A brief introduction to deep learning, providing rough interpretation to deep neural networks and simple implementations with Keras for deep learning beginners.
Brief presentation about keras framework. The propose of this presentation is to give some ideas about how it works and its main functionalities. In addition, is also shown a function to create different models from a config file.
First steps with Keras 2: A tutorial with ExamplesFelipe
In this presentation, we give a brief introduction to Keras and Neural networks, and use examples to explain how to build and train neural network models using this framework.
Talk given as part of an event by Rio Machine Learning Meetup.
Keras with Tensorflow backend can be used for neural networks and deep learning in both R and Python. The document discusses using Keras to build neural networks from scratch on MNIST data, using pre-trained models like VGG16 for computer vision tasks, and fine-tuning pre-trained models on limited data. Examples are provided for image classification, feature extraction, and calculating image similarities.
TensorFlow and Keras are popular deep learning frameworks. TensorFlow is an open source library for numerical computation using data flow graphs. It was developed by Google and is widely used for machine learning and deep learning. Keras is a higher-level neural network API that can run on top of TensorFlow. It focuses on user-friendliness, modularization and extensibility. Both frameworks make building and training neural networks easier through modular layers and built-in optimization algorithms.
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow, CNTK or Theano.
We can easily build a model and train it using keras very easily with few lines of code.The steps to train the model is described in the presentation.
Use Keras if you need a deep learning library that:
-Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
-Supports both convolutional networks and recurrent networks, as well as combinations of the two.
-Runs seamlessly on CPU and GPU.
Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better!
In this workshop, we will discuss the basics of Neural Networks and discuss how Deep Learning Neural networks are different from conventional Neural Network architectures. We will review a bit of mathematics that goes into building neural networks and understand the role of GPUs in Deep Learning. We will also get an introduction to Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano and Tensorflow.
This document provides an overview and outline of a TensorFlow tutorial. It discusses handling images, logistic regression, multi-layer perceptrons, and convolutional neural networks. Key concepts explained include the goal of deep learning as mapping vectors, one-hot encoding of output classes, the definitions of epochs, batch size, and iterations in training, and loading and preprocessing image data for a TensorFlow tutorial.
Image Classification Done Simply using Keras and TensorFlow Rajiv Shah
This presentation walks through the process of building an image classifier using Keras with a TensorFlow backend. It will give a basic understanding of image classification and show the techniques used in industry to build image classifiers. The presentation will start with building a simple convolutional network, augmenting the data, using a pretrained network, and finally using transfer learning by modifying the last few layers of a pretrained network. The classification will be based on the classic example of classifying cats and dogs. The code for the presentation can be found at https://github.com/rajshah4/image_keras, and the presentation will discuss how to extend the code to your own pictures to make a custom image classifier.
Brief presentation about keras framework. The propose of this presentation is to give some ideas about how it works and its main functionalities. In addition, is also shown a function to create different models from a config file.
First steps with Keras 2: A tutorial with ExamplesFelipe
In this presentation, we give a brief introduction to Keras and Neural networks, and use examples to explain how to build and train neural network models using this framework.
Talk given as part of an event by Rio Machine Learning Meetup.
Keras with Tensorflow backend can be used for neural networks and deep learning in both R and Python. The document discusses using Keras to build neural networks from scratch on MNIST data, using pre-trained models like VGG16 for computer vision tasks, and fine-tuning pre-trained models on limited data. Examples are provided for image classification, feature extraction, and calculating image similarities.
TensorFlow and Keras are popular deep learning frameworks. TensorFlow is an open source library for numerical computation using data flow graphs. It was developed by Google and is widely used for machine learning and deep learning. Keras is a higher-level neural network API that can run on top of TensorFlow. It focuses on user-friendliness, modularization and extensibility. Both frameworks make building and training neural networks easier through modular layers and built-in optimization algorithms.
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow, CNTK or Theano.
We can easily build a model and train it using keras very easily with few lines of code.The steps to train the model is described in the presentation.
Use Keras if you need a deep learning library that:
-Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
-Supports both convolutional networks and recurrent networks, as well as combinations of the two.
-Runs seamlessly on CPU and GPU.
Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better!
In this workshop, we will discuss the basics of Neural Networks and discuss how Deep Learning Neural networks are different from conventional Neural Network architectures. We will review a bit of mathematics that goes into building neural networks and understand the role of GPUs in Deep Learning. We will also get an introduction to Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano and Tensorflow.
This document provides an overview and outline of a TensorFlow tutorial. It discusses handling images, logistic regression, multi-layer perceptrons, and convolutional neural networks. Key concepts explained include the goal of deep learning as mapping vectors, one-hot encoding of output classes, the definitions of epochs, batch size, and iterations in training, and loading and preprocessing image data for a TensorFlow tutorial.
Image Classification Done Simply using Keras and TensorFlow Rajiv Shah
This presentation walks through the process of building an image classifier using Keras with a TensorFlow backend. It will give a basic understanding of image classification and show the techniques used in industry to build image classifiers. The presentation will start with building a simple convolutional network, augmenting the data, using a pretrained network, and finally using transfer learning by modifying the last few layers of a pretrained network. The classification will be based on the classic example of classifying cats and dogs. The code for the presentation can be found at https://github.com/rajshah4/image_keras, and the presentation will discuss how to extend the code to your own pictures to make a custom image classifier.
Dr. Erin LeDell, Machine Learning Scientist, H2O.ai at MLconf SEA - 5/20/16MLconf
Multi-algorithm Ensemble Learning at Scale: Software, Hardware and Algorithmic Approaches: Multi-algorithm ensemble machine learning methods are often used when the true prediction function is not easily approximated by a single algorithm. The Super Learner algorithm, also known as stacking, combines multiple, typically diverse, base learning algorithms into a single, powerful prediction function through a secondary learning process called metalearning. Although ensemble methods offer superior performance over their singleton counterparts, there is an implicit computational cost to ensembles, as it requires training and cross-validating multiple base learning algorithms.
We will demonstrate a variety of software- and hardware-based approaches that lead to more scalable ensemble learning software, including a highly scalable implementation of stacking called “H2O Ensemble”, built on top of the open source, distributed machine learning platform, H2O. H2O Ensemble scales across multi-node clusters and allows the user to create ensembles of deep neural networks, Gradient Boosting Machines, Random Forest, and others. As for algorithm-based approaches, we will present two algorithmic modifications to the original stacking algorithm that further reduce computation time — Subsemble algorithm and the Online Super Learner algorithm. This talk will also include benchmarks of the implementations of these new stacking variants.
Melanie Warrick, Deep Learning Engineer, Skymind.io at MLconf SF - 11/13/15MLconf
Attention Neural Net Model Fundamentals: Neural networks have regained popularity over the last decade because they are demonstrating real world value in different applications (e.g. targeted advertising, recommender engines, Siri, self driving cars, facial recognition). Several model types are currently explored in the field with recurrent neural networks (RNN) and convolution neural networks (CNN) taking the top focus. The attention model, a recently developed RNN variant, has started to play a larger role in both natural language processing and image analysis research.
This talk will cover the fundamentals of the attention model structure and how its applied to visual and speech analysis. I will provide an overview of the model functionality and math including a high-level differentiation between soft and hard types. The goal is to give you enough of an understanding of what the model is, how it works and where to apply it.
Erin LeDell, Machine Learning Scientist, H2O.ai at MLconf ATL 2016MLconf
Multi-algorithm Ensemble Learning at Scale: Software, Hardware and Algorithmic Approaches: Multi-algorithm ensemble machine learning methods are often used when the true prediction function is not easily approximated by a single algorithm. The Super Learner algorithm, also known as stacking, combines multiple, typically diverse, base learning algorithms into a single, powerful prediction function through a secondary learning process called metalearning. Although ensemble methods offer superior performance over their singleton counterparts, there is an implicit computational cost to ensembles, as it requires training and cross-validating multiple base learning algorithms.
We will demonstrate a variety of software- and hardware-based approaches that lead to more scalable ensemble learning software, including a highly scalable implementation of stacking called “H2O Ensemble”, built on top of the open source, distributed machine learning platform, H2O. H2O Ensemble scales across multi-node clusters and allows the user to create ensembles of deep neural networks, Gradient Boosting Machines, Random Forest, and others. As for algorithm-based approaches, we will present two algorithmic modifications to the original stacking algorithm that further reduce computation time — Subsemble algorithm and the Online Super Learner algorithm. This talk will also include benchmarks of the implementations of these new stacking variants.
Daniel Shank, Data Scientist, Talla at MLconf SF 2016MLconf
This document discusses neural Turing machines, which are neural networks combined with external memory systems that allow them to be trained end-to-end using backpropagation. Neural Turing machines can learn simple algorithms and generalize well for tasks like language modeling and question answering. However, they are difficult to train due to numerical instability and optimizing memory usage. The document recommends techniques like gradient clipping, loss clipping, and curriculum learning to improve training. It also covers developments like dynamic neural computers that can allocate and deallocate memory.
Hussein Mehanna, Engineering Director, ML Core - Facebook at MLconf ATL 2016MLconf
Applying Deep Learning at Facebook Scale: Facebook leverages Deep Learning for various applications including event prediction, machine translation, natural language understanding and computer vision at a very large scale. There are more than a billion users logging on to Facebook every daily generating thousands of posts per second and uploading more than a billion images and videos every day. This talk will explain how Facebook scaled Deep Learning inference for realtime applications with latency budgets in the milliseconds.
Deep Recurrent Neural Networks for Sequence Learning in Spark by Yves MabialaSpark Summit
Deep recurrent neural networks are well-suited for sequence learning tasks like text classification and generation. The author discusses implementing recurrent neural networks in Spark for distributed deep learning on big data. Two use cases are described: predictive maintenance using sensor data to detect failures, and sentiment analysis of tweets using RNNs which achieve better accuracy than traditional classifiers.
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016MLconf
This document discusses using DL4J and DataVec to build deep learning workflows for modeling time series sensor data with recurrent neural networks. It provides an example of loading and transforming time series data from sensors using DataVec, configuring an RNN using DL4J to classify the trends in the sensor data, and training the network both locally and distributed on Spark. The document promotes DL4J and DataVec as tools that can help enterprises overcome challenges to operationalizing deep learning and producing machine learning models at scale.
Develop a fundamental overview of Google TensorFlow, one of the most widely adopted technologies for advanced deep learning and neural network applications. Understand the core concepts of artificial intelligence, deep learning and machine learning and the applications of TensorFlow in these areas.
The deck also introduces the Spotle.ai masterclass in Advanced Deep Learning With Tensorflow and Keras.
Language translation with Deep Learning (RNN) with TensorFlowS N
This document provides an overview of a meetup on language translation with deep learning using TensorFlow on FloydHub. It will cover the language translation challenge, introducing key concepts like deep learning, RNNs, NLP, TensorFlow and FloydHub. It will then describe the solution approach to the translation task, including a demo and code walkthrough. Potential next steps and references for further learning are also mentioned.
DLD meetup 2017, Efficient Deep LearningBrodmann17
The document discusses efficient techniques for deep learning on edge devices. It begins by noting that deep neural networks have high computational complexity which makes inference inefficient for edge devices without powerful GPUs. It then outlines the deep learning stack from hardware to libraries to frameworks to algorithms. The document focuses on how algorithms define model complexity and discusses the evolution of CNN architectures from LeNet5 to ResNet which generally increased in complexity. It covers techniques for reducing model size and operations like pruning, quantization, and knowledge distillation. The challenges of real-life applications on edge devices are discussed.
Distributed implementation of a lstm on spark and tensorflowEmanuel Di Nardo
Academic project based on developing a LSTM distributing it on Spark and using Tensorflow for numerical operations.
Source code: https://github.com/EmanuelOverflow/LSTM-TensorSpark
Presentation given at the Stockholm R useR Group (SRUG) meetup on Dec 6, 2016. Contains a general overview of deep learning, material on using Tensorflow in R etc.
TensorFlow in 3 sentences
Barbara Fusinska provides a high-level overview of TensorFlow in 3 sentences or less. She demonstrates how to build a computational graph for classification tasks using APIs like tf.nn and tf.layers. Barbara encourages attendees to get involved with open source TensorFlow communities on GitHub and through tools like Docker containers.
This document summarizes a presentation on deep learning in Python. It discusses training a deep neural network (DNN), including data analysis, architecture design, optimization, and training. It also covers improving the DNN through techniques like data augmentation and monitoring layer training. Finally, it reviews popular open-source Python packages for deep learning like Theano, Keras, and Caffe and their uses in applications and research.
This document provides an overview of deep learning and neural networks. It begins with definitions of machine learning, artificial intelligence, and the different types of machine learning problems. It then introduces deep learning, explaining that it uses neural networks with multiple layers to learn representations of data. The document discusses why deep learning works better than traditional machine learning for complex problems. It covers key concepts like activation functions, gradient descent, backpropagation, and overfitting. It also provides examples of applications of deep learning and popular deep learning frameworks like TensorFlow. Overall, the document gives a high-level introduction to deep learning concepts and techniques.
Avi Pfeffer, Principal Scientist, Charles River Analytics at MLconf SEA - 5/2...MLconf
Practical Probabilistic Programming with Figaro: Probabilistic reasoning enables you to predict the future, infer the past, and learn from experience. Probabilistic programming enables users to build and reason with a wide variety of probabilistic models without machine learning expertise. In this talk, I will present Figaro, a mature probabilistic programming system with many applications. I will describe the main design principles of the language and show example applications. I will also discuss our current efforts to fully automate and optimize the inference process.
This document discusses Bayesian global optimization and its application to tuning machine learning models. It begins by outlining some of the challenges of tuning ML models, such as the non-intuitive nature of the task. It then introduces Bayesian global optimization as an approach to efficiently search the hyperparameter space to find optimal configurations. The key aspects of Bayesian global optimization are described, including using Gaussian processes to build models of the objective function from sampled points and finding the next best point to sample via expected improvement. Several examples are provided demonstrating how Bayesian global optimization outperforms standard tuning methods in optimizing real-world ML tasks.
This document provides an overview of machine learning concepts and how to build neural networks using TensorFlow and Keras. It introduces machine learning types like regression, classification and clustering. It also covers neural network topics such as layers, activation functions, training, and making predictions. Examples are provided for housing price prediction, iris classification, and building and training a neural network model in Keras.
This is a presentation I gave as a short overview of LSTMs. The slides are accompanied by two examples which apply LSTMs to Time Series data. Examples were implemented using Keras. See links in slide pack.
Performance Comparison between Pytorch and Mindsporeijdms
Deep learning has been well used in many fields. However, there is a large amount of data when training neural networks, which makes many deep learning frameworks appear to serve deep learning practitioners, providing services that are more convenient to use and perform better. MindSpore and PyTorch are both deep learning frameworks. MindSpore is owned by HUAWEI, while PyTorch is owned by Facebook. Some people think that HUAWEI's MindSpore has better performance than FaceBook's PyTorch, which makes deep learning practitioners confused about the choice between the two. In this paper, we perform analytical and experimental analysis to reveal the comparison of training speed of MIndSpore and PyTorch on a single GPU. To ensure that our survey is as comprehensive as possible, we carefully selected neural networks in 2 main domains, which cover computer vision and natural language processing (NLP). The contribution of this work is twofold. First, we conduct detailed benchmarking experiments on MindSpore and PyTorch to analyze the reasons for their performance differences. This work provides guidance for end users to choose between these two frameworks.
SENTIMENT ANALYSIS FOR MOVIES REVIEWS DATASET USING DEEP LEARNING MODELSIJDKP
Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to 50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Naïve Bayes and RNTN that were published in other works using English datasets.
Dr. Erin LeDell, Machine Learning Scientist, H2O.ai at MLconf SEA - 5/20/16MLconf
Multi-algorithm Ensemble Learning at Scale: Software, Hardware and Algorithmic Approaches: Multi-algorithm ensemble machine learning methods are often used when the true prediction function is not easily approximated by a single algorithm. The Super Learner algorithm, also known as stacking, combines multiple, typically diverse, base learning algorithms into a single, powerful prediction function through a secondary learning process called metalearning. Although ensemble methods offer superior performance over their singleton counterparts, there is an implicit computational cost to ensembles, as it requires training and cross-validating multiple base learning algorithms.
We will demonstrate a variety of software- and hardware-based approaches that lead to more scalable ensemble learning software, including a highly scalable implementation of stacking called “H2O Ensemble”, built on top of the open source, distributed machine learning platform, H2O. H2O Ensemble scales across multi-node clusters and allows the user to create ensembles of deep neural networks, Gradient Boosting Machines, Random Forest, and others. As for algorithm-based approaches, we will present two algorithmic modifications to the original stacking algorithm that further reduce computation time — Subsemble algorithm and the Online Super Learner algorithm. This talk will also include benchmarks of the implementations of these new stacking variants.
Melanie Warrick, Deep Learning Engineer, Skymind.io at MLconf SF - 11/13/15MLconf
Attention Neural Net Model Fundamentals: Neural networks have regained popularity over the last decade because they are demonstrating real world value in different applications (e.g. targeted advertising, recommender engines, Siri, self driving cars, facial recognition). Several model types are currently explored in the field with recurrent neural networks (RNN) and convolution neural networks (CNN) taking the top focus. The attention model, a recently developed RNN variant, has started to play a larger role in both natural language processing and image analysis research.
This talk will cover the fundamentals of the attention model structure and how its applied to visual and speech analysis. I will provide an overview of the model functionality and math including a high-level differentiation between soft and hard types. The goal is to give you enough of an understanding of what the model is, how it works and where to apply it.
Erin LeDell, Machine Learning Scientist, H2O.ai at MLconf ATL 2016MLconf
Multi-algorithm Ensemble Learning at Scale: Software, Hardware and Algorithmic Approaches: Multi-algorithm ensemble machine learning methods are often used when the true prediction function is not easily approximated by a single algorithm. The Super Learner algorithm, also known as stacking, combines multiple, typically diverse, base learning algorithms into a single, powerful prediction function through a secondary learning process called metalearning. Although ensemble methods offer superior performance over their singleton counterparts, there is an implicit computational cost to ensembles, as it requires training and cross-validating multiple base learning algorithms.
We will demonstrate a variety of software- and hardware-based approaches that lead to more scalable ensemble learning software, including a highly scalable implementation of stacking called “H2O Ensemble”, built on top of the open source, distributed machine learning platform, H2O. H2O Ensemble scales across multi-node clusters and allows the user to create ensembles of deep neural networks, Gradient Boosting Machines, Random Forest, and others. As for algorithm-based approaches, we will present two algorithmic modifications to the original stacking algorithm that further reduce computation time — Subsemble algorithm and the Online Super Learner algorithm. This talk will also include benchmarks of the implementations of these new stacking variants.
Daniel Shank, Data Scientist, Talla at MLconf SF 2016MLconf
This document discusses neural Turing machines, which are neural networks combined with external memory systems that allow them to be trained end-to-end using backpropagation. Neural Turing machines can learn simple algorithms and generalize well for tasks like language modeling and question answering. However, they are difficult to train due to numerical instability and optimizing memory usage. The document recommends techniques like gradient clipping, loss clipping, and curriculum learning to improve training. It also covers developments like dynamic neural computers that can allocate and deallocate memory.
Hussein Mehanna, Engineering Director, ML Core - Facebook at MLconf ATL 2016MLconf
Applying Deep Learning at Facebook Scale: Facebook leverages Deep Learning for various applications including event prediction, machine translation, natural language understanding and computer vision at a very large scale. There are more than a billion users logging on to Facebook every daily generating thousands of posts per second and uploading more than a billion images and videos every day. This talk will explain how Facebook scaled Deep Learning inference for realtime applications with latency budgets in the milliseconds.
Deep Recurrent Neural Networks for Sequence Learning in Spark by Yves MabialaSpark Summit
Deep recurrent neural networks are well-suited for sequence learning tasks like text classification and generation. The author discusses implementing recurrent neural networks in Spark for distributed deep learning on big data. Two use cases are described: predictive maintenance using sensor data to detect failures, and sentiment analysis of tweets using RNNs which achieve better accuracy than traditional classifiers.
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016MLconf
This document discusses using DL4J and DataVec to build deep learning workflows for modeling time series sensor data with recurrent neural networks. It provides an example of loading and transforming time series data from sensors using DataVec, configuring an RNN using DL4J to classify the trends in the sensor data, and training the network both locally and distributed on Spark. The document promotes DL4J and DataVec as tools that can help enterprises overcome challenges to operationalizing deep learning and producing machine learning models at scale.
Develop a fundamental overview of Google TensorFlow, one of the most widely adopted technologies for advanced deep learning and neural network applications. Understand the core concepts of artificial intelligence, deep learning and machine learning and the applications of TensorFlow in these areas.
The deck also introduces the Spotle.ai masterclass in Advanced Deep Learning With Tensorflow and Keras.
Language translation with Deep Learning (RNN) with TensorFlowS N
This document provides an overview of a meetup on language translation with deep learning using TensorFlow on FloydHub. It will cover the language translation challenge, introducing key concepts like deep learning, RNNs, NLP, TensorFlow and FloydHub. It will then describe the solution approach to the translation task, including a demo and code walkthrough. Potential next steps and references for further learning are also mentioned.
DLD meetup 2017, Efficient Deep LearningBrodmann17
The document discusses efficient techniques for deep learning on edge devices. It begins by noting that deep neural networks have high computational complexity which makes inference inefficient for edge devices without powerful GPUs. It then outlines the deep learning stack from hardware to libraries to frameworks to algorithms. The document focuses on how algorithms define model complexity and discusses the evolution of CNN architectures from LeNet5 to ResNet which generally increased in complexity. It covers techniques for reducing model size and operations like pruning, quantization, and knowledge distillation. The challenges of real-life applications on edge devices are discussed.
Distributed implementation of a lstm on spark and tensorflowEmanuel Di Nardo
Academic project based on developing a LSTM distributing it on Spark and using Tensorflow for numerical operations.
Source code: https://github.com/EmanuelOverflow/LSTM-TensorSpark
Presentation given at the Stockholm R useR Group (SRUG) meetup on Dec 6, 2016. Contains a general overview of deep learning, material on using Tensorflow in R etc.
TensorFlow in 3 sentences
Barbara Fusinska provides a high-level overview of TensorFlow in 3 sentences or less. She demonstrates how to build a computational graph for classification tasks using APIs like tf.nn and tf.layers. Barbara encourages attendees to get involved with open source TensorFlow communities on GitHub and through tools like Docker containers.
This document summarizes a presentation on deep learning in Python. It discusses training a deep neural network (DNN), including data analysis, architecture design, optimization, and training. It also covers improving the DNN through techniques like data augmentation and monitoring layer training. Finally, it reviews popular open-source Python packages for deep learning like Theano, Keras, and Caffe and their uses in applications and research.
This document provides an overview of deep learning and neural networks. It begins with definitions of machine learning, artificial intelligence, and the different types of machine learning problems. It then introduces deep learning, explaining that it uses neural networks with multiple layers to learn representations of data. The document discusses why deep learning works better than traditional machine learning for complex problems. It covers key concepts like activation functions, gradient descent, backpropagation, and overfitting. It also provides examples of applications of deep learning and popular deep learning frameworks like TensorFlow. Overall, the document gives a high-level introduction to deep learning concepts and techniques.
Avi Pfeffer, Principal Scientist, Charles River Analytics at MLconf SEA - 5/2...MLconf
Practical Probabilistic Programming with Figaro: Probabilistic reasoning enables you to predict the future, infer the past, and learn from experience. Probabilistic programming enables users to build and reason with a wide variety of probabilistic models without machine learning expertise. In this talk, I will present Figaro, a mature probabilistic programming system with many applications. I will describe the main design principles of the language and show example applications. I will also discuss our current efforts to fully automate and optimize the inference process.
This document discusses Bayesian global optimization and its application to tuning machine learning models. It begins by outlining some of the challenges of tuning ML models, such as the non-intuitive nature of the task. It then introduces Bayesian global optimization as an approach to efficiently search the hyperparameter space to find optimal configurations. The key aspects of Bayesian global optimization are described, including using Gaussian processes to build models of the objective function from sampled points and finding the next best point to sample via expected improvement. Several examples are provided demonstrating how Bayesian global optimization outperforms standard tuning methods in optimizing real-world ML tasks.
This document provides an overview of machine learning concepts and how to build neural networks using TensorFlow and Keras. It introduces machine learning types like regression, classification and clustering. It also covers neural network topics such as layers, activation functions, training, and making predictions. Examples are provided for housing price prediction, iris classification, and building and training a neural network model in Keras.
This is a presentation I gave as a short overview of LSTMs. The slides are accompanied by two examples which apply LSTMs to Time Series data. Examples were implemented using Keras. See links in slide pack.
Performance Comparison between Pytorch and Mindsporeijdms
Deep learning has been well used in many fields. However, there is a large amount of data when training neural networks, which makes many deep learning frameworks appear to serve deep learning practitioners, providing services that are more convenient to use and perform better. MindSpore and PyTorch are both deep learning frameworks. MindSpore is owned by HUAWEI, while PyTorch is owned by Facebook. Some people think that HUAWEI's MindSpore has better performance than FaceBook's PyTorch, which makes deep learning practitioners confused about the choice between the two. In this paper, we perform analytical and experimental analysis to reveal the comparison of training speed of MIndSpore and PyTorch on a single GPU. To ensure that our survey is as comprehensive as possible, we carefully selected neural networks in 2 main domains, which cover computer vision and natural language processing (NLP). The contribution of this work is twofold. First, we conduct detailed benchmarking experiments on MindSpore and PyTorch to analyze the reasons for their performance differences. This work provides guidance for end users to choose between these two frameworks.
SENTIMENT ANALYSIS FOR MOVIES REVIEWS DATASET USING DEEP LEARNING MODELSIJDKP
Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to 50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Naïve Bayes and RNTN that were published in other works using English datasets.
This lecture was delivered at the Intelligent systems and data mining workshop held in Faculty of Computers and information, Kafer Elshikh University On Wednesday 6 December 2017
This document provides an overview of deep learning and TensorFlow. It discusses machine learning and deep learning concepts. It then introduces TensorFlow as an open-source library for deep learning. Several TensorFlow examples and applications are demonstrated, including a deep neural network on MNIST data and a recurrent neural network for time series data. Considerations for running TensorFlow at scale on a cluster are also covered.
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Beginner's Guide to Diffusion Models..pptxIshaq Khan
1. Diffusion models are a new powerful family of deep generative models inspired by physics that destroy structure in data using a diffusion process and train a model to reverse the process.
2. Denoising Diffusion Probabilistic Models (DDPMs) were introduced to generate high-quality samples using a U-Net to predict noise levels and recover data from pure noise.
3. Improved DDPMs achieved competitive log-likelihoods while maintaining high sample quality through modifications like an improved noise schedule and optimization techniques.
Big data cloud-based recommendation system using NLP techniques with machine ...TELKOMNIKA JOURNAL
Recommendation systems (RS) are crucial for social networking sites. Without it, finding precise products is harder. However, existing systems lack adequate efficiency, especially with big data. This paper presents a prototype cloud-based recommendation system for processing big data. The proposed work is implemented by utilizing the matrix factorization method with three approaches. In the first approach, singular value decomposition (SVD) is used, which is an old and traditional recommendation technique. The second recommendation approach is fine-tuned using the alternating least squares (ALS) algorithm with Apache Spark. Finally, the deep neural network (DNN) algorithm is utilized with TensorFlow. This study solves the challenge of handling large-scale datasets in the collaborative filtering (CF) technique after tuning the algorithms by adjusting the parameters in the second approach, which uses machine learning, as well as in the third approach, which uses deep learning. Furthermore, the results of these two approaches outperformed conventional techniques and achieved an acceptable computational time. The dataset size is about 1.5 GB and it is collected from the Goodreads website API. Moreover, the Hadoop distributed file system (HDFS) is used as cloud storage instead of the computer’s local disk for handling larger dataset sizes in the future.
TensorFlow 16: Building a Data Science Platform Seldon
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3. DC/OS provides a full-stack platform to operationalize ML at scale through distributed computing resources, container orchestration, and integration of open source data and ML services.
AN INFORMATION THEORY-BASED FEATURE SELECTIONFRAMEWORK FOR BIG DATA UNDER APA...Nexgen Technology
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
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The aim of the proposed research will be to develop software for implementing a parallel solution for the RSA decryption algorithm. Multithread and distributed computing methods will be used to reach the aimed objective. This effort will include the development of a hybrid OpenMP/MPI program to maximize the use of computational resources and, consequently, decrease the time to decrypt large ciphertexts.
Distributed Models Over Distributed Data with MLflow, Pyspark, and PandasDatabricks
Does more data always improve ML models? Is it better to use distributed ML instead of single node ML?
In this talk I will show that while more data often improves DL models in high variance problem spaces (with semi or unstructured data) such as NLP, image, video more data does not significantly improve high bias problem spaces where traditional ML is more appropriate. Additionally, even in the deep learning domain, single node models can still outperform distributed models via transfer learning.
Data scientists have pain points running many models in parallel automating the experimental set up. Getting others (especially analysts) within an organization to use their models Databricks solves these problems using pandas udfs, ml runtime and MLflow.
The document discusses Keras, a Python deep learning library that allows for easy and fast prototyping of convolutional and recurrent neural networks. It presents an outline for a talk that introduces deep learning concepts and architectures like CNNs and RNNs. It then demonstrates how to model applications in image recognition, simulated car control, and speech recognition using Keras' simple API and layers. Code walkthroughs and demos are provided for each application.
Module 04 Content· As a continuation to examining your policies, rIlonaThornburg83
Module 04 Content
· As a continuation to examining your policies, review for procedures that may relate to them.
· In a 4-page paper, describe the procedures for each of the two compliance plans. NOTE: procedures are not the same thing as policies. Policies (Module 03) sets the parameters for decision-making, while procedures explains the "how". The procedures should be step-by-step instructions, and may include a checklist or process steps to follow.
· Break each procedure section into 2 pages each.
· Remember to support your procedures for each of two plans with a total of three research sources (1-2 per procedure), cited at the end in APA format.
· Write your procedures in a way that all employees will understand at a large medical facility where you are the Compliance Officer.
· Remember, you chose two compliance policy plans under the key compliance areas of Compliance Standards, High-Level Responsibility, Education, Communication, Monitoring/Auditing (for Safety), Enforcement/Discipline, and Response/Prevention. (Check them out if you forget! Remember, you may have written about different policies for the two different compliance plans.)
·
· Submit your completed assignment by following the directions linked below. Please check the Course Calendar for specific due dates.
·
· Save your assignment as a Microsoft Word document. (Mac users, please remember to append the ".docx" extension to the filename.) The name of the file should be your first initial and last name, followed by an underscore and the name of the assignment, and an underscore and the date. An example is shown below:
· Jstudent_exampleproblem_101504
1
8
Southeast Missouri State University
Department of Computer Science
Name of the Instructor: Dr. Reshmi Mitra
CS – 609 Graduate Project
December 2021
Team Member List
Jing Ma
Manoj Thapa
Nagendra Mokara
Contents
Machine learning on Stock Predition 0
Abstract 4
1 INTRODUCTION 4
1.1 LSTM Prediction 5
2 BACKGROUND AND RELATED WORK 6
2.1 Overview of the design 7
3 RESEARCH DESIGN AND METHODS 8
3.1 Stock Price Prediction Project using LSTM 8
3.2 Build the dashboard using plotly 9
4 EVALUTION, VALIDATION ANF KEYRESULTS 9
4.1 Screenshot of Result 10
5 CONCLUSIONS 11
References 12
Sound Classification using Deep Learning ……………………………………………...13
Abstract ………………………………………………………………………………….14
6 INTRODUCTION …………………………………………………………………….14
6.1 Keywords ……………………………………………………………………………15
7 COLLECTION OF DATA FROM THE DATASET …………………………………..15
7.1 Dataset ……………………………………………………………………………….15
7.2 Segregation of Data into Various Folders ..………………………………………….15
8 WRITING CODE AND DATA MODELING/TRAINING……………………………16
8.1 Design Diagram of the Project……………………………………………………….16
9 RESULT ……………………………………………………………………………….17
10 CONCLUSION/FUTURE WORK …………………………………………………..18
Machine learning on stock prediction
Jing Ma
December 2021
Southeast Missouri State University
Advisor: Dr Robert Lowe
Course: Researc ...
AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...Dr. Haxel Consult
Word embeddings, deep learning, transformer models and other pre-trained neural language models (sometimes recently referred to as "foundational models") have fundamentally changed the way state-of-the-art systems for natural language processing and information access are built today. The "Data-to-Value" process methodology (Leidner 2013; Leidner 2022a,b) has been devised to embody best practices for the construction of natural language engineering solutions; it can assist practitioners and has also been used to transfer industrial insights into the university classroom. This talk recaps how the methodology supports engineers in building systems more consistently and then outlines the changes in the methodology to adapt it to the deep learning age. The cost and energy implications will also be discussed.
Performance Improvement of Heterogeneous Hadoop Cluster using Ranking AlgorithmIRJET Journal
This document proposes using a ranking algorithm and sampling algorithm to improve the performance of a heterogeneous Hadoop cluster. The ranking algorithm prioritizes data distribution based on node frequency, so that higher frequency nodes are processed first. The sampling algorithm randomly selects nodes for data distribution instead of evenly distributing across all nodes. The proposed approach reduces computation time and improves overall cluster performance compared to the existing approach of evenly distributing data across nodes of varying sizes. Results show the proposed approach reduces execution time for various file sizes compared to the existing approach.
Multi-threaded approach in generating frequent itemset of Apriori algorithm b...TELKOMNIKA JOURNAL
This research is about the application of multi-threaded and trie data structures to the support calculation problem in the Apriori algorithm.
The support calculation results can search the association rule for market basket analysis problems. The support calculation process is a bottleneck process and can cause delays in the following process. This work observed five multi-threaded models based on Flynn’s taxonomy, which are single process, multiple data (SPMD), multiple process, single data (MPSD), multiple process, multiple data (MPMD), double SPMD first variant, and double SPMD second variant to shorten the processing time of the support calculation. In addition to the processing time, this works also consider the time difference between each multi-threaded model when the number of item variants increases. The time obtained from the experiment shows that the multi-threaded model that applies a double SPMD variant structure can perform almost three times faster than the multi-threaded model that applies the SPMD structure, MPMD structure, and combination of MPMD and SPMD based on the time difference of 5-itemsets and 10-itemsets experimental result.
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Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
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https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
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DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
Introduction to neural networks and Keras
1. Introduction to Neural Networks and Keras
Meetup - Big data - Montpellier
Jie He
TabMo
June 29, 2017
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 1 / 29
2. Investment trending of Artificial Intelligence (AI)
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 2 / 29
3. Deep Learning and Artificial Intelligence
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 3 / 29
4. Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 4 / 29
5. Outline
1 Brief History
2 Information processing in DNNs
3 Implementation with Keras
4 Summary
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 5 / 29
6. Brief History How Deep Neural networks was developed
Outline
1 Brief History
How Deep Neural networks was developed
Wide applications
2 Information processing in DNNs
Data representation
Data transformation
How to train a DNN
3 Implementation with Keras
Which platform and language to use
how to build a DNN model
Demo
Binary classification model
Decimal addition model
4 Summary
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 6 / 29
7. Brief History How Deep Neural networks was developed
1943 - Walter Pitts and Warren McCulloch proposed the first
mathematical model of a neural network.
1965 - Ivakhnenko and Lapa developed the earliest deep-learning-like
algorithms which had multiple layers. But the training process was
not optimized.
1970s - AI winter came as a result of reduced funding and interest
in AI research (which was due to the chain reaction of the AI hype).
1989 - Yann LeCun provided the first practical demonstration of
backpropagation at Bell Labs. He also combined convolutional
neural networks with backpropagation to read handwritten digits.
1985-90s - the second AI winter kicked in. Nevertheless, some
significant advances were made, e.g., support vector machine, long
short-term memory.
And now - AI regains its popularity. 62% of organizations will be
using AI Technologies by 2018, according to the survey from
Narrative Science.
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 7 / 29
8. Brief History Wide applications
Outline
1 Brief History
How Deep Neural networks was developed
Wide applications
2 Information processing in DNNs
Data representation
Data transformation
How to train a DNN
3 Implementation with Keras
Which platform and language to use
how to build a DNN model
Demo
Binary classification model
Decimal addition model
4 Summary
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 8 / 29
9. Brief History Wide applications
Background
Availability of big data Increase of computational power
Improved algorithms/architectures
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 9 / 29
10. Brief History Wide applications
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 10 / 29
11. Information processing in DNNs Data representation
Outline
1 Brief History
How Deep Neural networks was developed
Wide applications
2 Information processing in DNNs
Data representation
Data transformation
How to train a DNN
3 Implementation with Keras
Which platform and language to use
how to build a DNN model
Demo
Binary classification model
Decimal addition model
4 Summary
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 11 / 29
12. Information processing in DNNs Data representation
Three kinds of variables
Categorical variables
A finite number of categories or distinct groups. Categorical data might
not have a logical order. E.g., gender - male/female
Discrete variables
Numeric variables that have a countable number of values between any
two values. E.g., the number of children in a family - 3.
Continuous variables
Numeric variables that have an infinite number of values between any
two values. E.g., temperature - 25.9
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 12 / 29
13. Information processing in DNNs Data representation
Tensor representation of data
What is tensor?
Tensor is a general name of multidimensional array numeric data.
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 13 / 29
14. Information processing in DNNs Data transformation
Outline
1 Brief History
How Deep Neural networks was developed
Wide applications
2 Information processing in DNNs
Data representation
Data transformation
How to train a DNN
3 Implementation with Keras
Which platform and language to use
how to build a DNN model
Demo
Binary classification model
Decimal addition model
4 Summary
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 14 / 29
15. Information processing in DNNs Data transformation
Problem definition
Binary classification
Table: Data examples
x1 x2 label
1.4 2.7 0
3.8 3.4 0
1.5 0.7 1
2.5 3.1 0
0.5 0.3 1
1.2 2.8 0
... ... ...
Demo on data transformation
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 15 / 29
16. Information processing in DNNs Data transformation
The magic behind the transformation
Perceptron
y = f ( xi wi )
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 16 / 29
17. Information processing in DNNs Data transformation
The magic behind the transformation
Perceptron
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 17 / 29
18. Information processing in DNNs Data transformation
The magic behind the transformation
Stack layers of perceptron cells
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 18 / 29
19. Information processing in DNNs How to train a DNN
Outline
1 Brief History
How Deep Neural networks was developed
Wide applications
2 Information processing in DNNs
Data representation
Data transformation
How to train a DNN
3 Implementation with Keras
Which platform and language to use
how to build a DNN model
Demo
Binary classification model
Decimal addition model
4 Summary
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 19 / 29
20. Information processing in DNNs How to train a DNN
Training a DNN means finding the optimal weights
Forward Propagation and Back Propagation
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 20 / 29
21. Implementation with Keras Which platform and language to use
Outline
1 Brief History
How Deep Neural networks was developed
Wide applications
2 Information processing in DNNs
Data representation
Data transformation
How to train a DNN
3 Implementation with Keras
Which platform and language to use
how to build a DNN model
Demo
Binary classification model
Decimal addition model
4 Summary
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 21 / 29
22. Implementation with Keras Which platform and language to use
Which platform and language to use
Low-level: Google’s Tensorflow, Microsoft’s CNTK, Apple’s core ML,
Amazon’s mxnet, and University of Montreal’s theano, PyTorch...
High-level: Keras, TFLearn, TensorLayer
About Keras
Keras is a model-level library, providing high-level building blocks for
developing deep learning models, and has plugged several different backend
engines:TensorFlow backend, Theano backend and CNTK backend.
If you’re a beginner and interested in quickly implementing your ideas
Python + Keras: Super fast implementation, good extensibility
If you want to do fundamental research in Deep Learning
Python + Tensorflow or PyTorch: Excellent extensibility
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 22 / 29
23. Implementation with Keras how to build a DNN model
Outline
1 Brief History
How Deep Neural networks was developed
Wide applications
2 Information processing in DNNs
Data representation
Data transformation
How to train a DNN
3 Implementation with Keras
Which platform and language to use
how to build a DNN model
Demo
Binary classification model
Decimal addition model
4 Summary
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 23 / 29
24. Implementation with Keras how to build a DNN model
4 steps to apply a DNN model
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 24 / 29
25. Implementation with Keras Demo
Outline
1 Brief History
How Deep Neural networks was developed
Wide applications
2 Information processing in DNNs
Data representation
Data transformation
How to train a DNN
3 Implementation with Keras
Which platform and language to use
how to build a DNN model
Demo
Binary classification model
Decimal addition model
4 Summary
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 25 / 29
26. Implementation with Keras Demo
1 Binary classification model
Table: Synthetic data
f0 f1 f2 f3 f4 f5 f6 f7 f8 f9 label
0 16 41 3 95 45 8 19 56 95 40 0
1 26 79 51 85 55 40 35 7 54 70 1
2 17 79 48 14 17 37 3 84 66 22 0
3 85 42 12 70 51 9 51 51 0 37 0
4 88 28 61 22 80 52 10 74 7 27 1
... ... ...
Synthetic pattern
The label is 1 if:
f 0 > 67, f 1 < 32, f 2 mod 7 < 3, f 3 mod 30 > 12
Demo on Sublime
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 26 / 29
27. Implementation with Keras Demo
2 Decimal addition model
How to do decimal addition on computer? Like ?
Use a calculator, how simple that would be!
But... a Deep Neural Network can do that too,
and in a human-like manner!
Figure: Recurrent Neural Network (from here)
Demo on Sublime
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 27 / 29
28. Summary
Pros and cons about DNNs
Pros
No need for feature engineering
No limit for data volume
Fast training on huge data
Sustainability - incremental learning
Cons
Too many hyperparameters and variants
Easy to train a good model but difficult to get an excellent one
Black box - how the model works
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 28 / 29
29. Appendix
For Further Reading
Christopher Olah.
Neural Networks, Manifolds, and Topology
Jason Brownlee.
Time Series Prediction with LSTM Recurrent Neural Networks in
Python with Keras
Andrew Trask.
A Neural Network in 11 lines of Python
Jie He (TabMo) Introduction to Neural Networks and Keras June 29, 2017 29 / 29