding Gradients?
Exploding gradients are a major problem
for traditional RNNs trained with SGD.
One of the sources of the reputation of
RNNs being hard to train.
In 2012, R Pascanu and T. Mikolov
proposed clipping the norm of the gradient
to alleviate this.
Modern optimizers don’t seem to have this
problem - at least for classification text
analysis.
28. Better Gating Functions
Interesting paper at NIPS workshop (Q. Lyu, J. Zhu) - make the gates “steeper” so
they change more rapidly from “off” to “on” so model learns to use them quicker.
29. Better Initialization
Andrew Saxe last year showed that initializing weight matrices with random
orthogonal matrices works better than random gaussian (or uniform) matrices.
In addition, Richard Socher (and more recently Quoc Le) have used identity
initialization schemes which work great as well.
31. Comparing Optimizers
Adam (D. Kingma) combines the
early optimization speed of
Adagrad (J. Duchi) with the better
later convergence of various other
methods like Adadelta (M. Zeiler)
and RMSprop (T. Tieleman).
Warning: Generalization
performance of Adam seems
slightly worse for smaller datasets.
32. It adds up
Up to 10x more efficient training once you
add all the tricks together compared to a
naive implementation - much more stable
- rarely diverges.
Around 7.5x faster, the various tricks add
a bit of computation time.
33. Too much? - Overfitting
RNNs can overfit very well as we will
see. As they continue to fit to training
dataset, their performance on test data
will plateau or even worsen.
Keep track of it using a validation set,
save model at each iteration over
training data and pick the earliest, best,
validation performance.
34. The Showdown
Model #1 Model #2
+ 512 dim
embedding
512 dim
hidden state
output
Using bigrams and grid search on min_df for
vectorizer and regularization coefficient for model.
Using whatever I tried that worked :)
Adam, GRU, steeper sigmoid gates, ortho/identity
36. Effect of Dataset Size
● RNNs have poor generalization properties on small
datasets.
○ 1K labeled examples 25-50% worse than linear model…
● RNNs have better generalization properties on large
datasets.
○ 1M labeled examples 0-30% better than linear model.
● Crossovers between 10K and 1M examples
○ Depends on dataset.
37. The Thing we don’t talk about
For 1 million paragraph sized text examples to converge:
● Linear model takes 30 minutes on a single CPU core.
● RNN takes 90 minutes on a Titan X.
● RNN takes five days on a single CPU core.
RNN is about 250x slower on CPU than linear model…
This is why we use GPUs
40. Quantities of TimeQualifiers
Product nouns
Punctuation
Much cooler, model also begins to learn components of language from only binary sentiment labels
41. The library - Passage
● Tiny RNN library built on top of Theano
● https://github.com/IndicoDataSolutions/Passage
● Still alpha - we’re working on it!
● Supports simple, LSTM, and GRU recurrent layers
● Supports multiple recurrent layers
● Supports deep input to and deep output from hidden layers
○ no deep transitions currently
● Supports embedding and onehot input representations
● Can be used for both regression and classification problems
○ Regression needs preprocessing for stability - working on it
● Much more in the pipeline
53. Summary
● RNNs look to be a competitive tool in certain situations
for text analysis.
● Especially if you have a large 1M+ example dataset
o A GPU or great patience is essential
● Otherwise it can be difficult to justify over linear models
o Speed
o Complexity
o Poor generalization with small datasets