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.
The presentation briefly answers the questions:
1. What is Machine Learning?
2. Ideas behind Neural Networks?
3. What is Deep Learning? How different is it from NN?
4. Practical examples of applications.

For more information:
https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng
http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf
Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
papers: http://www.cs.toronto.edu/~hinton/science.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf
http://arxiv.org/pdf/1206.5538v3.pdf
http://arxiv.org/pdf/1404.7828v4.pdf
More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning
This document provides an overview of deep learning, including its history, algorithms, tools, and applications. It begins with the history and evolution of deep learning techniques. It then discusses popular deep learning algorithms like convolutional neural networks, recurrent neural networks, autoencoders, and deep reinforcement learning. It also covers commonly used tools for deep learning and highlights applications in areas such as computer vision, natural language processing, and games. In the end, it discusses the future outlook and opportunities of deep learning.
Deep Learning for Computer Vision: A comparision between Convolutional Neural...Vincenzo Lomonaco
In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general.
However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of “intelligence”.
The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them.
CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems.
HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain.
In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.
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 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.
The document provides an overview of deep learning, including its past, present, and future. It discusses the concepts of artificial general intelligence, artificial superintelligence, and predictions about their development from experts like Hawking, Musk, and Gates. Key deep learning topics are summarized, such as neural networks, machine learning approaches, important algorithms and researchers, and how deep learning works.
The presentation briefly answers the questions:
1. What is Machine Learning?
2. Ideas behind Neural Networks?
3. What is Deep Learning? How different is it from NN?
4. Practical examples of applications.

For more information:
https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng
http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf
Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
papers: http://www.cs.toronto.edu/~hinton/science.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf
http://arxiv.org/pdf/1206.5538v3.pdf
http://arxiv.org/pdf/1404.7828v4.pdf
More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning
This document provides an overview of deep learning, including its history, algorithms, tools, and applications. It begins with the history and evolution of deep learning techniques. It then discusses popular deep learning algorithms like convolutional neural networks, recurrent neural networks, autoencoders, and deep reinforcement learning. It also covers commonly used tools for deep learning and highlights applications in areas such as computer vision, natural language processing, and games. In the end, it discusses the future outlook and opportunities of deep learning.
Deep Learning for Computer Vision: A comparision between Convolutional Neural...Vincenzo Lomonaco
In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general.
However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of “intelligence”.
The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them.
CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems.
HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain.
In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.
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 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.
The document provides an overview of deep learning, including its past, present, and future. It discusses the concepts of artificial general intelligence, artificial superintelligence, and predictions about their development from experts like Hawking, Musk, and Gates. Key deep learning topics are summarized, such as neural networks, machine learning approaches, important algorithms and researchers, and how deep learning works.
Deep Learning Hardware: Past, Present, & FutureRouyun Pan
Yann LeCun gave a presentation on deep learning hardware, past, present, and future. Some key points:
- Early neural networks in the 1960s-1980s were limited by hardware and algorithms. The development of backpropagation and faster floating point hardware enabled modern deep learning.
- Convolutional neural networks achieved breakthroughs in vision tasks in the 1980s-1990s but progress slowed due to limited hardware and data.
- GPUs and large datasets like ImageNet accelerated deep learning research starting in 2012, enabling very deep convolutional networks for computer vision.
- Recent work applies deep learning to new domains like natural language processing, reinforcement learning, and graph networks.
- Future challenges include memory-aug
The document discusses deep learning concepts and frameworks. It provides an overview of deep learning concepts such as neural networks, layers, nodes, weights, activation functions, and optimization techniques. It also discusses specific deep learning frameworks including TensorFlow, Torch, and Theano. These frameworks can be compared based on factors like speed, ease of use, programming languages, hardware support, community size, and algorithms supported.
4 technologies including AI, IoT, blockchain, and virtual reality will revolutionize the maritime sector. AI and blockchain will have many applications including supply chain visibility, data science, voyage optimization, and diagnosis systems. Training seafarers for an AI-based future will require changes to curriculum, including teaching mathematics, programming, cybersecurity, and manual operation skills. Ensuring safe return to port capabilities with redundancy will also be important as autonomy increases.
This document provides an introduction to deep learning. It begins with a refresher on machine learning, covering classification, regression, supervised learning, unsupervised learning, and reinforcement learning. It then discusses neural networks and their basic components like layers, nodes, and weights. An example of unsupervised learning is given about learning Chinese. Deep learning is introduced as using large neural networks to learn complex feature hierarchies from large amounts of data. Key aspects of deep learning covered include representation learning, layer-wise training, and using unsupervised pre-training before supervised fine-tuning. Applications and impact areas of deep learning are also mentioned.
Deep learning is introduced along with its applications and key players in the field. The document discusses the problem space of inputs and outputs for deep learning systems. It describes what deep learning is, providing definitions and explaining the rise of neural networks. Key deep learning architectures like convolutional neural networks are overviewed along with a brief history and motivations for deep learning.
Gives an Introduction to Deep learning, What can you achieve with deep learning. What is deep learning's relationship with machine learning. Technical basics of working of deep learning. Introduction to LSTM. How LSTM can be used for Text classification. Results obtained.. Practical recommendations.
Handwritten Recognition using Deep Learning with RPoo Kuan Hoong
R User Group Malaysia Meet Up - Handwritten Recognition using Deep Learning with R
Source code available at: https://github.com/kuanhoong/myRUG_DeepLearning
This document outlines advances in deep learning and neural networks. It discusses challenges in machine learning like feature extraction. It describes how neuroscience experiments showed the brain's ability to learn new tasks. Neural networks aim to mimic the brain through techniques like backpropagation to train multi-layer models. Breakthroughs like pre-training and convolutional networks helped scale networks to many layers. Deep learning is now used in speech translation, image recognition, handwriting recognition and more.
Deep learning is a type of machine learning that uses neural networks inspired by the human brain. It has been successfully applied to problems like image recognition, speech recognition, and natural language processing. Deep learning requires large datasets, clear goals, computing power, and neural network architectures. Popular deep learning models include convolutional neural networks and recurrent neural networks. Researchers like Geoffry Hinton and companies like Google have advanced the field through innovations that have won image recognition challenges. Deep learning will continue solving harder artificial intelligence problems by learning from massive amounts of data.
This document summarizes research on using memristors to develop nano-scale associative memories (AMs). Key points:
- AMs are important for brain-like computing but scaling traditional circuits is challenging and stored information is fragile.
- Memristors are promising due to their small size and ability to remember resistance levels, but are typically only used as synapses in artificial neural networks (ANNs), not to their full potential.
- The authors develop a framework using genetic programming to automatically design AM circuits using memristors that can outperform traditional ANNs.
- Their results show memristor-based AMs can learn spatial and temporal correlations in inputs, optimize the tradeoff between size and
Soft computing (ANN and Fuzzy Logic) : Dr. Purnima PanditPurnima Pandit
The document discusses soft computing and its techniques, including artificial neural networks (ANN). It provides an overview of ANN, including how biological neurons inspired the basic ANN model. A neuron has inputs, outputs, weights, and an activation function. Networks can be single or multilayer. Learning involves updating weights to minimize error, with backpropagation commonly used for multilayer networks. Applications include pattern recognition, function approximation, and parameter estimation. A simple example is provided to estimate the slope and intercept of a line using ANN.
The document provides an overview of neural networks including:
- Their history from early models in the 1940s to the breakthrough of backpropagation in the 1980s.
- What a neural network is and how it works at the level of individual neurons and when connected together.
- Common applications of neural networks like prediction, classification, and clustering.
- Key considerations in choosing an appropriate neural network architecture and training data for a given problem.
IRJET- Recognition of Handwritten Characters based on Deep Learning with Tens...IRJET Journal
This paper proposes a convolutional neural network model to recognize handwritten digits using the MNIST dataset. The model is built using TensorFlow and consists of convolutional, pooling and fully connected layers. The model is trained on 60,000 images and tested on 10,000 images, achieving 98% accuracy on the training set and classifying digits with low error of 0.03% on the test set. Previous methods for handwritten digit recognition are discussed and the CNN approach is shown to provide superior performance with faster training times compared to other models.
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
This document provides an overview of deep learning, including definitions, origins, applications, and limitations. It defines deep learning as a machine learning technique that uses multiple layers to learn representations of data. Deep learning algorithms attempt to learn multiple levels of representation using a hierarchy of layers. While deep learning has been used since around 2000, it has grown as a subset of machine learning focused on deep artificial neural networks. Deep learning can learn both unsupervised and supervised and is useful for tasks like speech recognition, natural language processing, image recognition, and self-driving cars. However, it requires large amounts of data and time to train models.
This document is a resume for Ashly Bolton. It summarizes her professional experience including 10 years of military leadership experience leading over 500 personnel. She has extensive training and qualifications in leadership development, training, organizational development, and customer service management. Her objective is seeking a leadership position where she can utilize her skills in training, teaching, mentoring and coaching. She has held various roles in the military such as an artillery field manager, maintenance control manager, and senior army instructor. She also has private sector experience as a bank teller and director of a multi-level marketing company.
Impatto dell’utilizzo dei droni sulla sicurezza e sulla privacyAndrea Praitano
Presentazione tenuta presso il Forum ICT Security 2015 (16/10/2015) relativo alla tematica sicurezza, penetration testing e violazioni della privacy attraverso l'utilizzo di droni.
Deep Learning Hardware: Past, Present, & FutureRouyun Pan
Yann LeCun gave a presentation on deep learning hardware, past, present, and future. Some key points:
- Early neural networks in the 1960s-1980s were limited by hardware and algorithms. The development of backpropagation and faster floating point hardware enabled modern deep learning.
- Convolutional neural networks achieved breakthroughs in vision tasks in the 1980s-1990s but progress slowed due to limited hardware and data.
- GPUs and large datasets like ImageNet accelerated deep learning research starting in 2012, enabling very deep convolutional networks for computer vision.
- Recent work applies deep learning to new domains like natural language processing, reinforcement learning, and graph networks.
- Future challenges include memory-aug
The document discusses deep learning concepts and frameworks. It provides an overview of deep learning concepts such as neural networks, layers, nodes, weights, activation functions, and optimization techniques. It also discusses specific deep learning frameworks including TensorFlow, Torch, and Theano. These frameworks can be compared based on factors like speed, ease of use, programming languages, hardware support, community size, and algorithms supported.
4 technologies including AI, IoT, blockchain, and virtual reality will revolutionize the maritime sector. AI and blockchain will have many applications including supply chain visibility, data science, voyage optimization, and diagnosis systems. Training seafarers for an AI-based future will require changes to curriculum, including teaching mathematics, programming, cybersecurity, and manual operation skills. Ensuring safe return to port capabilities with redundancy will also be important as autonomy increases.
This document provides an introduction to deep learning. It begins with a refresher on machine learning, covering classification, regression, supervised learning, unsupervised learning, and reinforcement learning. It then discusses neural networks and their basic components like layers, nodes, and weights. An example of unsupervised learning is given about learning Chinese. Deep learning is introduced as using large neural networks to learn complex feature hierarchies from large amounts of data. Key aspects of deep learning covered include representation learning, layer-wise training, and using unsupervised pre-training before supervised fine-tuning. Applications and impact areas of deep learning are also mentioned.
Deep learning is introduced along with its applications and key players in the field. The document discusses the problem space of inputs and outputs for deep learning systems. It describes what deep learning is, providing definitions and explaining the rise of neural networks. Key deep learning architectures like convolutional neural networks are overviewed along with a brief history and motivations for deep learning.
Gives an Introduction to Deep learning, What can you achieve with deep learning. What is deep learning's relationship with machine learning. Technical basics of working of deep learning. Introduction to LSTM. How LSTM can be used for Text classification. Results obtained.. Practical recommendations.
Handwritten Recognition using Deep Learning with RPoo Kuan Hoong
R User Group Malaysia Meet Up - Handwritten Recognition using Deep Learning with R
Source code available at: https://github.com/kuanhoong/myRUG_DeepLearning
This document outlines advances in deep learning and neural networks. It discusses challenges in machine learning like feature extraction. It describes how neuroscience experiments showed the brain's ability to learn new tasks. Neural networks aim to mimic the brain through techniques like backpropagation to train multi-layer models. Breakthroughs like pre-training and convolutional networks helped scale networks to many layers. Deep learning is now used in speech translation, image recognition, handwriting recognition and more.
Deep learning is a type of machine learning that uses neural networks inspired by the human brain. It has been successfully applied to problems like image recognition, speech recognition, and natural language processing. Deep learning requires large datasets, clear goals, computing power, and neural network architectures. Popular deep learning models include convolutional neural networks and recurrent neural networks. Researchers like Geoffry Hinton and companies like Google have advanced the field through innovations that have won image recognition challenges. Deep learning will continue solving harder artificial intelligence problems by learning from massive amounts of data.
This document summarizes research on using memristors to develop nano-scale associative memories (AMs). Key points:
- AMs are important for brain-like computing but scaling traditional circuits is challenging and stored information is fragile.
- Memristors are promising due to their small size and ability to remember resistance levels, but are typically only used as synapses in artificial neural networks (ANNs), not to their full potential.
- The authors develop a framework using genetic programming to automatically design AM circuits using memristors that can outperform traditional ANNs.
- Their results show memristor-based AMs can learn spatial and temporal correlations in inputs, optimize the tradeoff between size and
Soft computing (ANN and Fuzzy Logic) : Dr. Purnima PanditPurnima Pandit
The document discusses soft computing and its techniques, including artificial neural networks (ANN). It provides an overview of ANN, including how biological neurons inspired the basic ANN model. A neuron has inputs, outputs, weights, and an activation function. Networks can be single or multilayer. Learning involves updating weights to minimize error, with backpropagation commonly used for multilayer networks. Applications include pattern recognition, function approximation, and parameter estimation. A simple example is provided to estimate the slope and intercept of a line using ANN.
The document provides an overview of neural networks including:
- Their history from early models in the 1940s to the breakthrough of backpropagation in the 1980s.
- What a neural network is and how it works at the level of individual neurons and when connected together.
- Common applications of neural networks like prediction, classification, and clustering.
- Key considerations in choosing an appropriate neural network architecture and training data for a given problem.
IRJET- Recognition of Handwritten Characters based on Deep Learning with Tens...IRJET Journal
This paper proposes a convolutional neural network model to recognize handwritten digits using the MNIST dataset. The model is built using TensorFlow and consists of convolutional, pooling and fully connected layers. The model is trained on 60,000 images and tested on 10,000 images, achieving 98% accuracy on the training set and classifying digits with low error of 0.03% on the test set. Previous methods for handwritten digit recognition are discussed and the CNN approach is shown to provide superior performance with faster training times compared to other models.
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
This document provides an overview of deep learning, including definitions, origins, applications, and limitations. It defines deep learning as a machine learning technique that uses multiple layers to learn representations of data. Deep learning algorithms attempt to learn multiple levels of representation using a hierarchy of layers. While deep learning has been used since around 2000, it has grown as a subset of machine learning focused on deep artificial neural networks. Deep learning can learn both unsupervised and supervised and is useful for tasks like speech recognition, natural language processing, image recognition, and self-driving cars. However, it requires large amounts of data and time to train models.
This document is a resume for Ashly Bolton. It summarizes her professional experience including 10 years of military leadership experience leading over 500 personnel. She has extensive training and qualifications in leadership development, training, organizational development, and customer service management. Her objective is seeking a leadership position where she can utilize her skills in training, teaching, mentoring and coaching. She has held various roles in the military such as an artillery field manager, maintenance control manager, and senior army instructor. She also has private sector experience as a bank teller and director of a multi-level marketing company.
Impatto dell’utilizzo dei droni sulla sicurezza e sulla privacyAndrea Praitano
Presentazione tenuta presso il Forum ICT Security 2015 (16/10/2015) relativo alla tematica sicurezza, penetration testing e violazioni della privacy attraverso l'utilizzo di droni.
The Purdue CCOCenter for Career Opportunities provides various career development services and resources to Purdue students and alumni. These include career counseling, resume and cover letter reviews, mock interviews, job search assistance, and networking opportunities. Students are encouraged to create an account on the myCCO website to access internship and job postings, apply for opportunities, and sign up for events. The document outlines recommended activities and services for students each year, such as exploring majors and careers as a freshman, gaining experience through internships as a junior, and networking as a senior. Graduate students are also offered tailored resources for their job searches.
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.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
The document provides an overview of deep learning, including its history, key concepts, applications, and recent advances. It discusses the evolution of deep learning techniques like convolutional neural networks, recurrent neural networks, generative adversarial networks, and their applications in computer vision, natural language processing, and games. Examples include deep learning for image recognition, generation, segmentation, captioning, and more.
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Foundations of ANNs: Tolstoy’s Genius Explored Using Transformer Architecturegerogepatton
Artificial Narrow Intelligence is in the phase of moving towards the AGN, which will attempt
to decide as a human being. We are getting closer to it by each day, but AI actually is indefinite to
many, although it is no different than any other set of mathematically defined computer operations in its
core. Generating new data from a pre-trained model introduces new challenges to science & technology. In
this work, the design of such an architecture from scratch, solving problems, and introducing alternative
approaches are what has been conducted. Using a deep thinker, Tolstoy, as an object of study is a source
of motivation for the entire research.
Foundations of ANNs: Tolstoy’s Genius Explored using Transformer Architecturegerogepatton
Artificial Narrow Intelligence is in the phase of moving towards the AGN, which will attempt
to decide as a human being. We are getting closer to it by each day, but AI actually is indefinite to
many, although it is no different than any other set of mathematically defined computer operations in its
core. Generating new data from a pre-trained model introduces new challenges to science & technology. In
this work, the design of such an architecture from scratch, solving problems, and introducing alternative
approaches are what has been conducted. Using a deep thinker, Tolstoy, as an object of study is a source
of motivation for the entire research.
Foundations of ANNs: Tolstoy’s Genius Explored Using Transformer Architectureijaia
Artificial Narrow Intelligence is in the phase of moving towards the AGN, which will attempt
to decide as a human being. We are getting closer to it by each day, but AI actually is indefinite to
many, although it is no different than any other set of mathematically defined computer operations in its
core. Generating new data from a pre-trained model introduces new challenges to science & technology. In
this work, the design of such an architecture from scratch, solving problems, and introducing alternative
approaches are what has been conducted. Using a deep thinker, Tolstoy, as an object of study is a source
of motivation for the entire research.
Traditional Machine Learning had used handwritten features and modality-specific machine learning to classify images, text or recognize voices. Deep learning / Neural network identifies features and finds different patterns automatically. Time to build these complex tasks has been drastically reduced and accuracy has exponentially increased because of advancements in Deep learning. Neural networks have been partly inspired from how 86 billion neurons work in a human and become more of a mathematical and a computer problem. We will see by the end of the blog how neural networks can be intuitively understood and implemented as a set of matrix multiplications, cost function, and optimization algorithms.
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.
Here is a presentation about Artificial Neural Networking(ANN).
Dive into the dynamic world of Artificial Neural Networking with our latest presentation! 🧠💡 Uncover the intricacies of neural networks, their role in AI, and the transformative impact on various industries. From fundamentals to advanced applications, this presentation offers a comprehensive exploration of the evolving landscape of neural networking. Join us on this journey of innovation and discovery. #ArtificialIntelligence #NeuralNetworking #Innovation
Neural networking this is about neural networksv02527031
Artificial neural networks (ANNs) are inspired by biological neural networks and are a type of machine learning. They follow principles of neuronal organization and learn by examples to make predictions. ANNs have multiple layers including an input layer, one or more hidden layers, and an output layer. They are often used for applications like image recognition, natural language processing, medical diagnosis, and autonomous vehicles. While ANNs can perform well on large datasets, they also face challenges including overfitting, data and computational requirements, and a lack of transparency.
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.
This document provides an overview of autoencoders and their use in unsupervised learning for deep neural networks. It discusses the history and development of neural networks, including early work in the 1940s-1980s and more recent advances in deep learning. It then explains how autoencoders work by setting the target values equal to the inputs, describes variants like denoising autoencoders, and how stacking autoencoders can create deep architectures for tasks like document retrieval, facial recognition, and signal denoising.
Deep learning Techniques JNTU R20 UNIT 2EXAMCELLH4
The document discusses natural language processing (NLP) and deep learning. It provides an overview of NLP, including common techniques like tokenization and part-of-speech tagging. Deep learning techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have achieved state-of-the-art results on NLP tasks. Overall, NLP is a rapidly evolving field that has the potential to revolutionize human-computer interaction.
This is an introduction to deep learning presented to Plymouth University students. In the introduction it is explained how a neural network works. In the practical section it is shown how to use Tensorflow for building simple models. Finally the case studies, how to use deep learning in real world applications.
Understanding Deep Learning & Parameter Tuning with MXnet, H2o Package in RManish Saraswat
Simple guide which explains deep learning and neural network with hands on experience in R using MXnet and H2o package. It also explains gradient descent and backpropagation algorithm.
Complete tutorial: http://blog.hackerearth.com/understanding-deep-learning-parameter-tuning-with-mxnet-h2o-package-r
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 document summarizes a lecture on deep learning. It began by defining deep learning as a subset of machine learning that uses mathematical functions and artificial neural networks to map inputs to outputs. It described how neural networks are arranged in layers to extract patterns from data through forward and backpropagation. The document contrasted deep learning with traditional machine learning, noting deep learning can process large, complex, unlabeled datasets while machine learning requires preprocessing. It provided examples of common neural network types like CNNs, RNNs, and GANs and their applications.
Nature Inspired Reasoning Applied in Semantic Webguestecf0af
1) Neural networks are computational structures inspired by biological neural networks and have been successfully used to solve complex tasks like image recognition and natural language processing.
2) Neural networks consist of interconnected nodes that perform simple mathematical functions to produce outputs. The connections between nodes and their weights can be modified through training to solve problems.
3) Nature inspired algorithms like neural networks are well-suited for semantic web problems because they can process large amounts of information quickly to find good enough solutions.
Artificial Neural Networks: Applications In ManagementIOSR Journals
With the advancement of computer and communication technology, the tools used for management decisions have undergone a gigantic change. Finding the more effective solution and tools for managerial problems is one of the most important topics in the management studies today. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component for business intelligence. The purpose of this article is to describe the basic behavior of neural networks as well as the works done in application of the same in management sciences and stimulate further research interests and efforts in the identified topics.
Artificial Neural Networks: Applications In Management
FreddyAyalaTorchDomineering
1. Abstract—Torch is a deep learning framework with wide
support for machine learning algorithms. It's open-source, simple
to use, and efficient, thanks to an easy and fast scripting language,
LuaJIT, and an underlying C / CUDA implementation.
Torch offers popular neural network and optimization libraries
that are easy to use, yet provide maximum flexibility to build
complex neural network topologies.
In the following paper we are going to use the torch framework,
convolutional neural networks and the LUA language in order to
solve the game name “Domineering:
Index Terms—AI, Torch, Torch7, deep learning, machine
learning, neural network, Artificial Intelligence, Lua.
I. INTRODUCTION
rtificial intelligence (AI) is intelligence exhibited by
machines. In computer science, an ideal "intelligent" machine
is a flexible rational agent that perceives its environment and
takes actions that maximize its chance of success at some goal.
Colloquially, the term "artificial intelligence" is applied when a
machine mimics "cognitive" functions that humans associate
with other human minds, such as "learning" and "problem
solving".
As machines become increasingly capable, mental facilities
once thought to require intelligence are removed from the
definition. For example, optical character recognition is no
longer perceived as an exemplar of "artificial intelligence",
having become a routine technology. Capabilities currently
classified as AI include successfully understanding human
speech, competing at a high level in strategic game systems
(such as Chess and Go), self-driving cars, and interpreting
complex data. Some people also consider AI a danger to
humanity if it progresses unabatedly.
AI research is divided into subfields that focus on specific
problems or on specific approaches or on the use of a particular
tool or towards satisfying particular applications.
Machine learning is the subfield of computer science that "gives
computers the ability to learn without being explicitly
programmed" (Arthur Samuel, 1959). Evolved from the study
of pattern recognition and computational learning theory in
artificial intelligence, machine learning explores the study and
construction of algorithms that can learn from and make
predictions on data – such algorithms overcome following
strictly static program instructions by making data driven
predictions or decisions, through building a model from sample
inputs.
II. DEEP LEARNING
Deep learning (also known as deep structured learning,
hierarchical learning or deep machine learning) is a branch of
machine learning based on a set of algorithms that attempt to
model high level abstractions in data. In a simple case, you
could have two sets of neurons: ones that receive an input signal
and ones that send an output signal. When the input layer
receives an input, it passes on a modified version of the input to
the next layer. In a deep network, there are many layers between
the input and output (and the layers are not made of neurons but
it can help to think of it that way), allowing the algorithm to use
multiple processing layers, composed of multiple linear and
non-linear transformations.
Deep learning is part of a broader family of machine learning
methods based on learning representations of data. An
observation (e.g., an image) can be represented in many ways
such as a vector of intensity values per pixel, or in a more
abstract way as a set of edges, regions of particular shape, etc.
Some representations are better than others at simplifying the
learning task (e.g., face recognition or facial expression
recognition). One of the promises of deep learning is replacing
handcrafted features with efficient algorithms for unsupervised
or semi-supervised feature learning and hierarchical feature
extraction.
III. TORCH FRAMEWORK
Torch is an open source machine learning library, a scientific
computing framework, and a script language based on the Lua
programming language. It provides a wide range of algorithms
for deep machine learning, and uses the scripting language
LuaJIT, and an underlying C implementation.
The core package of Torch is torch. It provides a flexible N-
dimensional array or Tensor, which supports basic routines for
indexing, slicing, transposing, type-casting, resizing, sharing
storage and cloning. This object is used by most other packages
and thus forms the core object of the library. The Tensor also
Deep Learning with Torch7 Framework
(December 2016)
Pablo Freddy Ayala Heinrich (Efrei Student)
A
2. supports mathematical operations like max, min, sum,
statistical distributions like uniform, normal and multinomial,
and BLAS operations like dot product, matrix-vector
multiplication, matrix-matrix multiplication, matrix-vector
product and matrix product.
IV. CONVOLUTIONAL NEURAL NETWORKS
Neural networks, as its name suggests, is a machine learning
technique which is modeled after the brain structure. It
comprises of a network of learning units called neurons. These
neurons learn how to convert input signals (e.g. picture of a cat)
into corresponding output signals (e.g. the label “cat”), forming
the basis of automated recognition.
The technique that Google researchers used is called
Convolutional Neural Networks (CNN), a type of advanced
artificial neural network. It differs from regular neural networks
in terms of the flow of signals between neurons. Typical neural
networks pass signals along the input-output channel in a single
direction, without allowing signals to loop back into the
network. This is called a forward feed.
While forward feed networks were successfully employed
for image and text recognition, it required all neurons to be
connected, resulting in an overly-complex network structure.
The cost of complexity grows when the network has to be
trained on large datasets which, coupled with the limitations of
computer processing speeds, result in grossly long training
times. Hence, forward feed networks have fallen into disuse
from mainstream machine learning in today’s high resolution,
high bandwidth, mass media age. A new solution was needed.
In 1986, researchers Hubel and Wiesel were examining a cat’s
visual cortex when they discovered that its receptive field
comprised sub-regions which were layered over each other to
cover the entire visual field. These layers act as filters that
process input images, which are then passed on to subsequent
layers. This proved to be a simpler and more efficient way to
carry signals. In 1998, Yann LeCun and Yoshua Bengio tried to
capture the organization of neurons in the cat’s visual cortex as
a form of artificial neural net, establishing the basis of the first
CNN.
V. CNN SUMMARIZED IN 4 STEPS
There are four main steps in CNN: convolution, subsampling,
activation and full connectedness.
A. Convolution
The first layers that receive an input signal are called
convolution filters. Convolution is a process where the network
tries to label the input signal by referring to what it has learned
in the past. If the input signal looks like previous cat images it
has seen before, the reference signal will be mixed into, or
convolved with, the input signal. The resulting output signal is
then passed on to the next layer.
B. Subsampling
Inputs from the convolution layer can be “smoothened” to
reduce the sensitivity of the filters to noise and variations. This
smoothing process is called subsampling, and can be achieved
by taking averages or taking the maximum over a sample of the
signal. Examples of subsampling methods (for image signals)
include reducing the size of the image, or reducing the color
contrast across red, green, blue (RGB) channels.
3. C. Activation
The activation layer controls how the signal flows from one
layer to the next, emulating how neurons are fired in our brain.
Output signals which are strongly associated with past
references would activate more neurons, enabling signals to be
propagated more efficiently for identification.
CNN is compatible with a wide variety of complex activation
functions to model signal propagation, the most common
function being the Rectified Linear Unit (ReLU), which is
favored for its faster training speed.
D. Fully Connected
The last layers in the network are fully connected, meaning that
neurons of preceding layers are connected to every neuron in
subsequent layers. This mimics high level reasoning where all
possible pathways from the input to output are considered.
When training the neural network, there is additional layer
called the loss layer. This layer provides feedback to the neural
network on whether it identified inputs correctly, and if not,
how far off its guesses were. This helps to guide the neural
network to reinforce the right concepts as it trains. This is
always the last layer during training.
Learning algorithms require feedback. This is done using a
validation set where the CNN would make predictions and
compare them with the true labels or ground truth. The
predictions which errors are made are then fed backwards to the
CNN to refine the weights learned, in a so called backwards
pass. Formally, this algorithm is called backpropagation of
errors, and it requires functions in the CNN to be differentiable
(almost).
VI. MONTE CARLO SIMULATION
Monte Carlo simulation is a computerized mathematical
technique that allows people to account for risk in quantitative
analysis and decision making. The technique is used by
professionals in such widely disparate fields as finance, project
management, energy, manufacturing, engineering, research and
development, insurance, oil & gas, transportation, and the
environment.
Monte Carlo simulation furnishes the decision-maker with a
range of possible outcomes and the probabilities they will occur
for any choice of action. It shows the extreme possibilities—the
outcomes of going for broke and for the most conservative
decision—along with all possible consequences for middle-of-
the-road decisions.
The technique was first used by scientists working on the atom
bomb; it was named for Monte Carlo, the Monaco resort town
renowned for its casinos. Since its introduction in World War
II, Monte Carlo simulation has been used to model a variety of
physical and conceptual systems.
VII. DOMINEERING GAME
Domineering (also called Stop-Gate or Crosscram) is a
mathematical game played on a sheet of graph paper, with any
set of designs traced out. For example, it can be played on a 6×6
square, a checkerboard, an entirely irregular polygon, or any
combination thereof.
The game is played on checkered boards of different sizes and
involves placing dominoes that cover two squares. Player A
starts and must place the dominoes in a ‘vertical’ orientation,
while Player B places the dominoes in a ‘horizontal’
orientation. The first player who is unable to place a domino on
the board has lost – there cannot be a draw.
VIII. GENERATING TRAINING DATA
In order to solve the Domineering game using a convolutional
neural network implemented in Torch it is necessary first to
generate training data that is going to be used to train the neural
network. In order to do so we use the MonteCarlo simulation in
the following way:
4. Select all the
Possible Moves for
Player A
Simulate 1000
games for all
possible moves
Pick the move with
the highest win
percentage
Select all the
Possible Moves for
Player B
Is there any moves left?
Yes
Player A loses
No
Simulate 1000
games for all
possible moves
Pick the move with
the highest win
percentage
Is there any moves left?
Player B loses
A C# program was written that evaluates all possible random
moves for each player and simulates for each move 1000 moves
until completion, picking the move that won the most times
until the game is completed. In order to load the information
into torch the program generates a csv file that has the following
structure:
[PlayerBoard], [FlippedBoard],[PlayerPlane],[MovetoLearn]
The real implemented program uses an 8x8 board, a 2x2
representation is shown for demonstration purposes:
0,0,0,0,1,1,1,1,1,1,1,1,1,0,0,0
Player Board: 2D representation of the current state
of the player board.
Flipped Board: Reverse values of the Board,
changing 1 per 0 and vice versa.
Player Plane: All positions occupied by 1 for player 2
and 0 for player 1.
Move to learn: Move to perform.
For each move, there is one line in the csv file generated using
the monte carlo simulation.
IX. LOADING THE DATA INTO TORCH
In torch the data is going to be loaded into tensors, that are
defined as:
local input = torch.Tensor(ctr,3,8,8)
local output = torch.Tensor(ctr,1,8,8)
For each line of the csv file, the tensors are loaded in the
following manner:
input[linenumber][tensornumber][row][column] = val
output[linenumber][1][row][column] = val
X. IMPLEMENTATION OF THE NEURAL NETWORK
In order to solve the problem using torch, we need to define
the following neural network:
Fully convolutional
3 planes for the input layer
64 planes for the hidden layers
Zero Padding
3x3 Filters
1 plane for the output layer
Rely
Softmax
The convolutional neural network is defined in the following
way:
require 'nn'
local convnet = nn.Sequential()
local nplanes = 64
--Filter, stride, padding
convnet:add(nn.SpatialConvolution(3,nplanes,5,5,1,1,2,2))
convnet:add(nn.ReLU())
convnet:add(nn.SpatialConvolution(nplanes,nplanes,3,3,1,1,1,
1))
0 0
0 0
1 1
1 1
1 1
1 1
1 0
0 0
5. convnet:add(nn.ReLU())
convnet:add(nn.SpatialConvolution(nplanes,nplanes,3,3,1,1,1,
1))
convnet:add(nn.ReLU())
convnet:add(nn.SpatialConvolution(nplanes,nplanes,3,3,1,1,1,
1))
convnet:add(nn.ReLU())
convnet:add(nn.SpatialConvolution(nplanes,nplanes,3,3,1,1,1,
1))
convnet:add(nn.ReLU())
convnet:add(nn.SpatialConvolution(nplanes,nplanes,3,3,1,1,1,
1))
convnet:add(nn.ReLU())
convnet:add(nn.SpatialConvolution(nplanes,1,3,3,1,1,1,1))
convnet:add(nn.View(64))
convnet:add(nn.SoftMax())
convnet:add(nn.View(8,8))
XI. TRAINING
In order to train our neural networks, we are going to analyses
two ways of doing so: criterion and optim SGD.
A. Criterion
Criterions are helpful to train a neural network. Given an input
and a target, they compute a gradient according to a given loss
function.
Criterions can be grouped into:
Classification
Regression
Embedding criterions
Miscellaneous criterions
Like the Module class, Criterions is an abstract class with the
following methods:
Forward(predicted, actualTarget). Given a predicted
value and a actual target computes the loss function
associated to the criterions.
Backward(input,target). Given an input and a target
compute the gradients of the loss function associated
to the criterion.
These two function should be used to compute the loss and
update the weights of the neural network during the training
phase.
In our example, we use the criterion as following to train the
neural network 1000 times for every line in the csv:
criterion = nn.MSECriterion()
for j =1,1000 do
for i =1,ctr-1 do
--feed it to the neural network and the criterion
err=criterion:forward(convnet:forward(input[i]),output[i])
--train over this example in 3 steps
--(1) zero the accumulation of the gradients
convnet:zeroGradParameters()
--(2) accumulate gradicleents
convnet:backward(input[i],criterion:backward(convnet.output,
output[i]))
--(3) update parameters with a 0.01 learning rate
convnet:updateParameters(0.2)
print("Error:"..err)
end
end
B. Optim SGD
This package provides a set of optimization algorithms, which
all follow a unified, closure-based API.
This package is fully compatible with the nn package, but can
also be used to optimize arbitrary objective functions, from this
package we used the Stochastic Gradient Descent.
Gradient descent is an optimization algorithm used to find the
values of parameters (coefficients) of a function (f) that
minimizes a cost function (cost).
Gradient descent is best used when the parameters cannot be
calculated analytically (e.g. using linear algebra) and must be
searched for by an optimization algorithm.
Gradient descent can be slow to run on very large datasets.
Because one iteration of the gradient descent algorithm requires
a prediction for each instance in the training dataset, it can take
a long time when you have many millions of instances.
In situations when you have large amounts of data, you can use
a variation of gradient descent called stochastic gradient
descent. In this variation, the gradient descent procedure
described above is run but the update to the coefficients is
performed for each training instance, rather than at the end of
the batch of instances.
The first step of the procedure requires that the order of the
training dataset is randomized. This is to mix up the order that
updates are made to the coefficients. Because the coefficients
are updated after every training instance, the updates will be
noisy jumping all over the place, and so will the corresponding
cost function. By mixing up the order for the updates to the
coefficients, it harnesses this random walk and avoids it getting
distracted or stuck.
6. The update procedure for the coefficients is the same as that
above, except the cost is not summed over all training patterns,
but instead calculated for one training pattern.
The learning can be much faster with stochastic gradient
descent for very large training datasets and often you only need
a small number of passes through the dataset to reach a good or
good enough set of coefficients, e.g. 1-to-10 passes through the
dataset.
We define our training method as following:
criterion = nn.MSECriterion()
for j =1,1000 do
for i =1,ctr-1 do
print("Cycle:"..j)
print("Tensor["..i.."]")
local optimState = {learningRate=0.2}
params, gradParams = convnet:getParameters()
local function feval(params)
gradParams:zero()
local outputs = convnet:forward(input[i])
local loss = criterion:forward(outputs,output[i])
local dloss_doutput = criterion:backward(outputs,
output[i])
print('loss:'..loss)
convnet:backward(input[i],dloss_doutput )
return loss, gradParams
end
optim.sgd(feval, params, optimState)
end
end
XII. TESTING THE MODEL
In order to test the learned model, we run it against the
original output using the method convent:forward.
for j=1,ctr-1 do
print(j)
out = convnet:forward(input[j])
print("input:")
print(input[j])
print("output:(original)")
print(output[j])
print("output:(learned)")
print(out)
end
Therefore, if we test the model we get the following
results,by using this input:
input:
(1,.,.) =
0 0 0 0 0 1 0 0
1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0
1 0 0 0 1 0 0 0
0 0 0 0 0 0 0 0
0 0 1 1 0 0 0 0
0 0 0 1 0 0 0 0
(2,.,.) =
1 1 1 1 1 0 1 1
0 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
0 1 1 1 1 1 1 1
0 1 1 1 0 1 1 1
1 1 1 1 1 1 1 1
1 1 0 0 1 1 1 1
1 1 1 0 1 1 1 1
(3,.,.) =
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
[torch.DoubleTensor of size 3x8x8]
We can compare the original output
output:(original)
(1,.,.) =
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
[torch.DoubleTensor of size 1x8x8]
To the learned output:
output:(learned)
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
[torch.DoubleTensor of size 8x8]
7. Therefore, we can verify that our model has correctly learned
how to play domineering.
XIII. CONCLUSIONS
The goal of Torch is to have maximum flexibility and speed
in building your scientific algorithms while making the process
extremely simple. Torch comes with a large ecosystem of
community-driven packages in machine learning, computer
vision, signal processing, parallel processing, image, video,
audio and networking among others, and builds on top of the
Lua community.
At the heart of Torch are the popular neural network and
optimization libraries which are simple to use, while having
maximum flexibility in implementing complex neural network
topologies. You can build arbitrary graphs of neural networks,
and parallelize them over CPUs and GPUs in an efficient
manner.
Consequently, we have successfully implemented a model that
uses torch in order to play the Domineering game using Deep
Learning and convolutional neural networks.
REFERENCES
[1] http://torch.ch/
[2] https://research.fb.com/fair-open-sources-deep-learning-modules-for-
torch/
[3] https://en.wikipedia.org/wiki/Torch_(machine_learning)
[4] https://github.com/torch/torch7/wiki/Cheatsheet
[5] https://github.com/torch/tutorials
[6] http://fastml.com/torch-vs-theano/
[7] http://hunch.net/~nyoml/torch7.pdf