Tensors are higher order extensions of matrices that can incorporate multiple modalities and encode higher order relationships in data. Tensors play a significant role in machine learning through (1) tensor contractions, (2) tensor sketches, and (3) tensor decompositions. Tensor contractions are extensions of matrix products to higher dimensions. Tensor sketches efficiently compress tensors while preserving information. Tensor decompositions compute low rank components that constitute a tensor.
Slides taught in the course of pattern recognition of Professor Zohreh Azimifar at Shiraz University.
The slides are owned by the University of Texas.
اسلاید های تدریس شده درس شناسایی آماری الگو استاد زهره عظیمی فر در دانشگاه شیراز.
اسلاید ها متعلق به دانشگاه تگزاس است.
Recurrent Neural Network
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Mohammad Sabouri
https://sites.google.com/view/acrrl/
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.
In this talk, Dmitry shares his approach to feature engineering which he used successfully in various Kaggle competitions. He covers common techniques used to convert your features into numeric representation used by ML algorithms.
Tensors are higher order extensions of matrices that can incorporate multiple modalities and encode higher order relationships in data. Tensors play a significant role in machine learning through (1) tensor contractions, (2) tensor sketches, and (3) tensor decompositions. Tensor contractions are extensions of matrix products to higher dimensions. Tensor sketches efficiently compress tensors while preserving information. Tensor decompositions compute low rank components that constitute a tensor.
Slides taught in the course of pattern recognition of Professor Zohreh Azimifar at Shiraz University.
The slides are owned by the University of Texas.
اسلاید های تدریس شده درس شناسایی آماری الگو استاد زهره عظیمی فر در دانشگاه شیراز.
اسلاید ها متعلق به دانشگاه تگزاس است.
Recurrent Neural Network
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Mohammad Sabouri
https://sites.google.com/view/acrrl/
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.
In this talk, Dmitry shares his approach to feature engineering which he used successfully in various Kaggle competitions. He covers common techniques used to convert your features into numeric representation used by ML algorithms.
KNN Algorithm using Python | How KNN Algorithm works | Python Data Science Tr...Edureka!
** Python for Data Science: https://www.edureka.co/python **
This Edureka tutorial on KNN Algorithm will help you to build your base by covering the theoretical, mathematical and implementation part of the KNN algorithm in Python. Topics covered under this tutorial includes:
1. What is KNN Algorithm?
2. Industrial Use case of KNN Algorithm
3. How things are predicted using KNN Algorithm
4. How to choose the value of K?
5. KNN Algorithm Using Python
6. Implementation of KNN Algorithm from scratch
Check out our playlist: http://bit.ly/2taym8X
Slide for Arithmer Seminar given by Dr. Daisuke Sato (Arithmer) at Arithmer inc.
The topic is on "explainable AI".
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Interpreting deep learning and machine learning models is not just another regulatory burden to be overcome. Scientists, physicians, researchers, and analyst that use these technologies for their important work have the right to trust and understand their models and the answers they generate. This talk is an overview of several techniques for interpreting deep learning and machine learning models and telling stories from their results.
Speaker: Patrick Hall is a Data Scientist and Product Engineer at H2O.ai. He’s also an Adjunct Professor at George Washington University for the Department of Decision Sciences. Prior to joining H2O, Patrick spent many years as a Senior Data Scientist SAS and has worked with many Fortune 500 companies on their data science and machine learning problems. https://www.linkedin.com/in/jpatrickhall
KNN Algorithm using Python | How KNN Algorithm works | Python Data Science Tr...Edureka!
** Python for Data Science: https://www.edureka.co/python **
This Edureka tutorial on KNN Algorithm will help you to build your base by covering the theoretical, mathematical and implementation part of the KNN algorithm in Python. Topics covered under this tutorial includes:
1. What is KNN Algorithm?
2. Industrial Use case of KNN Algorithm
3. How things are predicted using KNN Algorithm
4. How to choose the value of K?
5. KNN Algorithm Using Python
6. Implementation of KNN Algorithm from scratch
Check out our playlist: http://bit.ly/2taym8X
Slide for Arithmer Seminar given by Dr. Daisuke Sato (Arithmer) at Arithmer inc.
The topic is on "explainable AI".
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Interpreting deep learning and machine learning models is not just another regulatory burden to be overcome. Scientists, physicians, researchers, and analyst that use these technologies for their important work have the right to trust and understand their models and the answers they generate. This talk is an overview of several techniques for interpreting deep learning and machine learning models and telling stories from their results.
Speaker: Patrick Hall is a Data Scientist and Product Engineer at H2O.ai. He’s also an Adjunct Professor at George Washington University for the Department of Decision Sciences. Prior to joining H2O, Patrick spent many years as a Senior Data Scientist SAS and has worked with many Fortune 500 companies on their data science and machine learning problems. https://www.linkedin.com/in/jpatrickhall
8. وسنتناول بالشرح ما
يلى
• تحليل الفرق بين مربعين.
• تحليل مجموع مكعبين والفرق
بينهما.
• التحليل بالتقسيم.
9. تحليل الفرق بين مربعين
الفرق بين مربعين هو الفرق بين مربعى
عددين وتحليله يكون كالتى:
)س – ص (= )س + ص()س – ص(2 2
الفرق بين مربعى الكميتين= مجموع الكميتين × الفرق بينهما
