Handwritten bangla-digit-recognition-using-deep-learning
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The document discusses using deep learning approaches for handwritten Bangla digit recognition. It proposes using convolutional neural networks (CNNs) and deep belief networks (DBNs) with dropout and different filters. It finds that a CNN using Gabor features and dropout achieved the best accuracy compared to other techniques. The research is continuing to develop more advanced neural networks combining state preserving extreme learning machines to recognize Bangla numerals and characters.
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1. What is Deep Learning?
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The solution was designed to be implemented in two stages: Machine Learning and Application, according to the diagram below from the Data Science Academy.
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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
Three classes of deep learning networks
Deep learning refers to machine learning techniques that use multiple layers of non-linear information processing to learn representations of data. It has emerged as an area of machine learning research that is impacting signal and information processing. Deep learning can be used for unsupervised learning with models like deep belief networks, supervised learning with deep neural networks, or hybrid approaches that combine unsupervised and supervised techniques.
Speech Processing with deep learning
This document provides an introduction to speech recognition with deep learning. It discusses how speech recognition works, the development of the field from early methods like HMMs to modern deep learning approaches using neural networks. It defines deep learning and explains why it is called "deep" learning. It also outlines common deep learning architectures for speech recognition, including CNN-RNN models and sequence-to-sequence models. Finally, it describes the layers of a CNN like convolutional, pooling, ReLU and fully-connected layers.
Let_s_Dive_to_Deep_Learning.pptx
This document provides an overview of deep learning. It defines deep learning as a subset of machine learning that uses neural network architectures, especially deep neural networks containing many hidden layers. Deep learning models are trained on large labeled datasets to automatically extract features without manual feature engineering. Convolutional neural networks are commonly used for tasks like image classification by extracting hierarchical features from images. The document outlines the basic architecture of CNNs including convolutional layers that extract features and pooling layers that reduce size, as well as fully connected layers. It also briefly describes the learning process of forward and backward propagation.
Top 10 deep learning algorithms you should know in
In scientific computing, deep learning has gained immense popularity, and its algorithms are commonly used by companies that solve complex problems.
MaLAI_Hyderabad presentation
This document provides an overview of transfer learning in machine learning. It begins by explaining different types of machine learning including supervised, unsupervised, reinforcement, and semi-supervised learning. It then describes artificial neural networks and some specific types including perceptrons, feedforward neural networks, radial basis function networks, recurrent neural networks, long short-term memory networks, and convolutional neural networks. The document defines transfer learning as transferring knowledge gained from training a model on one problem to improve training on another related problem. It lists applications of transfer learning and provides references for further reading.
Deep learning
This document provides an overview of deep learning presented by Khaled Amirat at the University of Souk Ahras in Algeria in 2017. It defines artificial intelligence and machine learning, and explains that deep learning is a type of machine learning that uses neural networks with multiple layers to automatically learn representations of raw data. The document contrasts deep learning with traditional machine learning approaches that require manual feature engineering, and outlines different deep learning architectures like convolutional neural networks and recurrent neural networks. Examples are given of applications in areas like computer vision, natural language processing, and topic modeling from documents. The training process of neural networks using forward propagation and backpropagation is also summarized.
Convolutional Neural Networks square measure terribly kind of like n.pdf
Convolutional Neural Networks square measure terribly kind of like normal Neural Networks
from the previous chapter: they\'re created of neurons that have learnable weights and biases.
every vegetative cell receives some inputs, performs a inner product and optionally follows it
with a non-linearity. the total network still expresses one differentiable score function: from the
raw image pixels on one finish to category scores at the opposite. and that they still have a loss
operate (e.g. SVM/Softmax) on the last (fully-connected) layer and every one the tips/tricks we
have a tendency to developed for learning regular Neural Networks still apply.
So what will change? ConvNet architectures create the express assumption that the inputs square
measure pictures, that permits United States to cypher bound properties into the design. These
then create the forward operate additional economical to implement and immensely cut back the
quantity of parameters within the network.
In machine learning, a deep belief network (DBN) may be a generative graphical model, or or
else a sort of deep neural network, composed of multiple layers of latent variables (\"hidden
units\"), with connections between the layers however not between units at intervals every
layer.[1]
When trained on a group of examples in associate degree unsupervised manner, a DBN will learn
to probabilistically reconstruct its inputs. The layers then act as feature detectors on inputs. when
this learning step, a DBN are often more trained in a very supervised thanks to perform
classification.
DBNs are often viewed as a composition of straightforward, unsupervised networks like
restricted Ludwig Boltzmann machines (RBMs)[1] or autoencoders,[3] wherever every sub-
network\'s hidden layer is the visible layer for future. This conjointly results in a quick, layer-by-
layer unsupervised coaching procedure, wherever contrastive divergence is applied to every sub-
network successively, ranging from the \"lowest\" try of layers (the lowest visible layer being a
coaching set).
The observation, attributable to Yee-Whye Teh, Geoffrey Hinton\'s student, that DBNs are often
trained avariciously, one layer at a time, LED to at least one of the primary effective deep
learning algorithms.
A restricted physicist machine (RBM) may be a generative random artificial neural network that
may learn a likelihood distribution over its set of inputs.
RBMs were at the start fictitious beneath the name reed organ by Paul Smolensky in 1986, and
rose to prominence when Geoffrey Hinton and collaborators fictitious quick learning algorithms
for them within the mid-2000s. RBMs have found applications in spatiality reduction,
classification, cooperative filtering, feature learning and topic modelling. they will be trained in
either supervised or unsupervised ways that, counting on the task.
As their name implies, RBMs ar a variant of physicist machines, with the restriction that their
neurons should kind a bipartite graph: a .
BASIC CONCEPT OF DEEP LEARNING.pptx
Deep learning is a type of machine learning that uses neural networks with multiple layers to progressively extract higher-level features from raw input. Lower layers may identify simple elements like edges in images while higher layers identify more complex concepts like digits or faces. Deep learning models learn representations of data by using backpropagation to indicate how a machine should change its internal parameters to best fit the training data. Convolutional neural networks are a type of deep learning model that use convolution operations to identify patterns in grid-like data like images or text.
A Survey of Deep Learning Algorithms for Malware Detection
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
Deep Learning and Watson Studio
Deep learning and Watson Studio can be used for various tasks including planet discoveries, particle physics experiments at CERN, and scientific publications analysis. Convolutional neural networks are commonly used for image-related tasks like cancer diagnosis, object detection, and style transfer, while recurrent neural networks with LSTM or GRU are useful for sequential data like text for machine translation, sentiment analysis, and music generation. Hybrid and complex models combine different neural network architectures for tasks such as named entity recognition, music generation, blockchain security, and lip reading. Deep learning is now implemented using frameworks like TensorFlow and Keras on GPUs and distributed systems. Transfer learning helps accelerate development by reusing pre-trained models. Watson Studio provides a platform for developing, testing, and deploy
Deep learning Training-BITS.pptx
Burraq IT solutions provide Deep learning Training courses in Lahore. Deep learning is an artificial intelligence technique that trains computers to process data based on the human brain. Deep learning models can recognize complex patterns in images, text, sounds, and other data to produce accurate insights and predictions. You can use deep learning techniques to automate tasks that typically require human intelligence, such as describing images or transcribing an audio file into text. Deep learning algorithms are neural networks modeled on the human brain.
FreddyAyalaTorchDomineering
This summarizes a document describing the use of the Torch deep learning framework and convolutional neural networks to solve the Domineering game. It involves:
1) Generating training data for the neural network using Monte Carlo simulations of random Domineering games.
2) Loading the training data into Torch tensors.
3) Defining and implementing a convolutional neural network in Torch to take board configurations as input and output the best next move.
4) Training the neural network on the data for 1000 iterations using criteria and stochastic gradient descent optimization to minimize error between predictions and targets.
Cnn
Convolutional neural networks (CNNs) are a type of neural network used for processing grid-like data such as images. CNNs have an input layer, multiple hidden layers, and an output layer. The hidden layers typically include convolutional layers that extract features, pooling layers that reduce dimensionality, and fully connected layers similar to regular neural networks. CNNs are commonly used for computer vision tasks like image classification and object detection due to their ability to learn spatial hierarchies of features in the data. They have applications in areas like facial recognition, document analysis, and climate modeling.
Deep learning.pptx
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Deep learning tutorial 9/2019
This document provides an overview and introduction to deep learning. It discusses motivations for deep learning such as its powerful learning capabilities. It then covers deep learning basics like neural networks, neurons, training processes, and gradient descent. It also discusses different network architectures like convolutional neural networks and recurrent neural networks. Finally, it describes various deep learning applications, tools, and key researchers and companies in the field.
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Handwritten bangla-digit-recognition-using-deep-learning
- 1. Submitted By Subroto Saha Shuvro Md: Rumman Islam Nur Abdul Momin Department OF CSE WUB Supervise By Md. Ashiqur Rahman Senior Lecturer Department OF CSE WUB
- 2. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Ref:google.com
- 3. The traditional neural network consists of at most 2 layers and this type of structure of the neural network is not suitable for the computation of larger networks. Therefore, a neural network having more than 10 or even 100 layers is meant for Deep Learning. In this, a stack of the layer of neurons is developed. The lowest layer in the stack is responsible for the collection of raw data such as images, videos, text, etc. Each neuron of the lowest layer will store the information and pass the information further to the next layer of neurons and so on. As the information flows within the neurons of layers hidden information of the data is extracted. So, we can conclude that as the data moves from lowest layer to highest layer (moving deep inside the neural network) more abstracted information is collected.
- 4. Ref:google.com Automatic handwriting character recognition is of academic and commercial interests. Current algorithms are already excel in learning to recognize handwritten characters. The main challenge in handwritten character classification is to deal with the enormous variety of handwriting styles by different writers in different languages. Furthermore, some of the complex handwriting scripts comprise different styles for writing words. Depending on languages, characters are written isolated from each other in some cases, (e.g., Thai, Laos and Japanese). The large variety of writing styles, writers, and the complex features of handwritten characters are very challenging for accurately classifying the hand written characters.
- 5. Ref:google.com Support vector machine (SVM) : In machine learning, support vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Deep Belief Network (DFN): is a generative graphical model, of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers. Convolution neural network (CNN) : is a type of deep learning model that combines a deep CNN with Q- learning, a form of reinforcement learning.
- 6. Ref:google.com Gaussian Filter: Gaussian being or having the shape of a normal curve or a normal distribution. Gabor Filter: Gabor, is a linear filter used for texture analysis. Dropout is a regularization technique for reducing over fitting in neural networks.
- 7. Restricted Boltzman Machine (RBM): A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. CNN with Dropouts: Dropouts in CNN is very powerful training techniques usually used for fully connected layers.
- 13. • Dataset Description • CNN structure & parameters setup • DBN structure & parameters setup • Performance Evaluation • Comparison with the state of the arts
- 14. In this research, we proposed to use deep learning approaches for handwritten Bangla digit recognition(HBDR). We evaluated the performance of CNN and DBN with combination of dropout and different filters on a standard benchmark dataset: CMATERdb 3.1.1. From experimental results, it is observed that CNN with Gabor feature and dropout yields the best accuracy for HBDR compared to the alternative state-of-the-art techniques. Research work is currently progressing to develop more sophisticated deep neural networks with combination of State Preserving Extreme Learning Machine (Alom et al., 2015) for handwritten Bangla numeral and character recognition.