Dispelling Myths and Making Cents of Multifamily Green Building CertificationKEPHART
Green building certification is a great way to add value to a community for both owners and residents; however, it is often not pursued due to misconceptions that the process is too expensive and complicated. Plus, with so many certification programs out there, how do you decide which one to use? Join a panel of industry experts as they dispel some of the common myths about the complexity of certification as well as compare and contrast the costs and benefits of the most popular programs being used- including the National Green Building Standard (NGBS), Energy Star and LEED.
This document contains a summary and details of Ranjit Show's work experience as a manual tester, including over 2 years of experience testing web and client/server applications in the insurance and banking domains. It lists his technical skills and tools used, such as HP ALM, SQL Server, and various browsers. His responsibilities included test case development, defect management, and regression testing. The document also provides his education qualifications and personal details.
Deep Learning for Computer Vision: A comparision between Convolutional Neural...Vincenzo Lomonaco
In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general.
However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of “intelligence”.
The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them.
CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems.
HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain.
In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.
This document provides a summary of Vasantha Navaneeeth's professional experience in software testing over 8.5 years. It details his testing experience on various banking, financial, and telecommunications projects. It also lists his technical skills, tools experience, education qualifications, and employment history with companies like Scope International, IBM, Accenture, and Wipro Technologies.
Dispelling Myths and Making Cents of Multifamily Green Building CertificationKEPHART
Green building certification is a great way to add value to a community for both owners and residents; however, it is often not pursued due to misconceptions that the process is too expensive and complicated. Plus, with so many certification programs out there, how do you decide which one to use? Join a panel of industry experts as they dispel some of the common myths about the complexity of certification as well as compare and contrast the costs and benefits of the most popular programs being used- including the National Green Building Standard (NGBS), Energy Star and LEED.
This document contains a summary and details of Ranjit Show's work experience as a manual tester, including over 2 years of experience testing web and client/server applications in the insurance and banking domains. It lists his technical skills and tools used, such as HP ALM, SQL Server, and various browsers. His responsibilities included test case development, defect management, and regression testing. The document also provides his education qualifications and personal details.
Deep Learning for Computer Vision: A comparision between Convolutional Neural...Vincenzo Lomonaco
In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general.
However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of “intelligence”.
The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them.
CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems.
HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain.
In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.
This document provides a summary of Vasantha Navaneeeth's professional experience in software testing over 8.5 years. It details his testing experience on various banking, financial, and telecommunications projects. It also lists his technical skills, tools experience, education qualifications, and employment history with companies like Scope International, IBM, Accenture, and Wipro Technologies.