Using Deep Learning on Apache Spark to Diagnose Thoracic Pathology from Chest...Databricks
Overview and extended description: AI is expected to be the engine of technological advancements in the healthcare industry, especially in the areas of radiology and image processing. The purpose of this session is to demonstrate how we can build a AI-based Radiologist system using Apache Spark and Analytics Zoo to detect pneumonia and other diseases from chest x-ray images. The dataset, released by the NIH, contains around 110,00 X-ray images of around 30,000 unique patients, annotated with up to 14 different thoracic pathology labels. Stanford University developed a state-of-the-art model using CNN and exceeds average radiologist performance on the F1 metric. This talk focuses on how we can build a multi-label image classification model in a distributed Apache Spark infrastructure, and demonstrate how to build complex image transformations and deep learning pipelines using BigDL and Analytics Zoo with scalability and ease of use. Some practical image pre-processing procedures and evaluation metrics are introduced. We will also discuss runtime configuration, near-linear scalability for training and model serving, and other general performance topics.
This document summarizes an presentation about OpenML, an online platform for sharing machine learning data and experiments. OpenML allows users to search datasets, build machine learning models using various tools/APIs, run experiments on tasks, and automatically upload results. This facilitates reproducibility, benchmarking, and reuse of prior work. OpenML also aims to advance automated machine learning through meta-learning techniques that leverage the large amount of shared data and experiments.
Constrained Optimization with Genetic Algorithms and Project BonsaiIvo Andreev
Traditional machine learning requires volumes of labelled data that can be time consuming and expensive to produce,”
“Machine teaching leverages the human capability to decompose and explain concepts to train machine learning models
direction (teaching the correct answer is not by showing the data for it, but by using a person to show the answer).
Project Bonsai is a low code platform for intelligent solutions but with a different perspective on data it allows a completely new approach to tasks, especially when the physical world is involved. Under the hood it combines machine teaching, calibration and optimization to create intelligent control systems using simulations. The teaching curriculum is performed using a new language concept - “Inkling” and training a model is easy and interactive.
Emotion recognition using image processing in deep learningvishnuv43
User’s emotion using its facial expressions will be detected. These expressions can be derived from the live feed via system's camera or any pre-existing image available in the memory. Emotions possessed by humans can be recognized and has a vast scope of study in the computer vision industry upon which several researches have already been done.
We propose a compact CNN model for facial expression recognition.
The work has been implemented using Python Open Source Computer Vision Library (OpenCV) and NumPy,pandas,keras packages. The scanned image (testing dataset) is being compared to training dataset and thus emotion is predicted.
State of the art time-series analysis with deep learning by Javier Ordóñez at...Big Data Spain
Time series related problems have traditionally been solved using engineered features obtained by heuristic processes.
https://www.bigdataspain.org/2017/talk/state-of-the-art-time-series-analysis-with-deep-learning
Big Data Spain 2017
November 16th - 17th
The document discusses high performance computing and the path towards exascale systems. It covers key application requirements in areas like cancer research, climate modeling, and materials science. Technological challenges for exascale include power and resilience issues. The US Department of Energy is funding several exascale development programs through 2020, including the CANDLE project applying deep learning to precision cancer medicine. Reaching exascale will enable new capabilities in big data analytics, machine learning, and commercial applications.
In this talk, an overview of current trends in machine learning will be discussed with an emphasize on challenges and opportunities facing this field. It will focus on deep learning methods and applications. Deep learning has emerged as one of the most promising research fields in artificial intelligence. The significant advancements that deep learning methods have brought about for large scale image classification tasks have generated a surge of excitement in applying the techniques to other problems in computer vision and more broadly into other disciplines of computer science. Moreover, the impact of machine learning on education, research, and economy will be briefly presented. The rapid growth of machine learning is positioned to impact our lives in a way that we have not been able to fully imagine. It behooves government leaders to take a lead in developing the necessary resources to ride the projected benefits of machine learning.
Using Deep Learning on Apache Spark to Diagnose Thoracic Pathology from Chest...Databricks
Overview and extended description: AI is expected to be the engine of technological advancements in the healthcare industry, especially in the areas of radiology and image processing. The purpose of this session is to demonstrate how we can build a AI-based Radiologist system using Apache Spark and Analytics Zoo to detect pneumonia and other diseases from chest x-ray images. The dataset, released by the NIH, contains around 110,00 X-ray images of around 30,000 unique patients, annotated with up to 14 different thoracic pathology labels. Stanford University developed a state-of-the-art model using CNN and exceeds average radiologist performance on the F1 metric. This talk focuses on how we can build a multi-label image classification model in a distributed Apache Spark infrastructure, and demonstrate how to build complex image transformations and deep learning pipelines using BigDL and Analytics Zoo with scalability and ease of use. Some practical image pre-processing procedures and evaluation metrics are introduced. We will also discuss runtime configuration, near-linear scalability for training and model serving, and other general performance topics.
This document summarizes an presentation about OpenML, an online platform for sharing machine learning data and experiments. OpenML allows users to search datasets, build machine learning models using various tools/APIs, run experiments on tasks, and automatically upload results. This facilitates reproducibility, benchmarking, and reuse of prior work. OpenML also aims to advance automated machine learning through meta-learning techniques that leverage the large amount of shared data and experiments.
Constrained Optimization with Genetic Algorithms and Project BonsaiIvo Andreev
Traditional machine learning requires volumes of labelled data that can be time consuming and expensive to produce,”
“Machine teaching leverages the human capability to decompose and explain concepts to train machine learning models
direction (teaching the correct answer is not by showing the data for it, but by using a person to show the answer).
Project Bonsai is a low code platform for intelligent solutions but with a different perspective on data it allows a completely new approach to tasks, especially when the physical world is involved. Under the hood it combines machine teaching, calibration and optimization to create intelligent control systems using simulations. The teaching curriculum is performed using a new language concept - “Inkling” and training a model is easy and interactive.
Emotion recognition using image processing in deep learningvishnuv43
User’s emotion using its facial expressions will be detected. These expressions can be derived from the live feed via system's camera or any pre-existing image available in the memory. Emotions possessed by humans can be recognized and has a vast scope of study in the computer vision industry upon which several researches have already been done.
We propose a compact CNN model for facial expression recognition.
The work has been implemented using Python Open Source Computer Vision Library (OpenCV) and NumPy,pandas,keras packages. The scanned image (testing dataset) is being compared to training dataset and thus emotion is predicted.
State of the art time-series analysis with deep learning by Javier Ordóñez at...Big Data Spain
Time series related problems have traditionally been solved using engineered features obtained by heuristic processes.
https://www.bigdataspain.org/2017/talk/state-of-the-art-time-series-analysis-with-deep-learning
Big Data Spain 2017
November 16th - 17th
The document discusses high performance computing and the path towards exascale systems. It covers key application requirements in areas like cancer research, climate modeling, and materials science. Technological challenges for exascale include power and resilience issues. The US Department of Energy is funding several exascale development programs through 2020, including the CANDLE project applying deep learning to precision cancer medicine. Reaching exascale will enable new capabilities in big data analytics, machine learning, and commercial applications.
In this talk, an overview of current trends in machine learning will be discussed with an emphasize on challenges and opportunities facing this field. It will focus on deep learning methods and applications. Deep learning has emerged as one of the most promising research fields in artificial intelligence. The significant advancements that deep learning methods have brought about for large scale image classification tasks have generated a surge of excitement in applying the techniques to other problems in computer vision and more broadly into other disciplines of computer science. Moreover, the impact of machine learning on education, research, and economy will be briefly presented. The rapid growth of machine learning is positioned to impact our lives in a way that we have not been able to fully imagine. It behooves government leaders to take a lead in developing the necessary resources to ride the projected benefits of machine learning.
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
This document discusses a presentation given by Roy Russo of Predikto on using Elasticsearch and Spark for predictive analytics and big data. The presentation covers Predikto's use of Elasticsearch to store sensor and asset management data from various sources in order to perform predictive maintenance and anomaly detection using machine learning algorithms. Roy explains why Elasticsearch and Spark are well-suited for such tasks due to their ability to handle large volumes of time-series and heterogeneous data at scale through horizontal scaling and efficient querying.
A Neural Network that Understands HandwritingShivam Sawhney
This document summarizes a convolutional neural network (CNN) that was implemented using Keras to recognize handwritten digits from 0-9. The CNN model contains steps for convolution, ReLU activation, pooling, flattening, and fully connected layers. The model was trained on a dataset of handwritten digits and achieved 97% accuracy on the test set, demonstrating CNNs capabilities for image classification tasks. The project utilized common deep learning libraries like NumPy, Keras, TensorFlow and followed typical CNN architecture of feature extraction via convolution and pooling layers followed by classification with dense layers.
The document discusses neurosynaptic chips and their advantages over conventional chips. It provides an introduction to neurosynaptic systems and artificial neural networks. It then compares neurosynaptic chips to conventional chips in terms of architecture, complexity, power efficiency, density and speed. Neurosynaptic chips are more efficient and dense as they mimic the brain's architecture by integrating processing and storage. The document also analyzes the performance of neurosynaptic systems from IBM, Stanford and other research organizations compared to the human brain.
HiPEAC 2019 Workshop - Real-Time Modelling Visual Scenes with Biological Insp...Tulipp. Eu
- Computer vision has improved with more data and processing power, but global scene understanding remains challenging.
- The document proposes a multidisciplinary approach combining CNNs and human visual cognition to better model scene understanding, with the goal of applications like autonomous vehicles.
- It describes experiments observing how humans and primates recognize scenes to inform modeling, incorporating global and local descriptors with relationships. This approach aims to advance scene understanding capabilities.
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at: https://www.youtube.com/watch?feature=player_embedded&v=ar8cV0ynWAw
In this talk, after a brief overview of AI concepts in particular Machine Learning (ML) techniques, some of the well-known computer design concepts for high performance and power efficiency are presented. Subsequently, those techniques that have had a promising impact for computing ML algorithms are discussed. Deep learning has emerged as a game changer for many applications in various fields of engineering and medical sciences. Although the primary computation function is matrix vector multiplication, many competing efficient implementations of this primary function have been proposed and put into practice. This talk will review and compare some of those techniques that are used for ML computer design.
DeepLearning and Advanced Machine Learning on IoTRomeo Kienzler
This document discusses advances in machine learning and deep learning on IoT devices. It notes that the number of connected devices is growing rapidly and will reach 40 billion by 2020. It then covers different types of machine learning approaches like online learning vs learning from historic data. It also demonstrates several deep learning techniques including neural networks, convolutional neural networks, LSTMs, and autoencoders. Finally, it discusses challenges like computational complexity and potential solutions like IBM's TrueNorth neuromorphic chip.
Combining out - of - band monitoring with AI and big data for datacenter aut...Ganesan Narayanasamy
Andrea Bartolini presented a method for combining out-of-band monitoring with artificial intelligence and big data analytics to enable datacenter automation. Their system, called D.A.V.I.D.E., uses fine-grained power and performance monitoring of nodes through an embedded system. Data is collected and analyzed using MQTT, Cassandra, and Apache Spark. An autoencoder was trained on historical monitoring data to learn normal behavior and is used to detect anomalies through reconstruction error at the edge in real-time. Future work includes extending this approach for security and expanding it to larger systems.
This project aims to implement a recurrent neural network module for the ACL Neural Toolkit to expand its capabilities. A team will design the neuron and network models, develop a training algorithm, and integrate it into the Toolkit over 5 months. Milestones include preliminary models and algorithms, and incorporating it into the Toolkit to offer a necessary addition that can solve more complex problems than other methods.
Yuwei Cui from Numenta presented on real-time streaming data analysis using Hierarchical Temporal Memory (HTM). HTM is based on principles of the neocortex and allows for online learning of high-order sequences from streaming data. HTM can make multiple predictions simultaneously and is fault tolerant. It has been applied successfully to problems like anomaly detection in data center servers and geospatial tracking data. Numenta is working to further understand the neocortex and create more biologically accurate models to continue advancing machine intelligence.
The document discusses hardware evolution, which applies evolutionary techniques to hardware design and synthesis. It is not just implementing evolutionary algorithms in hardware. Hardware evolution can optimize hardware designs, map designs to programmable chips like FPGAs, and even evolve digital circuits directly on reconfigurable hardware. The document provides examples of how evolution can be used to optimize adder circuits, image compression algorithms, and other applications implemented on reconfigurable hardware. It also discusses constraints and evaluation strategies in hardware evolution.
This document provides an overview and tutorial on various techniques for object recognition, including cascading classifiers, convolutional neural networks (CNNs), and support vector machines (SVMs). It discusses the hierarchical concept formation problem and how these techniques can help a robot learn about its environment autonomously. For each technique, it covers the underlying concepts, example implementations in OpenCV or other libraries, and plans to analyze results through confusion matrices. The document serves as an introduction for researchers or students interested in object recognition and machine learning algorithms.
The Royal Society of Chemistry hosts large scale data collections and provides access to the data to the chemistry community. The largest RSC data set of wide scale interest to the community offers access to tens of millions of compounds. The host platform, ChemSpider, is limited as it is a structure centric hub only. A new architecture, the RSC data repository, has been developed that extends support to reactions, spectral data, crystallography data and related property data. It is also the architecture underlying a series of exemplar projects for managing data for a number of diverse laboratories. The adoption of data standards for the integration and distribution of data has been essential. Specific standards include molecular structure formats such as molfiles and InChIs, and spectral data formats such as JCAMP. This presentation will report on our development of the data repository, the importance of utilizing standards for data integration, the flexible nature of the architecture to deliver solutions for various laboratories and our efforts to develop new large data collections. This includes text-mining efforts to extract large spectrum-structure collections from large corpuses.
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.
An analysis of_machine_and_human_analytics_in_classificationSubhashis Hazarika
1) An analysis of machine learning and human-analytics classification models that found human-guided models performed better due to incorporating "soft knowledge" unavailable to machine models.
2) Two case studies were conducted comparing decision trees from visual analytics with human guidance to those from standard machine learning algorithms.
3) Humans were able to leverage soft knowledge like imagining outliers, looking ahead to future decisions, and incorporating domain expertise to construct superior classification models.
Separating Hype from Reality in Deep Learning with Sameer FarooquiDatabricks
Deep Learning is all the rage these days, but where does the reality of what Deep Learning can do end and the media hype begin? In this talk, I will dispel common myths about Deep Learning that are not necessarily true and help you decide whether you should practically use Deep Learning in your software stack.
I’ll begin with a technical overview of common neural network architectures like CNNs, RNNs, GANs and their common use cases like computer vision, language understanding or unsupervised machine learning. Then I’ll separate the hype from reality around questions like:
• When should you prefer traditional ML systems like scikit learn or Spark.ML instead of Deep Learning?
• Do you no longer need to do careful feature extraction and standardization if using Deep Learning?
• Do you really need terabytes of data when training neural networks or can you ‘steal’ pre-trained lower layers from public models by using transfer learning?
• How do you decide which activation function (like ReLU, leaky ReLU, ELU, etc) or optimizer (like Momentum, AdaGrad, RMSProp, Adam, etc) to use in your neural network?
• Should you randomly initialize the weights in your network or use more advanced strategies like Xavier or He initialization?
• How easy is it to overfit/overtrain a neural network and what are the common techniques to ovoid overfitting (like l1/l2 regularization, dropout and early stopping)?
This document provides an introduction to neural networks. It discusses how neural networks have recently achieved state-of-the-art results in areas like image and speech recognition and how they were able to beat a human player at the game of Go. It then provides a brief history of neural networks, from the early perceptron model to today's deep learning approaches. It notes how neural networks can automatically learn features from data rather than requiring handcrafted features. The document concludes with an overview of commonly used neural network components and libraries for building neural networks today.
Descripción de la plática:
CoreML es el puente entre iOS y Machine Learning, pero con ciertas limitantes. Explotaremos el potencial que tiene CoreML, responderemos las siguientes preguntas:
- ¿Cómo podríamos ir más allá de los límites de CoreML?
- ¿Cómo puede nuestra App aprender de la experiencia?
In-App purchases es la manera en la cual podemos integrar compras o suscripciones dentro de nuestras apps para ofrecer ya sea contenido extra o nuevas características en las mismas. Integraremos StoreKit en una aplicación en vivo para verlo en acción.
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
This document discusses a presentation given by Roy Russo of Predikto on using Elasticsearch and Spark for predictive analytics and big data. The presentation covers Predikto's use of Elasticsearch to store sensor and asset management data from various sources in order to perform predictive maintenance and anomaly detection using machine learning algorithms. Roy explains why Elasticsearch and Spark are well-suited for such tasks due to their ability to handle large volumes of time-series and heterogeneous data at scale through horizontal scaling and efficient querying.
A Neural Network that Understands HandwritingShivam Sawhney
This document summarizes a convolutional neural network (CNN) that was implemented using Keras to recognize handwritten digits from 0-9. The CNN model contains steps for convolution, ReLU activation, pooling, flattening, and fully connected layers. The model was trained on a dataset of handwritten digits and achieved 97% accuracy on the test set, demonstrating CNNs capabilities for image classification tasks. The project utilized common deep learning libraries like NumPy, Keras, TensorFlow and followed typical CNN architecture of feature extraction via convolution and pooling layers followed by classification with dense layers.
The document discusses neurosynaptic chips and their advantages over conventional chips. It provides an introduction to neurosynaptic systems and artificial neural networks. It then compares neurosynaptic chips to conventional chips in terms of architecture, complexity, power efficiency, density and speed. Neurosynaptic chips are more efficient and dense as they mimic the brain's architecture by integrating processing and storage. The document also analyzes the performance of neurosynaptic systems from IBM, Stanford and other research organizations compared to the human brain.
HiPEAC 2019 Workshop - Real-Time Modelling Visual Scenes with Biological Insp...Tulipp. Eu
- Computer vision has improved with more data and processing power, but global scene understanding remains challenging.
- The document proposes a multidisciplinary approach combining CNNs and human visual cognition to better model scene understanding, with the goal of applications like autonomous vehicles.
- It describes experiments observing how humans and primates recognize scenes to inform modeling, incorporating global and local descriptors with relationships. This approach aims to advance scene understanding capabilities.
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at: https://www.youtube.com/watch?feature=player_embedded&v=ar8cV0ynWAw
In this talk, after a brief overview of AI concepts in particular Machine Learning (ML) techniques, some of the well-known computer design concepts for high performance and power efficiency are presented. Subsequently, those techniques that have had a promising impact for computing ML algorithms are discussed. Deep learning has emerged as a game changer for many applications in various fields of engineering and medical sciences. Although the primary computation function is matrix vector multiplication, many competing efficient implementations of this primary function have been proposed and put into practice. This talk will review and compare some of those techniques that are used for ML computer design.
DeepLearning and Advanced Machine Learning on IoTRomeo Kienzler
This document discusses advances in machine learning and deep learning on IoT devices. It notes that the number of connected devices is growing rapidly and will reach 40 billion by 2020. It then covers different types of machine learning approaches like online learning vs learning from historic data. It also demonstrates several deep learning techniques including neural networks, convolutional neural networks, LSTMs, and autoencoders. Finally, it discusses challenges like computational complexity and potential solutions like IBM's TrueNorth neuromorphic chip.
Combining out - of - band monitoring with AI and big data for datacenter aut...Ganesan Narayanasamy
Andrea Bartolini presented a method for combining out-of-band monitoring with artificial intelligence and big data analytics to enable datacenter automation. Their system, called D.A.V.I.D.E., uses fine-grained power and performance monitoring of nodes through an embedded system. Data is collected and analyzed using MQTT, Cassandra, and Apache Spark. An autoencoder was trained on historical monitoring data to learn normal behavior and is used to detect anomalies through reconstruction error at the edge in real-time. Future work includes extending this approach for security and expanding it to larger systems.
This project aims to implement a recurrent neural network module for the ACL Neural Toolkit to expand its capabilities. A team will design the neuron and network models, develop a training algorithm, and integrate it into the Toolkit over 5 months. Milestones include preliminary models and algorithms, and incorporating it into the Toolkit to offer a necessary addition that can solve more complex problems than other methods.
Yuwei Cui from Numenta presented on real-time streaming data analysis using Hierarchical Temporal Memory (HTM). HTM is based on principles of the neocortex and allows for online learning of high-order sequences from streaming data. HTM can make multiple predictions simultaneously and is fault tolerant. It has been applied successfully to problems like anomaly detection in data center servers and geospatial tracking data. Numenta is working to further understand the neocortex and create more biologically accurate models to continue advancing machine intelligence.
The document discusses hardware evolution, which applies evolutionary techniques to hardware design and synthesis. It is not just implementing evolutionary algorithms in hardware. Hardware evolution can optimize hardware designs, map designs to programmable chips like FPGAs, and even evolve digital circuits directly on reconfigurable hardware. The document provides examples of how evolution can be used to optimize adder circuits, image compression algorithms, and other applications implemented on reconfigurable hardware. It also discusses constraints and evaluation strategies in hardware evolution.
This document provides an overview and tutorial on various techniques for object recognition, including cascading classifiers, convolutional neural networks (CNNs), and support vector machines (SVMs). It discusses the hierarchical concept formation problem and how these techniques can help a robot learn about its environment autonomously. For each technique, it covers the underlying concepts, example implementations in OpenCV or other libraries, and plans to analyze results through confusion matrices. The document serves as an introduction for researchers or students interested in object recognition and machine learning algorithms.
The Royal Society of Chemistry hosts large scale data collections and provides access to the data to the chemistry community. The largest RSC data set of wide scale interest to the community offers access to tens of millions of compounds. The host platform, ChemSpider, is limited as it is a structure centric hub only. A new architecture, the RSC data repository, has been developed that extends support to reactions, spectral data, crystallography data and related property data. It is also the architecture underlying a series of exemplar projects for managing data for a number of diverse laboratories. The adoption of data standards for the integration and distribution of data has been essential. Specific standards include molecular structure formats such as molfiles and InChIs, and spectral data formats such as JCAMP. This presentation will report on our development of the data repository, the importance of utilizing standards for data integration, the flexible nature of the architecture to deliver solutions for various laboratories and our efforts to develop new large data collections. This includes text-mining efforts to extract large spectrum-structure collections from large corpuses.
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.
An analysis of_machine_and_human_analytics_in_classificationSubhashis Hazarika
1) An analysis of machine learning and human-analytics classification models that found human-guided models performed better due to incorporating "soft knowledge" unavailable to machine models.
2) Two case studies were conducted comparing decision trees from visual analytics with human guidance to those from standard machine learning algorithms.
3) Humans were able to leverage soft knowledge like imagining outliers, looking ahead to future decisions, and incorporating domain expertise to construct superior classification models.
Separating Hype from Reality in Deep Learning with Sameer FarooquiDatabricks
Deep Learning is all the rage these days, but where does the reality of what Deep Learning can do end and the media hype begin? In this talk, I will dispel common myths about Deep Learning that are not necessarily true and help you decide whether you should practically use Deep Learning in your software stack.
I’ll begin with a technical overview of common neural network architectures like CNNs, RNNs, GANs and their common use cases like computer vision, language understanding or unsupervised machine learning. Then I’ll separate the hype from reality around questions like:
• When should you prefer traditional ML systems like scikit learn or Spark.ML instead of Deep Learning?
• Do you no longer need to do careful feature extraction and standardization if using Deep Learning?
• Do you really need terabytes of data when training neural networks or can you ‘steal’ pre-trained lower layers from public models by using transfer learning?
• How do you decide which activation function (like ReLU, leaky ReLU, ELU, etc) or optimizer (like Momentum, AdaGrad, RMSProp, Adam, etc) to use in your neural network?
• Should you randomly initialize the weights in your network or use more advanced strategies like Xavier or He initialization?
• How easy is it to overfit/overtrain a neural network and what are the common techniques to ovoid overfitting (like l1/l2 regularization, dropout and early stopping)?
This document provides an introduction to neural networks. It discusses how neural networks have recently achieved state-of-the-art results in areas like image and speech recognition and how they were able to beat a human player at the game of Go. It then provides a brief history of neural networks, from the early perceptron model to today's deep learning approaches. It notes how neural networks can automatically learn features from data rather than requiring handcrafted features. The document concludes with an overview of commonly used neural network components and libraries for building neural networks today.
Descripción de la plática:
CoreML es el puente entre iOS y Machine Learning, pero con ciertas limitantes. Explotaremos el potencial que tiene CoreML, responderemos las siguientes preguntas:
- ¿Cómo podríamos ir más allá de los límites de CoreML?
- ¿Cómo puede nuestra App aprender de la experiencia?
In-App purchases es la manera en la cual podemos integrar compras o suscripciones dentro de nuestras apps para ofrecer ya sea contenido extra o nuevas características en las mismas. Integraremos StoreKit en una aplicación en vivo para verlo en acción.
La propiedad IBDesignable permite al desarrollador generar una UIView, UIControl o UIViewController con propiedades definidas de acuerdo a nuestras necesidades. La mayor ventaja y diferencia con una subclase es que nos brinda la capacidad de editar estas propiedades desde el Interface Builder y ver estos cambios en tiempo real.
Los nuevos protocolos son muy parecidos a las interfaces en lenguajes de programación, aunque solo a simple vista. En Swift podemos hacer uso de los protocolos para dar increíbles poderes de personalización a nuestras clases y tipos de valor.
El documento presenta una introducción a Swift y el desarrollo de aplicaciones iOS, incluyendo una breve historia de Cocoa y Cocoa Touch, la construcción de aplicaciones iOS con Swift, Swift Playgrounds y el ciclo de vida de una aplicación iOS.
Este documento describe diferentes herramientas para gestionar dependencias en proyectos iOS. Explica brevemente Submódulos Git, Carthage y Cocoapods, tres de las herramientas más populares. Incluye ejemplos de cómo agregar frameworks como dependencias usando cada una de estas herramientas. También cubre conceptos clave como versiones semánticas y archivos Podfile.lock.
Taller sobre la herramienta PaintCode de @vicktormanuel
¿Qué es paint code?, ¿Por qué debería tenerlo en cuenta o conocerlo?, ¿Para qué me sirve esta herramienta?
Presentación de @JDandini sobre la arquitectura VIPER:
¿Hay algo mas alla de la arquitectura MV(X)[MVC, MVVM etc]?
¿Por qué debería dejar lo que ya se por otro tipo de arquitectura?
Pues bueno averigüemoslo juntos, te quiero presentar una arquitectura nueva que es VIPER, basada en el SRP (principio de una sola responsabilidad por sus siglas en inglés) la cual te prometo ayudará a concebir el enfoque de tus proyectos de otra forma.
Programación funcional con swift. Se ven conceptos como funciones de primera clase, funciones de orden superior, métodos como filter, map y el patrón Result para la gestión de errores.
Este documento presenta una guía para crear una aplicación de Instagram hecha con código para publicar fotos y datos asociados de forma local o en la nube. Explica los componentes clave como el controlador de vista para agregar héroes, el controlador de tabla de héroes, el procesamiento de nuevas publicaciones y el almacenamiento local y remoto de datos. También cubre los requisitos de aprobación de Apple para publicar la aplicación en la App Store.
Este documento presenta una introducción a las matemáticas en la programación y la ingeniería. Explica brevemente el origen de la computación desde las primeras máquinas mecánicas en el siglo XIX hasta los desarrollos modernos. También describe cómo surgieron conceptos clave como el software y lenguajes de programación. Finalmente, destaca la importancia de las matemáticas formales en el desarrollo de software e ingeniería.
This document discusses developing 2D video games using SpriteKit and Swift. It outlines the key ingredients needed including a game engine, scenes, sprites, particles, physics, input, and effects. Specifically, it will cover using SpriteKit's rendering engine, coordinating sprites and particles, implementing collision detection and movement, and adding finishing touches like sound effects and music. The goal is to provide an overview of the process for building a basic 2D game.
Este documento habla sobre la importancia de las pruebas unitarias para asegurar que el código funcione correctamente y prevenir errores. Recomienda probar funcionalidad nueva, central y flujos comunes, así como límites. Las buenas pruebas deben ser rápidas, aisladas, repetibles y autovalidadas. También enfatiza verificar la arquitectura y usar el ciclo de rojo, verde y refactorizar para escribir pruebas efectivas.
Bridgefy permite el envío y recepción de mensajes entre dispositivos móviles sin necesidad de Internet a través de redes mesh. Su SDK permite a los desarrolladores agregar esta funcionalidad a sus aplicaciones y maneja problemas como nodos móviles y cambiantes, diferencias entre dispositivos, y entrega inteligente de mensajes. El SDK está disponible para iOS y Android en Swift y Objective-C.
Este documento habla sobre el diseño ágil para desarrolladores, incluyendo temas como el diseño para iOS, prototipado, guías de interfaz humana de iOS y Material Design. También discute los niveles de experiencia del cliente, experiencia de usuario e interfaz de usuario, así como el proceso de diseño que involucra bocetos, wireframes, pruebas, prototipos y iteraciones. Finalmente, enfatiza la importancia de la empatía en el diseño.
This document discusses clean architecture principles for mobile applications. It describes common iOS code smells like god view controllers and tightly coupled code. The document introduces SOLID principles to improve code quality and testability. It then outlines architectural layers including entities, use cases, interface adapters, and frameworks. The layers are arranged based on the dependency rule, where inner layers do not depend on outer ones. Specific patterns like MVC, MVP, MVVM, VIPER and repositories are presented for each layer. The document emphasizes designing applications that are decoupled from frameworks and user interfaces to improve reusability and flexibility.
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Fluke Solar Application Specialist Will White is presenting on this engaging topic:
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1. C O R E M L A N D
C O M P U T E R V I S I O N
@ M I L L A N I M I X
2. H U N I M I X U U K M U WA N
L E O N A R D O - I S R A E L M I L L Á N - G A R C Í A @millanimix
• Mexicano, Tenochca, SkyAnahuacwalker
• Student of the Pre-Hispanic Tradition
• Rusty Researcher of Computer Vision
• Objective-C developer (Oldie but goodie)
• ¡Ah! I currently work as Project Manager
3. C O R E M L
• Integrate machine
learning models into your
app.
• A trained model is the
result of applying a
machine learning
algorithm to a set of
training data.
• The model makes
predictions based on new
input data.
https://developer.apple.com/documentation/coreml
6. C O R E M L
• Core ML Supports:
• Image analysis.
• Foundation NLP.
• Learned decision
trees.
https://developer.apple.com/documentation/coreml
7. V I S I O N
N AT U R A L V S A R T I F I C I A L
6 to 7 millions of cones
120 millions of rods
iPhone X 12MP
8. V I S I O N F R A M E W O R K
C O M P U T E R V I S I O N
9. V I S I O N F R A M E W O R K
• Still Image Analysis.
• Image Sequence
Analysis.
• Object Tracking.
• Rectangle Detection.
• Face Detection.
• Barcode Detection.
• Text Detection.
• Horizon Detection.
• Image Alignment.
• Machine-Learning
Image Analysis.
• Coordinate
Conversion.
10. C O R E M L
• Core ML Tools: Python package
• Machine learning models
(.mlmodel):
• Neural networks.
• Tree ensembles.
• Support vector machines.
• Generalized linear models.
• Takes advantages of the CPU
and GPU
11. C O R E M L
• BNNS (Basic Neural Network
Subroutines)
• Accelerate Framework: A
collection of math functions.
• CPU’s fast vector
instructions.
• MPSCNN: Metal Performance
Shaders - Convolutional Neural
Networks
• Compute kernels that run
on the GPU
https://developer.apple.com/documentation/coreml
15. H U M A N N E U R A L N E T W O R K S
• Neuron
• Electrically excitable cell.
• Receives, processes and
transmits information
through electrical and
chemical signals.
• Human Brain
• 1508 g.
• 86 billion neurons.
16. A R T I F I C I A L N E U R A L N E T W O R K S
• Input layer
• Hidden layers
• Output layer
18. A 1 1 B I O N I C
• System on a Chip (SoC).
• 64-bit ARM / 4.3 billion
transistors.
• iPhone 8 / Plus & X.
• Two performance cores /
Four high-efficiency cores.
• Apple-designed GPU with
three-core design.
19.
20.
21. S U M M A RY
A R T I F I C I A L I N T E L L I G E N C E
22. S U M M A RY
A R T I F I C I A L I N T E L L I G E N C E