This document discusses techniques for unsupervised feature learning from unlabeled data using neural networks. It describes using sparse autoencoders to learn feature hierarchies in an unsupervised manner by training networks to reconstruct their inputs while enforcing sparsity constraints. Convolutional deep belief networks are also discussed as a method for hierarchical probabilistic modeling of audio, images and video. The document concludes that unsupervised feature learning has achieved state-of-the-art results on various tasks such as object classification, activity recognition and speech processing.
Anomaly detection using deep one class classifier홍배 김
- Anomaly detection의 다양한 방법을 소개하고
- Support Vector Data Description (SVDD)를 이용하여
cluster의 모델링을 쉽게 하도록 cluster의 형상을 단순화하고
boundary근방의 애매한 point를 처리하는 방법 소개
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
#6 PyData Warsaw: Deep learning for image segmentationMatthew Opala
Deep learning techniques ignited a great progress in many computer vision tasks like image classification, object detection, and segmentation. Almost every month a new method is published that achieves state-of-the-art result on some common benchmark dataset. In addition to that, DL is being applied to new problems in CV.
In the talk we’re going to focus on DL application to image segmentation task. We want to show the practical importance of this task for the fashion industry by presenting our case study with results achieved with various attempts and methods.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data anlytics tools.
Anomaly detection using deep one class classifier홍배 김
- Anomaly detection의 다양한 방법을 소개하고
- Support Vector Data Description (SVDD)를 이용하여
cluster의 모델링을 쉽게 하도록 cluster의 형상을 단순화하고
boundary근방의 애매한 point를 처리하는 방법 소개
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
#6 PyData Warsaw: Deep learning for image segmentationMatthew Opala
Deep learning techniques ignited a great progress in many computer vision tasks like image classification, object detection, and segmentation. Almost every month a new method is published that achieves state-of-the-art result on some common benchmark dataset. In addition to that, DL is being applied to new problems in CV.
In the talk we’re going to focus on DL application to image segmentation task. We want to show the practical importance of this task for the fashion industry by presenting our case study with results achieved with various attempts and methods.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data anlytics tools.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
High-Performance GPU Programming for Deep LearningIntel Nervana
This session goes over many of the techniques we use at Nervana in GPU programming to achieve state-of-the-art performance for deep learning networks. The main focus will be on the customization of dense linear algebra kernels: Winograd 3x3 convolution, direct convolution, and small tile GEMM (matrix multiply). In particular, we'll look at how we achieve high utilization at very small mini batches which is important for multi-gpu scaling and inference. In addition we'll talk about where and how you can effectively leverage lower and mixed precision to further increase performance without loss in accuracy.
Kicking off the first in a series of global GPU Technology Conferences, NVIDIA co-founder and CEO Jen-Hsun Huang today at GTC China unveiled technology that will accelerate the deep learning revolution that is sweeping across industries. Huang spoke in front of a crowd of more than 2,500 scientists, engineers, entrepreneurs and press, gathered in Beijing for a day devoted to deep learning and AI. On stage he announced the Tesla P4 and P40 GPU accelerators for inferencing production workloads for AI services and, a small, energy-efficient AI supercomputer for highway driving — the NVIDIA DRIVE PX 2 for AutoCruise.
Deep Water - GPU Deep Learning for H2O - Arno CandelSri Ambati
Deep Water brings the latest and greatest in the Deep Learning space all under the H2O hood. Use Tensorflow, Mxnet & Caffe all from standard H2O interface's including R, Python & Flow. Also deploy your models easily using the H2O platform.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree SearchKarel Ha
the presentation of the article "Mastering the game of Go with deep neural networks and tree search" given at the Optimization Seminar 2015/2016
Notes:
- All URLs are clickable.
- All citations are clickable (when hovered over the "year" part of "[author year]").
- To download without a SlideShare account, use https://www.dropbox.com/s/p4rnlhoewbedkjg/AlphaGo.pdf?dl=0
- The corresponding leaflet is available at http://www.slideshare.net/KarelHa1/leaflet-for-the-talk-on-alphago
- The source code is available at https://github.com/mathemage/AlphaGo-presentation
The slides go through the implementation details of Google Deepmind's AlphaGo, a computer Go AI that defeated the European champion. The slides are targeted for beginners in the machine learning area.
Korean version (한국어 버젼): http://www.slideshare.net/ShaneSeungwhanMoon/ss-59226902
Research 101 - Paper Writing with LaTeXJia-Bin Huang
Paper Writing with LaTeX
PDF: https://filebox.ece.vt.edu/~jbhuang/slides/Research%20101%20-%20Paper%20Writing%20with%20LaTeX.pdf
PPTX: https://filebox.ece.vt.edu/~jbhuang/slides/Research%20101%20-%20Paper%20Writing%20with%20LaTeX.pptx
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. During this workshop, members of the Amazon Machine Learning team will provide a short background on Deep Learning focusing on relevant application domains and an introduction to using the powerful and scalable Deep Learning framework, MXNet. At the end of this tutorial you’ll gain hands on experience targeting a variety of applications including computer vision and recommendation engines as well as exposure to how to use preconfigured Deep Learning AMIs and CloudFormation Templates to help speed your development.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and elementary calculus (derivatives), are helpful in order to derive the maximum benefit from this session.
Next we'll see a simple neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
生成式對抗網路 (Generative Adversarial Network, GAN) 顯然是深度學習領域的下一個熱點,Yann LeCun 說這是機器學習領域這十年來最有趣的想法 (the most interesting idea in the last 10 years in ML),又說這是有史以來最酷的東西 (the coolest thing since sliced bread)。生成式對抗網路解決了什麼樣的問題呢?在機器學習領域,回歸 (regression) 和分類 (classification) 這兩項任務的解法人們已經不再陌生,但是如何讓機器更進一步創造出有結構的複雜物件 (例如:圖片、文句) 仍是一大挑戰。用生成式對抗網路,機器已經可以畫出以假亂真的人臉,也可以根據一段敘述文字,自己畫出對應的圖案,甚至還可以畫出二次元人物頭像 (左邊的動畫人物頭像就是機器自己生成的)。本課程希望能帶大家認識生成式對抗網路這個深度學習最前沿的技術。
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks. You'll also learn how to incorporate Deep Learning in Android applications. Basic knowledge of matrices is helpful for this session, which is targeted primarily to beginners.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
7. Self-taught learning Sparse coding, LCC, etc. , …, k Use learned , …, k to represent training/test sets. Using , …, k a a , …, a k If have labeled training set is small, can give huge performance boost. Car Motorcycle
12. Logistic regression Logistic regression has a learned parameter vector . On input x, it outputs: where Draw a logistic regression unit as: x 1 x 2 x 3 +1
13.
14. Neural Network x 1 x 2 x 3 +1 +1 Layer 1 Layer 2 Layer 4 +1 Layer 3 Example 4 layer network with 2 output units:
18. Unsupervised feature learning with a neural network Training a sparse autoencoder. Given unlabeled training set x 1 , x 2 , … Reconstruction error term L 1 sparsity term a 1 a 2 a 3
19. Unsupervised feature learning with a neural network x 4 x 5 x 6 +1 Layer 1 Layer 2 x 1 x 2 x 3 x 4 x 5 x 6 x 1 x 2 x 3 +1 Layer 3 a 1 a 2 a 3
20. Unsupervised feature learning with a neural network x 4 x 5 x 6 +1 Layer 1 Layer 2 x 1 x 2 x 3 +1 a 1 a 2 a 3 New representation for input.
22. Unsupervised feature learning with a neural network x 4 x 5 x 6 +1 x 1 x 2 x 3 +1 a 1 a 2 a 3 +1 b 1 b 2 b 3 Train parameters so that , subject to b i ’s being sparse.
23. Unsupervised feature learning with a neural network x 4 x 5 x 6 +1 x 1 x 2 x 3 +1 a 1 a 2 a 3 +1 b 1 b 2 b 3 Train parameters so that , subject to b i ’s being sparse.
24. Unsupervised feature learning with a neural network x 4 x 5 x 6 +1 x 1 x 2 x 3 +1 a 1 a 2 a 3 +1 b 1 b 2 b 3 Train parameters so that , subject to b i ’s being sparse.
25. Unsupervised feature learning with a neural network x 4 x 5 x 6 +1 x 1 x 2 x 3 +1 a 1 a 2 a 3 +1 b 1 b 2 b 3 New representation for input.
27. Unsupervised feature learning with a neural network x 4 x 5 x 6 +1 x 1 x 2 x 3 +1 a 1 a 2 a 3 +1 b 1 b 2 b 3 +1 c 1 c 2 c 3
28. Unsupervised feature learning with a neural network x 4 x 5 x 6 +1 x 1 x 2 x 3 +1 a 1 a 2 a 3 +1 b 1 b 2 b 3 +1 c 1 c 2 c 3 New representation for input. Use [c 1 , c 3 , c 3 ] as representation to feed to learning algorithm.
29.
30. Restricted Boltzmann machine (RBM) Input [x 1, x 2 , x 3 , x 4 ] Layer 2. [a 1, a 2 , a 3 ] (binary-valued) MRF with joint distribution: Use Gibbs sampling for inference. Given observed inputs x, want maximum likelihood estimation: x 4 x 1 x 2 x 3 a 2 a 3 a 1
31. Restricted Boltzmann machine (RBM) Input [x 1, x 2 , x 3 , x 4 ] Layer 2. [a 1, a 2 , a 3 ] (binary-valued) Gradient ascent on log P(x) : [x i a j ] obs from fixing x to observed value, and sampling a from P(a|x). [x i a j ] prior from running Gibbs sampling to convergence. Adding sparsity constraint on a i ’s usually improves results. x 4 x 1 x 2 x 3 a 2 a 3 a 1
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33. Deep Belief Network Input [x 1, x 2 , x 3 , x 4 ] Layer 2. [a 1, a 2 , a 3 ] Layer 3. [b 1, b 2 , b 3 ] Layer 4. [c 1, c 2 , c 3 ]
37. Probabilistic max pooling X 3 X 1 X 2 X 4 max {x 1 , x 2 , x 3 , x 4 } Convolutional Neural net: Convolutional DBN: Where x i are real numbers. Where x i are {0,1}, and mutually exclusive . Thus, 5 possible cases: Collapse 2 n configurations into n+1 configurations. Permits bottom up and top down inference. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 X 3 X 1 X 2 X 4 max {x 1 , x 2 , x 3 , x 4 }
41. Convolutional DBN for Images Visible nodes (binary or real) At most one hidden nodes are active. Hidden nodes (binary) “ Filter” weights (shared) Input data V W k Detection layer H Max-pooling layer P ‘’ max-pooling’’ node (binary)
42. Convolutional DBN on face images pixels edges object parts (combination of edges) object models Note: Sparsity important for these results.
43. Learning of object parts Examples of learned object parts from object categories Faces Cars Elephants Chairs
44. Training on multiple objects Plot of H (class|neuron active) Trained on 4 classes (cars, faces, motorbikes, airplanes). Second layer: Shared-features and object-specific features. Third layer: More specific features. Second layer bases learned from 4 object categories. Third layer bases learned from 4 object categories.
45. Hierarchical probabilistic inference Input images Samples from feedforward Inference (control ) Samples from Full posterior inference Generating posterior samples from faces by “filling in” experiments (cf. Lee and Mumford, 2003). Combine bottom-up and top-down inference.
50. State-of-the-art task performance Audio Images Multimodal (audio/video) Video TIMIT Phone classification Accuracy Prior art (Clarkson et al.,1999) 79.6% Stanford Feature learning 80.3% TIMIT Speaker identification Accuracy Prior art (Reynolds, 1995) 99.7% Stanford Feature learning 100.0% CIFAR Object classification Accuracy Prior art (Yu and Zhang, 2010) 74.5% Stanford Feature learning 75.5% NORB Object classification Accuracy Prior art (Ranzato et al., 2009) 94.4% Stanford Feature learning 96.2% AVLetters Lip reading Accuracy Prior art (Zhao et al., 2009) 58.9% Stanford Feature learning 63.1% UCF activity classification Accuracy Prior art (Kalser et al., 2008) 86% Stanford Feature learning 87% Hollywood2 classification Accuracy Prior art (Laptev, 2004) 47% Stanford Feature learning 50%
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Editor's Notes
Sometimes, most data wins. So, how to get more data? Even with AMT, often slow and expensive.
End: One of challenges is scaling up. Most people: 14x14 up to 32x32.
Time-invariant features
Visual bases: Look at them and see if make sense/correspond to Gabors. Try to perform similar analysis on audio bases.
Aglioti et al., 1994; Halligan et al., 1993; Weinstein, 1969; Ramachandran, 1998; Halligan et al., 1993; Sadato et al., 1996; Halligan et al., 1999
http://www.cbsnews.com/stories/2000/06/29/tech/main210684.shtml: 12.3 Tflops, $110 million, used to simulate nuclear weapon testing. Like 13 graphics cards costing $250 each. 40 people with US$250 graphics card #18 on top supercomputers list 2 years back. http://www.top500.org/list/2006/11/100