PyTorch is one of the most widely used deep learning library in python community. In this talk I will cover the basic to advanced guide to implement deep learning model using PyTorch. My goal is to introduce PyTorch and show how to use it for deep learning project.
Simulation of Single and Multilayer of Artificial Neural Network using Verilogijsrd.com
Artificial neural network play an important role in VLSI circuit to find and diagnosis multiple fault in digital circuit. In this paper, the example of single layer and multi-layer neural network had been discussed secondly implement those structure by using verilog code and same idea must be implement in mat lab for getting number of iteration and verilog code gives us time taken to adjust the weight when error become almost equal to zero. The purposed aim at reducing resource requirement, without much compromises on the speed that neural network can be realized on single chip at lower cost.
PyTorch is one of the most widely used deep learning library in python community. In this talk I will cover the basic to advanced guide to implement deep learning model using PyTorch. My goal is to introduce PyTorch and show how to use it for deep learning project.
Simulation of Single and Multilayer of Artificial Neural Network using Verilogijsrd.com
Artificial neural network play an important role in VLSI circuit to find and diagnosis multiple fault in digital circuit. In this paper, the example of single layer and multi-layer neural network had been discussed secondly implement those structure by using verilog code and same idea must be implement in mat lab for getting number of iteration and verilog code gives us time taken to adjust the weight when error become almost equal to zero. The purposed aim at reducing resource requirement, without much compromises on the speed that neural network can be realized on single chip at lower cost.
Deep Learning in Python with Tensorflow for FinanceBen Ball
Speaker: Ben Ball
Abstract: Python is becoming the de facto standard for many machine learning applications. We have been using Python with deep learning and other ML techniques, with a focus in prediction and exploitation in transactional markets. I am presenting one of our implementations (A dueling double DQN - a class of Reinforcement Learning algorithm) in Python using TensorFlow, along with information and background around the class of deep learning algorithm, and the application to financial markets we have employed. Attendees will learn how to implement a DQN using Tensorflow, and how to design a system for deep learning for solving a wide range of problems. The code will be available on github for attendees.
Bio: Ben is a believer in making a career out of what you love. He is inspired by the joining of excellent technology and research and likes building software that is easy to use, but does amazing things. His work has spanned 15 years, with a dual focus in AI Software Engineering and Algorithmic Trading. He is currently working as the CTO of http://prediction-machines.com
Video of the presentation:
https://engineers.sg/video/deep-learning-with-python-in-finance-singapore-python-user-group--1875
This presentation begins with explaining the basic algorithms of machine learning and using the same concepts, discusses in detail 2 supervised learning/deep learning algorithms - Artificial neural nets and Convolutional Neural Nets. The relationship between Artificial neural nets and basic machine learning algorithms such as logistic regression and soft max is also explored. For hands on the implementation of ANN's and CNN's on MNIST dataset is also explained.
I am Andrew O. I am a Computer Science Assignment Help Expert at programminghomeworkhelp.com. I hold a Ph.D. in Programming, Southampton, UK. I have been helping students with their homework for the past 10 years. I solve assignments related to Computer Science.
Visit programminghomeworkhelp.com or email support@programminghomeworkhelp.com.You can also call on +1 678 648 4277 for any assistance with Computer Science assignments.
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...MLconf
Understanding Deep Learning for Big Data: The complexity and scale of big data impose tremendous challenges for their analysis. Yet, big data also offer us great opportunities. Some nonlinear phenomena, features or relations, which are not clear or cannot be inferred reliably from small and medium data, now become clear and can be learned robustly from big data. Typically, the form of the nonlinearity is unknown to us, and needs to be learned from data as well. Being able to harness the nonlinear structures from big data could allow us to tackle problems which are impossible before or obtain results which are far better than previous state-of-the-arts.
Nowadays, deep neural networks are the methods of choice when it comes to large scale nonlinear learning problems. What makes deep neural networks work? Is there any general principle for tackling high dimensional nonlinear problems which we can learn from deep neural works? Can we design competitive or better alternatives based on such knowledge? To make progress in these questions, my machine learning group performed both theoretical and experimental analysis on existing and new deep learning architectures, and investigate three crucial aspects on the usefulness of the fully connected layers, the advantage of the feature learning process, and the importance of the compositional structures. Our results point to some promising directions for future research, and provide guideline for building new deep learning models.
Aaa ped-23-Artificial Neural Network: Keras and TensorfowAminaRepo
We will focus in this part on two important libraries for ANN: Tensorflow and Keras. Both of them propose two types of model creation. We will use the high level API of tenshorflow, and the sequential models of Keras.
We will introduce you to some basic important concept related to tensorflow, and we will present you tensorboard. The later one is used to visualize, among other things, quantitative values related to a training process.
[Notebook](https://colab.research.google.com/drive/13KlhoNvYmeRZTZ-TLKAtW3rOkFzQVGYC)
Aaa ped-22-Artificial Neural Network: Introduction to ANNAminaRepo
Finally we will talk about Artificial Neural Networks (ANN) . In this part we will focus on the building blocks of ANN's. We will describe the perceptron and its learning rules. We will talk in more details about the gradient descent algorithm.
We will also define an important concept in ANN which is : the back-propagation algorithm. For our examples we will use two libraries: neurolab and scikit-learn libraries.
[Notebook](https://colab.research.google.com/drive/1CqYwu9NzeXuUNeR8RmkDplUd71DHWNod)
Sergei Vassilvitskii, Research Scientist, Google at MLconf NYC - 4/15/16MLconf
Teaching K-Means New Tricks: Over 50 years old, the k-means algorithm remains one of the most popular clustering algorithms. In this talk we’ll cover some recent developments, including better initialization, the notion of coresets, clustering at scale, and clustering with outliers.
What is pattern recognition (lecture 3 of 6)Randa Elanwar
In this series I intend to simplify a beautiful branch of computer science that we as humans use it in everyday life without knowing. Pattern recognition is a sub-branch of the computer vision research and is tightly related to digital signal processing research as well as machine learning and artificial intelligence.
What is pattern_recognition (lecture 2 of 6)Randa Elanwar
In this series I intend to simplify a beautiful branch of computer science that we as humans use it in everyday life without knowing. Pattern recognition is a sub-branch of the computer vision research and is tightly related to digital signal processing research as well as machine learning and artificial intelligence
Deep Learning in Python with Tensorflow for FinanceBen Ball
Speaker: Ben Ball
Abstract: Python is becoming the de facto standard for many machine learning applications. We have been using Python with deep learning and other ML techniques, with a focus in prediction and exploitation in transactional markets. I am presenting one of our implementations (A dueling double DQN - a class of Reinforcement Learning algorithm) in Python using TensorFlow, along with information and background around the class of deep learning algorithm, and the application to financial markets we have employed. Attendees will learn how to implement a DQN using Tensorflow, and how to design a system for deep learning for solving a wide range of problems. The code will be available on github for attendees.
Bio: Ben is a believer in making a career out of what you love. He is inspired by the joining of excellent technology and research and likes building software that is easy to use, but does amazing things. His work has spanned 15 years, with a dual focus in AI Software Engineering and Algorithmic Trading. He is currently working as the CTO of http://prediction-machines.com
Video of the presentation:
https://engineers.sg/video/deep-learning-with-python-in-finance-singapore-python-user-group--1875
This presentation begins with explaining the basic algorithms of machine learning and using the same concepts, discusses in detail 2 supervised learning/deep learning algorithms - Artificial neural nets and Convolutional Neural Nets. The relationship between Artificial neural nets and basic machine learning algorithms such as logistic regression and soft max is also explored. For hands on the implementation of ANN's and CNN's on MNIST dataset is also explained.
I am Andrew O. I am a Computer Science Assignment Help Expert at programminghomeworkhelp.com. I hold a Ph.D. in Programming, Southampton, UK. I have been helping students with their homework for the past 10 years. I solve assignments related to Computer Science.
Visit programminghomeworkhelp.com or email support@programminghomeworkhelp.com.You can also call on +1 678 648 4277 for any assistance with Computer Science assignments.
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...MLconf
Understanding Deep Learning for Big Data: The complexity and scale of big data impose tremendous challenges for their analysis. Yet, big data also offer us great opportunities. Some nonlinear phenomena, features or relations, which are not clear or cannot be inferred reliably from small and medium data, now become clear and can be learned robustly from big data. Typically, the form of the nonlinearity is unknown to us, and needs to be learned from data as well. Being able to harness the nonlinear structures from big data could allow us to tackle problems which are impossible before or obtain results which are far better than previous state-of-the-arts.
Nowadays, deep neural networks are the methods of choice when it comes to large scale nonlinear learning problems. What makes deep neural networks work? Is there any general principle for tackling high dimensional nonlinear problems which we can learn from deep neural works? Can we design competitive or better alternatives based on such knowledge? To make progress in these questions, my machine learning group performed both theoretical and experimental analysis on existing and new deep learning architectures, and investigate three crucial aspects on the usefulness of the fully connected layers, the advantage of the feature learning process, and the importance of the compositional structures. Our results point to some promising directions for future research, and provide guideline for building new deep learning models.
Aaa ped-23-Artificial Neural Network: Keras and TensorfowAminaRepo
We will focus in this part on two important libraries for ANN: Tensorflow and Keras. Both of them propose two types of model creation. We will use the high level API of tenshorflow, and the sequential models of Keras.
We will introduce you to some basic important concept related to tensorflow, and we will present you tensorboard. The later one is used to visualize, among other things, quantitative values related to a training process.
[Notebook](https://colab.research.google.com/drive/13KlhoNvYmeRZTZ-TLKAtW3rOkFzQVGYC)
Aaa ped-22-Artificial Neural Network: Introduction to ANNAminaRepo
Finally we will talk about Artificial Neural Networks (ANN) . In this part we will focus on the building blocks of ANN's. We will describe the perceptron and its learning rules. We will talk in more details about the gradient descent algorithm.
We will also define an important concept in ANN which is : the back-propagation algorithm. For our examples we will use two libraries: neurolab and scikit-learn libraries.
[Notebook](https://colab.research.google.com/drive/1CqYwu9NzeXuUNeR8RmkDplUd71DHWNod)
Sergei Vassilvitskii, Research Scientist, Google at MLconf NYC - 4/15/16MLconf
Teaching K-Means New Tricks: Over 50 years old, the k-means algorithm remains one of the most popular clustering algorithms. In this talk we’ll cover some recent developments, including better initialization, the notion of coresets, clustering at scale, and clustering with outliers.
What is pattern recognition (lecture 3 of 6)Randa Elanwar
In this series I intend to simplify a beautiful branch of computer science that we as humans use it in everyday life without knowing. Pattern recognition is a sub-branch of the computer vision research and is tightly related to digital signal processing research as well as machine learning and artificial intelligence.
What is pattern_recognition (lecture 2 of 6)Randa Elanwar
In this series I intend to simplify a beautiful branch of computer science that we as humans use it in everyday life without knowing. Pattern recognition is a sub-branch of the computer vision research and is tightly related to digital signal processing research as well as machine learning and artificial intelligence
تعريف بمدونة علماء مصر ومحاور التدريب على الكتابة للمدونينRanda Elanwar
مرحبا بكم فى التدريب الأساسى لمدونى علماء مصر
التدريب الأساسى هو فقط مقدمة شاملة لتوسيع المدارك، وتصحيح المفاهيم الخاطئة، ولا يهدف إلى تدريب متخصص فى أى المحاور التى يتناولها
أولا: المقدمة وفيها تعريف بأبواب المدونة وأمثلة للمواضيع الفرعية التى يمكنك الكتابة فيها ومحاور التدريب
ثانيا: المحور الأول وفيه تدريب على هدف وهيكل المقالات المبنية على البحث واختيار الكلمات المفتاحية مع أمثلة
ثالثا: المحور الثانى وفيه تدريب على هدف وهيكل المقالات الإخبارية مع أمثلة
رابعا: المحور الثالث وفيه تدريب على هدف وهيكل مقالات الموارد مع أمثلة
خامسا: المحور الرابع وفيه تدريب على فنيات الكتابة للمقالات والقصة القصيرة والخاطرة وتلخيص للأخطاء اللغوية والإملائية الشائعة وعلامات الترقيم
سادسا المحور الخامس وفيه تدريب على كيفية الترجمة والتلخيص وأهم النصائح والأدوات
What is pattern recognition (lecture 5 of 6)Randa Elanwar
In this series I intend to simplify a beautiful branch of computer science that we as humans use it in everyday life without knowing. Pattern recognition is a sub-branch of the computer vision research and is tightly related to digital signal processing research as well as machine learning and artificial intelligence.
The Mapping Network is a nationwide group of professionals providing the highest quality and most affordable bathymetric, sediment and asset mapping products on the market today. The Mapping Network has partners located throughout the county working directly with recreational property owners, lake associations, engineers, golf courses, marinas, developers, governmental organizations, and fishing clubs. A local expert aquatic service provider understands the dynamics of a lake to determine the best transect paths to precisely model the lake's bathymetry. With 12 years of experience in the bathymetry mapping profession, our highly trained staff understand and employ the latest technology trends to generate a bathymetric map that is amazingly accurate and also aesthetically enjoyable.
www.themappingnetwork.com
info@themappingnetwork.com
The eye gaze analysis represents a challenging field of
research, since it offers a reproducible method to study the mechanisms of the brain. Eye movements are arguably the most frequent of all human movements and an essential part of human vision: they drive the fovea and consequently, the attention towards regions of interest in space. This enables the visual system to fixate and to process an image or its details with high resolution: act of fixation. This chapter investigates some common techniques and algorithms to study human vision.
Main steps to build a digital library:
Data collection and digitization
Metadata selection and designing the digital library interface
Annotation of digitized data (may be word spotting as well)
Information retrieval techniques
RuleML2015: Similarity-Based Strict Equality in a Fully Integrated Fuzzy Logi...RuleML
The extension of a given similarity relation R between pairs
of symbols of a particular alphabet to terms built with such symbols
can be implemented at a very high abstract level by a set of fuzzy program
rules defining a predicate called sse. This predicate is defined for
incorporating “Similarity-based Strict Equality” into the new fuzzy logic
language FASILL (acronym of “Fuzzy Aggregators and Similarity Into
a Logic Language”) that we have recently developed in our research
group. FASILL aims to cope with implicit/explicit truth degree annotations,
a great variety of connectives and unification by similarity. In
this paper we show the benefits of using this sophisticated notion of
equality which is somehow inspired by the so-called “Strict Equality”
of functional and functional-logic languages with lazy semantics (e.g.:
Haskell and Curry respectively) and the “Similarity-based Equality”
of fuzzy logic languages using weak unification (Bousi∼Prolog, Likelog),
a notion beyond classic syntactic unification.
Brief introduction of neural network including-
1. Fitting Tool
2. Clustering data with a self-organising map
3. Pattern Recognition Tool
4. Time Series Toolbox
Towards neuralprocessingofgeneralpurposeapproximateprogramsParidha Saxena
Did validation of one of the machine learning algorithms of neural networks,and compared the results for its implementation on hardware (FPGA) using xilinx, with that of a sequential code execution(using FANN).
An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain's neural networks. It consists of interconnected nodes, often referred to as neurons or units, organized in layers. These layers typically include an input layer, one or more hidden layers, and an output layer.
This work is proposed the feed forward neural network with symmetric table addition method to design the
neuron synapses algorithm of the sine function approximations, and according to the Taylor series
expansion. Matlab code and LabVIEW are used to build and create the neural network, which has been
designed and trained database set to improve its performance, and gets the best a global convergence with
small value of MSE errors and 97.22% accuracy.
Plotting the training process
Regularization
Batch normalization
Saving and loading the weights and the architecture of a model
Visualize a Deep Learning Neural Network Model in Keras
Character Recognition using Artificial Neural NetworksJaison Sabu
Mini Project, Computer Science Department, College of Engineering Chengannur 2003-2007, Affiliated to Cochin University of Science and Technology (CUSAT), Kerala, India
(chapter 4) A Concise and Practical Introduction to Programming Algorithms in...Frank Nielsen
These are the slides accompanying the textbook:
A Concise and Practical Introduction to Programming Algorithms in Java
by Frank Nielsen
Published by Springer-Verlag (2009), Undergraduate textbook in computer science (UTiCS series)
ISBN: 978-1-84882-338-9
http://www.lix.polytechnique.fr/~nielsen/JavaProgramming/
http://link.springer.com/book/10.1007%2F978-1-84882-339-6
Auto-Scaling Apache Spark cluster using Deep Reinforcement Learning.pdfKundjanasith Thonglek
Real-time processing is a fast and prompt processing technology that needs to complete the execution within a limited time constraint almost equal to the input time. Executing such real-time processing needs an efficient auto-scaling system which provides sufficient resources to compute the process within the time constraint. We use Apache Spark framework to build a cluster which supports real-time processing. The major challenge of scaling Apache Spark cluster automatically for the real-time processing is how to handle the unpredictable input data size and also the unpredictable resource availability of the underlying cloud infrastructure. If the scaling-out of the cluster is too slow then the application can not be executed within the time constraint as a result of insufficient resources. If the scaling-in of the cluster is slow, the resources are wasted without being utilized, and it leads less resource utilization. This research follows the real-world scenario where the computing resources are bounded by a certain number of computing nodes due to limited budget as well as the computing time is limited due to the nature of near real-time application. We design an auto-scaling system that applies a deep reinforcement learning technique, DQN (Deep Q-Network), to improve resource utilization efficiently. Our model-based DQN allows to automatically optimize the scaling of the cluster, because the DQN can autonomously learn the given environment features so that it can take suitable actions to get the maximum reward under the limited execution time and worker nodes.
Matrix Multiplication with Ateji PX for JavaPatrick Viry
Matrix multiplication is a standard benchmark for evaluating the performance of intensive dataparallel operations on recent multi-core processors. This whitepaper shows to use Ateji PX for Java to achieve state-of-the-art parallel performance, by adding one single operator in your existing code.
Design of airfoil using backpropagation training with mixed approachEditor Jacotech
Levenberg-Marquardt back-propagation training method has some limitations associated with over fitting and local optimum problems. Here, we proposed a new algorithm to increase the convergence speed of Backpropagation learning to design the airfoil. The aerodynamic force coefficients corresponding to series of airfoil are stored in a database along with the airfoil coordinates. A feedforward neural network is created with aerodynamic coefficient as input to produce the airfoil coordinates as output. In the proposed algorithm, for output layer, we used the cost function having linear & nonlinear error terms then for the hidden layer, we used steepest descent cost function. Results indicate that this mixed approach greatly enhances the training of artificial neural network and may accurately predict airfoil profile.
Similar to What is pattern recognition (lecture 6 of 6) (20)
الجزء السادس ماذا ستقدم لعميلك ريادة الأعمال خطوة بخطوةRanda Elanwar
فى هذه السلسلة (السلسلة الثانية) نستكمل تقديم أساسيات علم ريادة الأعمال التجارية Business Entrepreneurship التى تحتاج أن تتعلمها قبل أن تقوم ببناء شركتك أو مؤسستك الهادفة للربح؛ حتى تتعرف على الخطوات الأولية للعمل وكيفية تنفيذها، وتكتشف المفاهيم الخاطئة السائدة، ثم تقوم فى النهاية ببناء تجارتك على أساس صحيح من وجهة نظر العميل، وليس من وجهة نظرك كصاحب العمل. هذه السلسلة هى ملخص للدروس المستفادة من دورة ريادة الأعمال المفتوحة التى يقدمها معهد ماساتشوستس للتقنية MIT على منصة Edx بعنوان MITx: 15.390.2x Entrepreneurship 102: What can you do for your customer?
رابط الدورة: https://www.edx.org/course/entrepreneurship-102-what-can-you-do-mitx-15-390-2x
الجزء الرابع ماذا ستقدم لعميلك ريادة الأعمال خطوة بخطوةRanda Elanwar
فى هذه السلسلة (السلسلة الثانية) نستكمل تقديم أساسيات علم ريادة الأعمال التجارية Business Entrepreneurship التى تحتاج أن تتعلمها قبل أن تقوم ببناء شركتك أو مؤسستك الهادفة للربح؛ حتى تتعرف على الخطوات الأولية للعمل وكيفية تنفيذها، وتكتشف المفاهيم الخاطئة السائدة، ثم تقوم فى النهاية ببناء تجارتك على أساس صحيح من وجهة نظر العميل، وليس من وجهة نظرك كصاحب العمل. هذه السلسلة هى ملخص للدروس المستفادة من دورة ريادة الأعمال المفتوحة التى يقدمها معهد ماساتشوستس للتقنية MIT على منصة Edx بعنوان MITx: 15.390.2x Entrepreneurship 102: What can you do for your customer?
رابط الدورة: https://www.edx.org/course/entrepreneurship-102-what-can-you-do-mitx-15-390-2x
الجزء الثاني ماذا ستقدم لعميلك ريادة الأعمال خطوة بخطوةRanda Elanwar
فى هذه السلسلة (السلسلة الثانية) نستكمل تقديم أساسيات علم ريادة الأعمال التجارية Business Entrepreneurship التى تحتاج أن تتعلمها قبل أن تقوم ببناء شركتك أو مؤسستك الهادفة للربح؛ حتى تتعرف على الخطوات الأولية للعمل وكيفية تنفيذها، وتكتشف المفاهيم الخاطئة السائدة، ثم تقوم فى النهاية ببناء تجارتك على أساس صحيح من وجهة نظر العميل، وليس من وجهة نظرك كصاحب العمل. هذه السلسلة هى ملخص للدروس المستفادة من دورة ريادة الأعمال المفتوحة التى يقدمها معهد ماساتشوستس للتقنية MIT على منصة Edx بعنوان MITx: 15.390.2x Entrepreneurship 102: What can you do for your customer?
رابط الدورة: https://www.edx.org/course/entrepreneurship-102-what-can-you-do-mitx-15-390-2x
تدريب مدونة علماء مصر على الكتابة الفنية (الترجمة والتلخيص )_Pdf5of5Randa Elanwar
مرحبا بكم فى التدريب الأساسى لمدونى علماء مصر
التدريب الأساسى هو فقط مقدمة شاملة لتوسيع المدارك، وتصحيح المفاهيم الخاطئة، ولا يهدف إلى تدريب متخصص فى أى المحاور التى يتناولها
أولا: المقدمة وفيها تعريف بأبواب المدونة وأمثلة للمواضيع الفرعية التى يمكنك الكتابة فيها ومحاور التدريب
ثانيا: المحور الأول وفيه تدريب على هدف وهيكل المقالات المبنية على البحث واختيار الكلمات المفتاحية مع أمثلة
ثالثا: المحور الثانى وفيه تدريب على هدف وهيكل المقالات الإخبارية مع أمثلة
رابعا: المحور الثالث وفيه تدريب على هدف وهيكل مقالات الموارد مع أمثلة
خامسا: المحور الرابع وفيه تدريب على فنيات الكتابة للمقالات والقصة القصيرة والخاطرة وتلخيص للأخطاء اللغوية والإملائية الشائعة وعلامات الترقيم
سادسا المحور الخامس وفيه تدريب على كيفية الترجمة والتلخيص وأهم النصائح والأدوات
تدريب مدونة علماء مصر على الكتابة الفنية (القصة القصيرة والخاطرة والأخطاء ال...Randa Elanwar
مرحبا بكم فى التدريب الأساسى لمدونى علماء مصر
التدريب الأساسى هو فقط مقدمة شاملة لتوسيع المدارك، وتصحيح المفاهيم الخاطئة، ولا يهدف إلى تدريب متخصص فى أى المحاور التى يتناولها
أولا: المقدمة وفيها تعريف بأبواب المدونة وأمثلة للمواضيع الفرعية التى يمكنك الكتابة فيها ومحاور التدريب
ثانيا: المحور الأول وفيه تدريب على هدف وهيكل المقالات المبنية على البحث واختيار الكلمات المفتاحية مع أمثلة
ثالثا: المحور الثانى وفيه تدريب على هدف وهيكل المقالات الإخبارية مع أمثلة
رابعا: المحور الثالث وفيه تدريب على هدف وهيكل مقالات الموارد مع أمثلة
خامسا: المحور الرابع وفيه تدريب على فنيات الكتابة للمقالات والقصة القصيرة والخاطرة وتلخيص للأخطاء اللغوية والإملائية الشائعة وعلامات الترقيم
سادسا المحور الخامس وفيه تدريب على كيفية الترجمة والتلخيص وأهم النصائح والأدوات
تدريب مدونة علماء مصر على الكتابة الفنية (مقالات الموارد )_Pdf3of5Randa Elanwar
مرحبا بكم فى التدريب الأساسى لمدونى علماء مصر
التدريب الأساسى هو فقط مقدمة شاملة لتوسيع المدارك، وتصحيح المفاهيم الخاطئة، ولا يهدف إلى تدريب متخصص فى أى المحاور التى يتناولها
أولا: المقدمة وفيها تعريف بأبواب المدونة وأمثلة للمواضيع الفرعية التى يمكنك الكتابة فيها ومحاور التدريب
ثانيا: المحور الأول وفيه تدريب على هدف وهيكل المقالات المبنية على البحث واختيار الكلمات المفتاحية مع أمثلة
ثالثا: المحور الثانى وفيه تدريب على هدف وهيكل المقالات الإخبارية مع أمثلة
رابعا: المحور الثالث وفيه تدريب على هدف وهيكل مقالات الموارد مع أمثلة
خامسا: المحور الرابع وفيه تدريب على فنيات الكتابة للمقالات والقصة القصيرة والخاطرة وتلخيص للأخطاء اللغوية والإملائية الشائعة وعلامات الترقيم
سادسا المحور الخامس وفيه تدريب على كيفية الترجمة والتلخيص وأهم النصائح والأدوات
تدريب مدونة علماء مصر على الكتابة الفنية (المقالات الإخبارية )_Pdf2of5Randa Elanwar
مرحبا بكم فى التدريب الأساسى لمدونى علماء مصر
التدريب الأساسى هو فقط مقدمة شاملة لتوسيع المدارك، وتصحيح المفاهيم الخاطئة، ولا يهدف إلى تدريب متخصص فى أى المحاور التى يتناولها
أولا: المقدمة وفيها تعريف بأبواب المدونة وأمثلة للمواضيع الفرعية التى يمكنك الكتابة فيها ومحاور التدريب
ثانيا: المحور الأول وفيه تدريب على هدف وهيكل المقالات المبنية على البحث واختيار الكلمات المفتاحية مع أمثلة
ثالثا: المحور الثانى وفيه تدريب على هدف وهيكل المقالات الإخبارية مع أمثلة
رابعا: المحور الثالث وفيه تدريب على هدف وهيكل مقالات الموارد مع أمثلة
خامسا: المحور الرابع وفيه تدريب على فنيات الكتابة للمقالات والقصة القصيرة والخاطرة وتلخيص للأخطاء اللغوية والإملائية الشائعة وعلامات الترقيم
سادسا المحور الخامس وفيه تدريب على كيفية الترجمة والتلخيص وأهم النصائح والأدوات
تدريب مدونة علماء مصر على الكتابة الفنية (المقالات المبنية على البحث )_Pdf1of5Randa Elanwar
مرحبا بكم فى التدريب الأساسى لمدونى علماء مصر
التدريب الأساسى هو فقط مقدمة شاملة لتوسيع المدارك، وتصحيح المفاهيم الخاطئة، ولا يهدف إلى تدريب متخصص فى أى المحاور التى يتناولها
أولا: المقدمة وفيها تعريف بأبواب المدونة وأمثلة للمواضيع الفرعية التى يمكنك الكتابة فيها ومحاور التدريب
ثانيا: المحور الأول وفيه تدريب على هدف وهيكل المقالات المبنية على البحث واختيار الكلمات المفتاحية مع أمثلة
ثالثا: المحور الثانى وفيه تدريب على هدف وهيكل المقالات الإخبارية مع أمثلة
رابعا: المحور الثالث وفيه تدريب على هدف وهيكل مقالات الموارد مع أمثلة
خامسا: المحور الرابع وفيه تدريب على فنيات الكتابة للمقالات والقصة القصيرة والخاطرة وتلخيص للأخطاء اللغوية والإملائية الشائعة وعلامات الترقيم
سادسا المحور الخامس وفيه تدريب على كيفية الترجمة والتلخيص وأهم النصائح والأدوات
فى هذه السلسلة نقدم لك أساسيات علم ريادة الأعمال التجارية
Business Entrepreneurship
التى تحتاج أن تتعلمها قبل أن تقوم ببناء شركتك أو مؤسستك الهادفة للربح؛ حتى تتعرف على الخطوات الأولية للعمل وكيفية تنفيذها، وتكتشف المفاهيم الخاطئة السائدة، ثم تقوم فى النهاية ببناء تجارتك على أساس صحيح من وجهة نظر العميل، وليس من وجهة نظرك كصاحب العمل. هذه السلسلة هى ملخص للدروس المستفادة من دورة ريادة الأعمال المفتوحة التى يقدمها معهد ماساتشوستس للتقنية
MIT
على منصة
Edx
بعنوان
MITx: 15.390.1x Entrepreneurship 101: Who is your customer?
رابط الدورة:
https://www.edx.org/course/entrepreneurship-101-who-customer-mitx-15-390-1x#.VL-MN0eUfHA
فى هذه السلسلة نقدم لك أساسيات علم ريادة الأعمال التجارية
Business Entrepreneurship
التى تحتاج أن تتعلمها قبل أن تقوم ببناء شركتك أو مؤسستك الهادفة للربح؛ حتى تتعرف على الخطوات الأولية للعمل وكيفية تنفيذها، وتكتشف المفاهيم الخاطئة السائدة، ثم تقوم فى النهاية ببناء تجارتك على أساس صحيح من وجهة نظر العميل، وليس من وجهة نظرك كصاحب العمل. هذه السلسلة هى ملخص للدروس المستفادة من دورة ريادة الأعمال المفتوحة التى يقدمها معهد ماساتشوستس للتقنية
MIT
على منصة
Edx
بعنوان
MITx: 15.390.1x Entrepreneurship 101: Who is your customer?
رابط الدورة:
https://www.edx.org/course/entrepreneurship-101-who-customer-mitx-15-390-1x#.VL-MN0eUfHA
فى هذه السلسلة نقدم لك أساسيات علم ريادة الأعمال التجارية
Business Entrepreneurship
التى تحتاج أن تتعلمها قبل أن تقوم ببناء شركتك أو مؤسستك الهادفة للربح؛ حتى تتعرف على الخطوات الأولية للعمل وكيفية تنفيذها، وتكتشف المفاهيم الخاطئة السائدة، ثم تقوم فى النهاية ببناء تجارتك على أساس صحيح من وجهة نظر العميل، وليس من وجهة نظرك كصاحب العمل. هذه السلسلة هى ملخص للدروس المستفادة من دورة ريادة الأعمال المفتوحة التى يقدمها معهد ماساتشوستس للتقنية
MIT
على منصة
Edx
بعنوان
MITx: 15.390.1x Entrepreneurship 101: Who is your customer?
رابط الدورة:
https://www.edx.org/course/entrepreneurship-101-who-customer-mitx-15-390-1x#.VL-MN0eUfHA
فى هذه السلسلة نقدم لك أساسيات علم ريادة الأعمال التجارية
Business Entrepreneurship
التى تحتاج أن تتعلمها قبل أن تقوم ببناء شركتك أو مؤسستك الهادفة للربح؛ حتى تتعرف على الخطوات الأولية للعمل وكيفية تنفيذها، وتكتشف المفاهيم الخاطئة السائدة، ثم تقوم فى النهاية ببناء تجارتك على أساس صحيح من وجهة نظر العميل، وليس من وجهة نظرك كصاحب العمل. هذه السلسلة هى ملخص للدروس المستفادة من دورة ريادة الأعمال المفتوحة التى يقدمها معهد ماساتشوستس للتقنية
MIT
على منصة
Edx
بعنوان
MITx: 15.390.1x Entrepreneurship 101: Who is your customer?
رابط الدورة:
https://www.edx.org/course/entrepreneurship-101-who-customer-mitx-15-390-1x#.VL-MN0eUfHA
هي قصة مشوار بدأ ولم ينتهِ بعد. سيكون فيها كل يوم شيءٌ جديد. سأتعلم وسأحكي لكم ما تعلمته. ربما وفّرت عليك التجربة لتُغيّرَ كثيرًا من قناعات لديك.
إن كنت طالبًا، أو حديث التخرج، وتنوي عمل دراسات عليا بمصر، فدعني أعرّفك قليلًا على أشياء خارج توقعاتك، إن لم يكن لديك فكرة. وإن كنت قد اتخذت خطواتك الأولى بالفعل فربما تجد في قصّتي ما يفسر ألغازك، ويهوّن عليك المفاجآت. لن أقول لك الآن ما مجال دراستي، فرغم احتمال أن تكون دارسًا لتخصصٍ آخر يختلف عني، ولكنني أثق أن لديك نفس الأسئلة، ونفس الشكوى
هي قصة مشوار بدأ ولم ينتهِ بعد. سيكون فيها كل يوم شيءٌ جديد. سأتعلم وسأحكي لكم ما تعلمته. ربما وفّرت عليك التجربة لتُغيّرَ كثيرًا من قناعات لديك.
إن كنت طالبًا، أو حديث التخرج، وتنوي عمل دراسات عليا بمصر، فدعني أعرّفك قليلًا على أشياء خارج توقعاتك، إن لم يكن لديك فكرة. وإن كنت قد اتخذت خطواتك الأولى بالفعل فربما تجد في قصّتي ما يفسر ألغازك، ويهوّن عليك المفاجآت. لن أقول لك الآن ما مجال دراستي، فرغم احتمال أن تكون دارسًا لتخصصٍ آخر يختلف عني، ولكنني أثق أن لديك نفس الأسئلة، ونفس الشكوى.
هي قصة مشوار بدأ ولم ينتهِ بعد. سيكون فيها كل يوم شيءٌ جديد. سأتعلم وسأحكي لكم ما تعلمته. ربما وفّرت عليك التجربة لتُغيّرَ كثيرًا من قناعات لديك.
إن كنت طالبًا، أو حديث التخرج، وتنوي عمل دراسات عليا بمصر، فدعني أعرّفك قليلًا على أشياء خارج توقعاتك، إن لم يكن لديك فكرة. وإن كنت قد اتخذت خطواتك الأولى بالفعل فربما تجد في قصّتي ما يفسر ألغازك، ويهوّن عليك المفاجآت. لن أقول لك الآن ما مجال دراستي، فرغم احتمال أن تكون دارسًا لتخصصٍ آخر يختلف عني، ولكنني أثق أن لديك نفس الأسئلة، ونفس الشكوى
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Nucleophilic Addition of carbonyl compounds.pptxSSR02
Nucleophilic addition is the most important reaction of carbonyls. Not just aldehydes and ketones, but also carboxylic acid derivatives in general.
Carbonyls undergo addition reactions with a large range of nucleophiles.
Comparing the relative basicity of the nucleophile and the product is extremely helpful in determining how reversible the addition reaction is. Reactions with Grignards and hydrides are irreversible. Reactions with weak bases like halides and carboxylates generally don’t happen.
Electronic effects (inductive effects, electron donation) have a large impact on reactivity.
Large groups adjacent to the carbonyl will slow the rate of reaction.
Neutral nucleophiles can also add to carbonyls, although their additions are generally slower and more reversible. Acid catalysis is sometimes employed to increase the rate of addition.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
2. Contents
Network implementation on MATLAB
Network type creation
Input definition
Parameters tuning
2
ERI Summer training (C&S) Dr. Randa Elanwar
Parameters tuning
Activation functions
Network training
Network testing (simulation)
3. Network implementation on MATLAB
3
To simulate a NN on MATLAB "neural networks
toolbox“ you have to first make three main choices (1)
the network type and topology, (2) the activation
function type, and the (3) training mode.
The main steps to simulate a NN on matlab are:
1. Creating the network (of your defined type, structure and
parameters)
2. Training the network by the training pairs you have
(<input, output>)
3. Testing the network
ERI Summer training (C&S) Dr. Randa Elanwar
4. Important Note
4
Each of the three steps is done via a set of Matlab
instructions that update (name, arguments) from
version to version.
So I will focus on the instruction target rather than
the instruction syntax.
ERI Summer training (C&S) Dr. Randa Elanwar
5. Network type creation
5
Step 1: Network creation
In this step you have to decide:
1. the network type (feed forward, radial basis, etc.),
2. the activation function,2. the activation function,
3. the internal structure (the number of nodes in the input
layer and the number of nodes into the output layer),
4. the input stream type (serial, concurrent), and
5. parameters values (weights, bias, delay).
ERI Summer training (C&S) Dr. Randa Elanwar
6. Network type creation
6
And before creation you have to settle upon the data sets
(input vectors and their corresponding output values) for
training and the test data sets (input vectors only).
Neural network types creationNeural network types creation
Matlab offers creation of a variety of neural networks
types: Perceptrons, Feed-forward neural network, Recurrent neural
network, Probabilistic neural network, Radial basis neural
networks, Self-organizing, Time-delay neural network, etc.
Each has a creation function with a set of input arguments to
define its structure: inputs, number of nodes, number of
layers, etc.
ERI Summer training (C&S) Dr. Randa Elanwar
7. Network type creation
7
All what you need is to call the function and specify
values for these main arguments in the required data
structure format (scalar, vectors, matrices, etc.)
Neural network creation functionsNeural network creation functions
The names might change with newer Matlab versions so this
screen shot is just to illustrate the capabilities of Matlab to
simulate the different neural networks types. The creation
functions can be found in the "Neural networks toolbox",
where you can click on the required function name
(hyperlink) to navigate to the full description with examples.
ERI Summer training (C&S) Dr. Randa Elanwar
9. Input definition
9
Not all networks take input data vectors of length n-by-
1, some take data as single input with or without delay
between input instances. For example:
Concurrent:Concurrent:
net = newlin([1 3;1 3],1);
This command creates a new custom network with linear
transfer function and specifies the range of neuron input p1
as [1 3] and input p2 as [1 3] also, i.e., two inputs and a
single output ‘1’.
ERI Summer training (C&S) Dr. Randa Elanwar
10. Input definition
10
Suppose that the network simulation
data set consists four concurrent vectors
Concurrent vectors are presented to theConcurrent vectors are presented to the
network as a single matrix:
P = [1 2 2 3; 2 1 3 1];
We can now simulate the network:
A = sim(net,P)
A = 5 4 8 5
ERI Summer training (C&S) Dr. Randa Elanwar
11. Input definition
11
Sequential:
When a network contains delays, the input to the
network would normally be a sequence of input vectors
that occur in a certain time order.
The following commands create this network:
net = newlin([-1 1],1,[0 1]);
This command limits the input value from -1 to 1 with
1 output and a delay that is limited from 0 to 1.
ERI Summer training (C&S) Dr. Randa Elanwar
12. Input definition
12
Suppose that the input sequence is p1 = [1], p2 = [2], p3 = [3] and p4 =
[4]. Sequential inputs are presented to the network as elements of a cell
array:
P = {1 2 3 4};
We can now simulate the network:
A = sim(net,P)
A = [1] [4] [7] [10]
ERI Summer training (C&S) Dr. Randa Elanwar
13. Input definition
13
Important note: usually you have to specify the upper and
lower limit expected for the values of input elements.
Some training algorithms generally works best when the
network inputs and targets are scaled so that they fall
approximately in the range [-1,1].approximately in the range [-1,1].
But! If your inputs and targets do not fall in this range, you
can use the functions "premnmx", or "mapstd", to perform
the scaling and processes input and target data by mapping
its mean and standard deviations to 0 and 1 respectively.
Then use "poststd" to post-process data which has been pre-
processed by "mapstd". It converts the data back into
unnormalized units.
ERI Summer training (C&S) Dr. Randa Elanwar
14. Parameters tuning
14
Usually the network is created with default values for its parameters,
but you can change this either by resetting then assigning new values
or direct assignment of new values.
Example:
Adjusting weights and bias for a concurrent input network
net = newlin([1 3;1 3],1);net = newlin([1 3;1 3],1);
net.IW{1,1} = [1 2];
net.b{1} = 0;
Adjusting weights and bias for a sequential input network
net = newlin([-1 1],1,[0 1]);
net.biasConnect = 0;
net.IW{1,1} = [1 2];
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15. Parameters tuning
15
In this example we only have the weights of the input
layer but if the network has multiple layers, then a certain
notation should be used to the assign the weights of the
other layers:
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16. Activation functions
16
There is a variety of activation functions that you
can chose from for your network:
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17. Activation functions
17
Activation function name argument in Matlab
These activation functions names are set as an input argument to
the network creation function
Example:
net = newelm([0 1],[3 2],{'tansig','purelin'});
All you need is to read more about the recommended
uses of each network type to design the classifier
topology and internal structure you want to implement.
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19. Network training
19
Matlab offers you a variety of learning rules and
methods to train the network you designed.
For example you can use "train" function and set theFor example you can use "train" function and set the
parameters of (training mode, learning rate,
number of epochs and the error limit), or use
standard training function like "trainb" for example
for batch training mode.
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21. Network training
21
Here newff is used to create a two-layer feed-forward
network. The network has one hidden layer with ten neurons.
net = feedforwardnet(10);
net = configure(net,p,t);
y1 = sim(net,p)y1 = sim(net,p)
The network is trained for up to 300 epochs to an error
goal of 0.1 and then re-simulated.
net.trainParam.epochs = 300;
net.trainParam.goal = 0.1;
net.trainFcn = 'trainb';
net = train(net,p,t);
y2 = sim(net,p)
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22. Network training
22
There is a nice option to know when had the training
converged, if you set the parameter "show" before you call
the training function
net.trainParam.show = 25;
In such case the error value will appear on your work spaceIn such case the error value will appear on your work space
every "25" iterations like this:
TRAINB, Epoch 0/100, MSE 0.5/0.1.
TRAINB, Epoch 25/100, MSE 0.181122/0.1.
TRAINB, Epoch 50/100, MSE 0.111233/0.1.
TRAINB, Epoch 64/100, MSE 0.0999066/0.1.
TRAINB, Performance goal met.
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23. Network training
23
It's important also to note that some training
functions are designed to have different (adaptive)
values for the learning rate to help faster
convergence like "traingda"
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24. Network testing (simulation)
24
After training the network you can test the
performance on a test set, simply you can call the
"sim" function giving your trained network and
the test samples as input arguments.
This is the fun part, Enjoy it
ERI Summer training (C&S) Dr. Randa Elanwar