Deep learning is now making the Artificial Intelligence near to Human. Machine Learning and Deep Artificial Neural Network make the copy of Human Brain. The success is due to large storage, computation with efficient algorithms to handle more behavioral and cognitive problem
Deep learning is now making the Artificial Intelligence near to Human. Machine Learning and Deep Artificial Neural Network make the copy of Human Brain. The success is due to large storage, computation with efficient algorithms to handle more behavioral and cognitive problem
This presentation guide you through Neural Networks, use neural networksNeural Networks v/s Conventional
Computer, Inspiration from Neurobiology, Types of neural network, The Learning Process, Hetero-association recall mechanisms and Key Features,
For more topics stay tuned with Learnbay.
Basics of Neural networks and its image recognition and its applications of engineering fields and medicines and how it detect those images and give the results of those images....
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The main objective of this paper is to recognize and predict handwritten digits from 0 to 9 where data set of 5000 examples of MNIST was given as input. As we know as every person has different style of writing digits humans can recognize easily but for computers it is comparatively a difficult task so here we have used neural network approach where in the machine will learn on itself by gaining experiences and the accuracy will increase based upon the experience it gains. The dataset was trained using feed forward neural network algorithm. The overall system accuracy obtained was 95.7% Jyoti Shinde | Chaitali Rajput | Prof. Mrunal Shidore | Prof. Milind Rane"Handwritten Digit Recognition" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd8384.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/8384/handwritten-digit-recognition/jyoti-shinde
An Neural Network (NN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.
It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems.
An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process.
An artificial neuron is a device with many inputs and one output. The neuron has two modes of operation; the training mode and the using mode. In the training mode, the neuron can be trained to fire (or not), for particular input patterns.
In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output.
We will use 7 emotions namely - We have used 7 emotions namely - 'Angry', 'Disgust'濫, 'Fear', 'Happy', 'Neutral', 'Sad'☹️, 'Surprise' to train and test our algorithm using Convolution Neural Networks.
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
This presentation guide you through Neural Networks, use neural networksNeural Networks v/s Conventional
Computer, Inspiration from Neurobiology, Types of neural network, The Learning Process, Hetero-association recall mechanisms and Key Features,
For more topics stay tuned with Learnbay.
Basics of Neural networks and its image recognition and its applications of engineering fields and medicines and how it detect those images and give the results of those images....
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
The main objective of this paper is to recognize and predict handwritten digits from 0 to 9 where data set of 5000 examples of MNIST was given as input. As we know as every person has different style of writing digits humans can recognize easily but for computers it is comparatively a difficult task so here we have used neural network approach where in the machine will learn on itself by gaining experiences and the accuracy will increase based upon the experience it gains. The dataset was trained using feed forward neural network algorithm. The overall system accuracy obtained was 95.7% Jyoti Shinde | Chaitali Rajput | Prof. Mrunal Shidore | Prof. Milind Rane"Handwritten Digit Recognition" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd8384.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/8384/handwritten-digit-recognition/jyoti-shinde
An Neural Network (NN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.
It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems.
An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process.
An artificial neuron is a device with many inputs and one output. The neuron has two modes of operation; the training mode and the using mode. In the training mode, the neuron can be trained to fire (or not), for particular input patterns.
In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output.
We will use 7 emotions namely - We have used 7 emotions namely - 'Angry', 'Disgust'濫, 'Fear', 'Happy', 'Neutral', 'Sad'☹️, 'Surprise' to train and test our algorithm using Convolution Neural Networks.
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
by Kan Min-Yen, Deputy Director (Research) of
NUS Institute for the Application of Learning Sciences and Education Technology
5th IBC EduCon, Singapore, 28 Sep 2017
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).
Deep Learning: Evolution of ML from Statistical to Brain-like Computing- Data...Impetus Technologies
Presentation on 'Deep Learning: Evolution of ML from Statistical to Brain-like Computing'
Speaker- Dr. Vijay Srinivas Agneeswaran,Director, Big Data Labs, Impetus
The main objective of the presentation is to give an overview of our cutting edge work on realizing distributed deep learning networks over GraphLab. The objectives can be summarized as below:
- First-hand experience and insights into implementation of distributed deep learning networks.
- Thorough view of GraphLab (including descriptions of code) and the extensions required to implement these networks.
- Details of how the extensions were realized/implemented in GraphLab source – they have been submitted to the community for evaluation.
- Arrhythmia detection use case as an application of the large scale distributed deep learning network.
Advancements in HPCC Systems Machine LearningHPCC Systems
This presentation will provide an overview of the latest advancements in Machine Learning modules over the past year, including Clustering, Natural Language Processing, Deep Learning, and the Expanded Model Evaluation Metrics.
Clustering Methods of the HPCC Systems Machine Learning Library
The clustering method is an important part of unsupervised learning. To gain the unsupervised learning capability, two widely applied clustering methods, KMeans and DBSCAN are adopted to the current Machine Learning library. This presentation will introduce the newly developed clustering algorithms and the evaluation methods.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Lecture conducted by me on Deep Learning concepts and applications. Discussed FNNs, CNNs, Simple RNNs and LSTM Networks in detail. Finally conducted a hands-on session on deep-learning using Keras and scikit-learn.
Similar to Animesh Prasad and Muthu Kumar Chandrasekaran - WESST - Basics of Deep Learning (20)
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.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
6. Machine Learning, AI
• Four key ingredients for ML towards AI
1. Lots & lots of data
2. Very flexible models
3.
Enough computing power
4. Powerful priors that can defeat the curse of
dimensionality
6
7. Bypassing the curse of dimensionality
We need to build compositionality into our ML models
Just as human languages exploit compositionality to give representations and
meanings to complex ideas
Exploiting compositionality gives an exponential gain in representational power
(1) Distributed representations / embeddings: feature learning
(2) Deep architecture: multiple levels of feature learning
Additional prior: compositionality is useful to describe the
world around us efficiently
7
8. Each feature can be discovered without the need for
seeing the exponentially large number of
configurations of the other features
• Consider a network whose hidden units discover the following features:
• Person wears glasses
• Person is female
• Person is a child
• Etc.
If each of n feature requires O(k) parameters, need O(nk) examples Non-
parametric methods would require O(nd
) examples
8
10. Hidden Units Discover Semantically Meaningful
Concepts
• Network trained to recognize places, not objects
10
11. Why does it work?
•
It only works because we are making some assumptions about
the data generating distribution
•
Worse-case distributions still require exponential data
•
But the world has structure and we can get an exponential gain by
exploiting some of it
11
30. 30
Gated Recurrent Units & LSTM
Create a path where gradients can
flow for longer with self-loop
• Corresponds to an eigenvalue of
Jacobian slightly less than 1
• LSTM is heavily used
• GRU light-weight version
38. 38
Piecewise Linear Nonlinearity
Absolute value rectification works better than tanh
in lower layers of convnet.
Using a rectifier non- linearity (ReLU) instead of
tanh of softplus allows for the first time to train
very deep supervised networks without the need
for unsupervised pre-training; was biologically
motivated
Rectifiers one of the crucial ingredients in
ImageNet breakthrough
39. 39
Dropout Regularizer: Super-
Efficient Bagging
• Dropouts trick: during training
multiply neuron output by random bit
(p=0.5), during test by 0.5
• Used in deep supervised networks
• Similar to denoising auto-encoder,
but corrupting every layer
• Works better with some non-
linearities (rectifiers, maxout)
41. 41
Early Stopping
• Beautiful FREE LUNCH (no need to launch many different
training runs for each value of hyper-parameter for #iterations)
• Monitor validation error during training (after visiting # of
training examples = a multiple of validation set size)
• Keep track of parameters with best validation error and report
them at the end
• If error does not improve enough (with some patience), stop.
42. 42
Sampling Hyperparameters
• Common approach: manual +
grid search
• Grid search over
hyperparameters: simple &
wasteful
• Random search: simple &
efficient
• Independently sample each HP,
• More convenient: ok to early-
stop, continue further, etc.
46. 46
Analogical Representations for
Free Text
• Semantic relations appear
as linear relationships in the
space of learned
representations
• King – Queen ≈ Man –
Woman
• Paris – France + Italy ≈
Rome
47. 47
• Intermediate representation of meaning = ‘universal representation’
• Encoder: from word sequence to sentence representation
• Decoder: from representation to word sequence distribution
Encoder-Decoder Framework
48. 48
Attention Mechanism for Deep
Learning
• Consider an input (or intermediate) sequence or image
• Consider an upper level representation, which can choose « where to
look », by assigning a weight or probability to each input position, as
produced by an MLP, applied at each position
58. 58
Insights
• The big payoff of deep learning is to allow learning
higher levels of abstraction
• Higher-level abstractions disentangle the factors of
variation, which allows much easier generalization and
transfer
60. 60
Hands On
• Login to NUS SoC VPN with your NUSNET Account
• ssh wesst@kpe.d1.comp.nus.edu.sg
Password: kerasuser
• copy folder demo to a new folder rename demo_<yourname>