Playing Atari with Deep Reinforcement Learning
- Author: Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller
- Origin: https://arxiv.org/abs/1312.5602
- Related: https://github.com/number9473/nn-algorithm/issues/250
This document discusses matrices and determinants. It provides the general form of a matrix and defines what a determinant is. It then provides examples of how to calculate the determinant of matrices of different sizes (second order, third order, and higher). It also lists some theorems regarding how changing elements or rows/columns of a matrix affects its determinant value.
This document contains tables summarizing rate laws, integrated rate laws, and derivations for several common reaction orders and conditions:
Table 7a describes zero-order, first-order, second-order, and general nth-order reactions for a single reactant A. It shows the rate law, integrated rate law, derivations, and graphs for each order.
Table 7b describes reactions with two reactants A and B, where the rate is given by the rate law rate = k[A]x[B]y. It lists the specific rate laws and integrated rate laws for different values of x and y.
This document discusses using a recurrent neural network (RNN) to manage an environment and take actions. The RNN learns to maximize reward by updating its value function based on the reward received and the difference in value estimates for the current and next states. The RNN predicts the probability of actions at each time step based on the previous states and actions.
Nelly Litvak presents a document on degree-degree dependencies in random graphs with heavy-tailed degrees. She discusses Newman's assortativity coefficient ρ(G) which is a measure of correlations between the degrees of connected nodes. Positive values indicate assortative mixing where high degree nodes connect to other high degree nodes, while negative values indicate disassortative mixing. She reviews that technological and biological networks are typically disassortative while social networks are assortative. Litvak then presents theorems showing that in power law graphs with γ ∈ (1,3), the assortativity coefficient converges to a non-negative value, so these graphs are never strongly disassortative. She also discusses
The document discusses various number systems including decimal, binary, octal and hexadecimal systems. It provides details on the concepts of natural numbers, integers, rational numbers and negative numbers. It explains how decimal, binary, octal and hexadecimal numbers are represented and how to convert between these different number bases. Techniques for addition, subtraction and arithmetic operations in binary numbers are also summarized.
This document introduces R functions for analyzing time-series data. It describes functions for testing the stationarity of time series, identifying break points, and computing determinacy and causality values. The functions prototype tests originally developed as Basic and Java programs. The R functions offer easy usability, powerful data processing and visualization capabilities. Examples demonstrate applying stationary, abnormality, determinacy, and causality tests to economic time series data from Japan. Further refinement of some tests and visualization skills is still needed.
This flow diagram summarizes the process of enrolling, allocating, and following participants in a clinical trial. It shows the number of participants assessed for eligibility, excluded based on inclusion criteria or declining participation. It then displays the number randomized into each study group, receiving the allocated intervention, lost to follow up, discontinuing the intervention, and finally analyzed.
This document discusses matrices and determinants. It provides the general form of a matrix and defines what a determinant is. It then provides examples of how to calculate the determinant of matrices of different sizes (second order, third order, and higher). It also lists some theorems regarding how changing elements or rows/columns of a matrix affects its determinant value.
This document contains tables summarizing rate laws, integrated rate laws, and derivations for several common reaction orders and conditions:
Table 7a describes zero-order, first-order, second-order, and general nth-order reactions for a single reactant A. It shows the rate law, integrated rate law, derivations, and graphs for each order.
Table 7b describes reactions with two reactants A and B, where the rate is given by the rate law rate = k[A]x[B]y. It lists the specific rate laws and integrated rate laws for different values of x and y.
This document discusses using a recurrent neural network (RNN) to manage an environment and take actions. The RNN learns to maximize reward by updating its value function based on the reward received and the difference in value estimates for the current and next states. The RNN predicts the probability of actions at each time step based on the previous states and actions.
Nelly Litvak presents a document on degree-degree dependencies in random graphs with heavy-tailed degrees. She discusses Newman's assortativity coefficient ρ(G) which is a measure of correlations between the degrees of connected nodes. Positive values indicate assortative mixing where high degree nodes connect to other high degree nodes, while negative values indicate disassortative mixing. She reviews that technological and biological networks are typically disassortative while social networks are assortative. Litvak then presents theorems showing that in power law graphs with γ ∈ (1,3), the assortativity coefficient converges to a non-negative value, so these graphs are never strongly disassortative. She also discusses
The document discusses various number systems including decimal, binary, octal and hexadecimal systems. It provides details on the concepts of natural numbers, integers, rational numbers and negative numbers. It explains how decimal, binary, octal and hexadecimal numbers are represented and how to convert between these different number bases. Techniques for addition, subtraction and arithmetic operations in binary numbers are also summarized.
This document introduces R functions for analyzing time-series data. It describes functions for testing the stationarity of time series, identifying break points, and computing determinacy and causality values. The functions prototype tests originally developed as Basic and Java programs. The R functions offer easy usability, powerful data processing and visualization capabilities. Examples demonstrate applying stationary, abnormality, determinacy, and causality tests to economic time series data from Japan. Further refinement of some tests and visualization skills is still needed.
This flow diagram summarizes the process of enrolling, allocating, and following participants in a clinical trial. It shows the number of participants assessed for eligibility, excluded based on inclusion criteria or declining participation. It then displays the number randomized into each study group, receiving the allocated intervention, lost to follow up, discontinuing the intervention, and finally analyzed.
This document summarizes the topics covered in the CSE460 VLSI Design lecture 2. It reviews digital logic design concepts including logic gates, Boolean algebra, truth tables, logic functions and synthesis using sum of products and k-maps. It also discusses logic blocks like multiplexers and sequential elements like latches and flip-flops. Positive and negative level triggered latches are described along with positive and negative edge triggered flip-flops and how they can be built from latches. Finally, it distinguishes between level and edge triggered elements.
The document discusses backward search planning and plan-space planning techniques. Backward search starts at the goal and works backwards to find a plan, and can have a lower branching factor than forward search. Plan-space planning formulates planning as a constraint satisfaction problem to produce partially ordered plans with more flexibility. It works by iteratively refining a partial plan to resolve flaws such as open goals or threats between causal links, until a solution plan with no flaws is found.
This document discusses probabilistic planning domains and solutions. It defines a probabilistic planning domain as one where actions have multiple possible outcomes, each with a probability. Solutions must be either safe, with a probability of 1 of reaching the goal, or unsafe, with a probability between 0 and 1. Both acyclic and cyclic safe policies are possible, while unsafe policies can get stuck with some probability less than 1.
This document discusses probabilistic planning domains and solutions. It introduces the concept of actions having probabilistic outcomes in a probabilistic planning domain. Solutions to stochastic shortest path problems must be either safe, with a probability of 1 of reaching the goal, or unsafe, with a probability between 0 and 1. Both acyclic and cyclic safe policies are possible, while unsafe policies can get stuck in implicit or explicit dead ends with some non-zero probability of failing to reach the goal. Examples of different types of policies are provided to illustrate safe versus unsafe solutions.
The document describes Q-learning, an algorithm for reinforcement learning. Q-learning allows an agent to learn through trial-and-error interactions with its environment without relying on training data. The algorithm works by the agent storing the quality (Q) of taking actions in a Q-table, which is updated each time an action is taken. The agent's goal is to learn which actions yield the maximum reward by finding the optimal policy that maximizes long-term rewards. The document provides an example of using Q-learning to train a robot to navigate a maze by updating the Q-table after each action based on the reward received.
A very private, introductory notes on reinforcement learning.
Focusing to understand DQN by Google DeepMind.
Mainly based on the nice article
"DEMYSTIFYING DEEP REINFORCEMENT LEARNING"by Tambet Matiisen on
http://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/
Problem statement mathematical foundationsBangaluru
The document provides instructions and grading guidelines for a mathematical foundations assignment. It asks the student to submit their work by uploading pictures of handwritten calculations along with explanations in a word document. It then lists four problems to solve related to maxima/minima of functions, finding the slope and equation of a line, and identifying critical points. The grading scale ranges from A to F based on whether the assignment was completed and submitted on time.
This document provides a summary of spatial data modeling and analysis techniques. It begins with an outline of the topics to be covered, including additive statistical models for spatial data, spatial covariance functions, the multivariate normal distribution, kriging for prediction and uncertainty, and the likelihood function for parameter estimation. It then introduces the key concepts and equations for modeling spatial processes as Gaussian random fields with specified covariance functions. Examples are given of commonly used covariance functions and the types of random surfaces they generate. Kriging is described as a best linear unbiased prediction technique that uses a spatial covariance function and observations to make predictions at unknown locations. The document concludes with examples of parameter estimation via maximum likelihood and using the fitted model to make predictions and conditional simulations
It will give a short overview of Reinforcement Learning and its combination with Neural Networks (Deep Reinforcement Learning) in a brief and simple way
This document provides an overview of reinforcement learning. It discusses how reinforcement learning differs from supervised and unsupervised learning by focusing on choosing actions to maximize long-term reward. It also formalizes reinforcement learning problems using Markov decision processes and describes algorithms like value iteration and policy iteration that can be used to find optimal policies.
2014-06-20 Multinomial Logistic Regression with Apache SparkDB Tsai
Logistic Regression can not only be used for modeling binary outcomes but also multinomial outcome with some extension. In this talk, DB will talk about basic idea of binary logistic regression step by step, and then extend to multinomial one. He will show how easy it's with Spark to parallelize this iterative algorithm by utilizing the in-memory RDD cache to scale horizontally (the numbers of training data.) However, there is mathematical limitation on scaling vertically (the numbers of training features) while many recent applications from document classification and computational linguistics are of this type. He will talk about how to address this problem by L-BFGS optimizer instead of Newton optimizer.
Bio:
DB Tsai is a machine learning engineer working at Alpine Data Labs. He is recently working with Spark MLlib team to add support of L-BFGS optimizer and multinomial logistic regression in the upstream. He also led the Apache Spark development at Alpine Data Labs. Before joining Alpine Data labs, he was working on large-scale optimization of optical quantum circuits at Stanford as a PhD student.
The document contains the solutions to several algorithm questions. It begins by discussing a recursive Fibonacci number program, calculating that it performs Gn = Fn - 1 additions for the nth term. It then provides two more efficient algorithms using dynamic programming with an array or matrix exponentiation with O(log n) time. Subsequent questions discuss quicksort's O(log n) space complexity, deriving a closed formula for a divide-and-conquer recurrence, and proving properties about the minimum of |x-xi| and maximum product partition of a number.
The document discusses asymptotic notation, which is used to describe how algorithms scale as the input size increases. It defines Big-O, Big-Omega, and Big-Theta notations to classify functions based on their growth rates. Specifically, it provides the mathematical definitions of O, Ω, and Θ notations and examples of classifying common functions like polynomials, logarithms, and exponentials using this notation. It also covers properties like transitivity and relations between the different notations.
TensorFlow and Deep Learning Tips and TricksBen Ball
Presented at https://www.meetup.com/TensorFlow-and-Deep-Learning-Singapore/events/241183195/ . Tips and Tricks for using Tensorflow with Deep Reinforcement Learning.
See our blog for more information at http://prediction-machines.com/blog/
Reinforcement learning is a machine learning technique where an agent learns how to behave in an environment by receiving rewards or punishments for its actions. The goal of the agent is to learn an optimal policy that maximizes long-term rewards. Reinforcement learning can be applied to problems like game playing, robot control, scheduling, and economic modeling. The reinforcement learning process involves an agent interacting with an environment to learn through trial-and-error using state, action, reward, and policy. Common algorithms include Q-learning which uses a Q-table to learn the optimal action-selection policy.
The document discusses techniques for parameter selection and sensitivity analysis when estimating parameters from observational data. It introduces local sensitivity analysis based on derivatives to determine how sensitive model outputs are to individual parameters. Global sensitivity analysis techniques like ANOVA (analysis of variance) are also discussed, which quantify how parameter uncertainties contribute to uncertainty in model outputs. The ANOVA approach uses a Sobol decomposition to represent models as sums of parameter main effects and interactions, allowing variance-based sensitivity indices to be defined that quantify the influence of individual parameters and groups of parameters.
Efficient Scalar Multiplication for Ate Based Pairing over KSS Curve of Embed...Md. Al-Amin Khandaker Nipu
Efficiency of the next generation pairing based security pro- tocols rely not only on the faster pairing calculation but also on efficient scalar multiplication on higher degree rational points. In this paper we proposed a scalar multiplication technique in the context of Ate based pairing with Kachisa-Schaefer-Scott (KSS) pairing friendly curves with embedding degree k = 18 at the 192-bit security level. From the system- atically obtained characteristics p, order r and Frobenious trace t of KSS curve, which is given by certain integer z also known as mother parame- ter, we exploit the relation #E(Fp) = p+1−t mod r by applying Frobe- nius mapping with rational point to enhance the scalar multiplication. In addition we proposed z-adic representation of scalar s. In combination of Frobenious mapping with multi-scalar multiplication technique we ef- ficiently calculate scalar multiplication by s. Our proposed method can achieve 3 times or more than 3 times faster scalar multiplication com- pared to binary scalar multiplication, sliding-window and non-adjacent form method.
Overview on Optimization algorithms in Deep LearningKhang Pham
Overview on function optimization in general and in deep learning. The slides cover from basic algorithms like batch gradient descent, stochastic gradient descent to the state of art algorithm like Momentum, Adagrad, RMSprop, Adam.
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Se...郁凱 黃
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning
- Author: Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang
- Origin: https://papers.nips.cc/paper/5421-deep-learning-for-real-time-atari-game-play-using-offline-monte-carlo-tree-search-planning
- Related: https://github.com/number9473/nn-algorithm/issues/251
Human-level control through deep reinforcement learning郁凱 黃
Human-level control through deep reinforcement learning
- Author: Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg & Demis Hassabis
- Origin: https://www.nature.com/articles/nature14236
- https://github.com/number9473/nn-algorithm/issues/252
Ring loss: Convex Feature Normalization for Face Recognition郁凱 黃
Ring loss is a feature normalization approach for deep networks that augments standard loss functions like softmax. It encourages the norm of sample features to be a learned parameter R rather than enforcing hard normalization. This soft normalization helps address issues with imbalanced classification margins and disconnect between training and testing metrics due to variation in feature norms. Experiments on large face recognition datasets show Ring loss improves performance compared to softmax, especially for low resolution images where feature norms are typically lower. It achieves state-of-the-art results on benchmarks like LFW, IJB-A, MegaFace, and CFP.
This document summarizes the topics covered in the CSE460 VLSI Design lecture 2. It reviews digital logic design concepts including logic gates, Boolean algebra, truth tables, logic functions and synthesis using sum of products and k-maps. It also discusses logic blocks like multiplexers and sequential elements like latches and flip-flops. Positive and negative level triggered latches are described along with positive and negative edge triggered flip-flops and how they can be built from latches. Finally, it distinguishes between level and edge triggered elements.
The document discusses backward search planning and plan-space planning techniques. Backward search starts at the goal and works backwards to find a plan, and can have a lower branching factor than forward search. Plan-space planning formulates planning as a constraint satisfaction problem to produce partially ordered plans with more flexibility. It works by iteratively refining a partial plan to resolve flaws such as open goals or threats between causal links, until a solution plan with no flaws is found.
This document discusses probabilistic planning domains and solutions. It defines a probabilistic planning domain as one where actions have multiple possible outcomes, each with a probability. Solutions must be either safe, with a probability of 1 of reaching the goal, or unsafe, with a probability between 0 and 1. Both acyclic and cyclic safe policies are possible, while unsafe policies can get stuck with some probability less than 1.
This document discusses probabilistic planning domains and solutions. It introduces the concept of actions having probabilistic outcomes in a probabilistic planning domain. Solutions to stochastic shortest path problems must be either safe, with a probability of 1 of reaching the goal, or unsafe, with a probability between 0 and 1. Both acyclic and cyclic safe policies are possible, while unsafe policies can get stuck in implicit or explicit dead ends with some non-zero probability of failing to reach the goal. Examples of different types of policies are provided to illustrate safe versus unsafe solutions.
The document describes Q-learning, an algorithm for reinforcement learning. Q-learning allows an agent to learn through trial-and-error interactions with its environment without relying on training data. The algorithm works by the agent storing the quality (Q) of taking actions in a Q-table, which is updated each time an action is taken. The agent's goal is to learn which actions yield the maximum reward by finding the optimal policy that maximizes long-term rewards. The document provides an example of using Q-learning to train a robot to navigate a maze by updating the Q-table after each action based on the reward received.
A very private, introductory notes on reinforcement learning.
Focusing to understand DQN by Google DeepMind.
Mainly based on the nice article
"DEMYSTIFYING DEEP REINFORCEMENT LEARNING"by Tambet Matiisen on
http://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/
Problem statement mathematical foundationsBangaluru
The document provides instructions and grading guidelines for a mathematical foundations assignment. It asks the student to submit their work by uploading pictures of handwritten calculations along with explanations in a word document. It then lists four problems to solve related to maxima/minima of functions, finding the slope and equation of a line, and identifying critical points. The grading scale ranges from A to F based on whether the assignment was completed and submitted on time.
This document provides a summary of spatial data modeling and analysis techniques. It begins with an outline of the topics to be covered, including additive statistical models for spatial data, spatial covariance functions, the multivariate normal distribution, kriging for prediction and uncertainty, and the likelihood function for parameter estimation. It then introduces the key concepts and equations for modeling spatial processes as Gaussian random fields with specified covariance functions. Examples are given of commonly used covariance functions and the types of random surfaces they generate. Kriging is described as a best linear unbiased prediction technique that uses a spatial covariance function and observations to make predictions at unknown locations. The document concludes with examples of parameter estimation via maximum likelihood and using the fitted model to make predictions and conditional simulations
It will give a short overview of Reinforcement Learning and its combination with Neural Networks (Deep Reinforcement Learning) in a brief and simple way
This document provides an overview of reinforcement learning. It discusses how reinforcement learning differs from supervised and unsupervised learning by focusing on choosing actions to maximize long-term reward. It also formalizes reinforcement learning problems using Markov decision processes and describes algorithms like value iteration and policy iteration that can be used to find optimal policies.
2014-06-20 Multinomial Logistic Regression with Apache SparkDB Tsai
Logistic Regression can not only be used for modeling binary outcomes but also multinomial outcome with some extension. In this talk, DB will talk about basic idea of binary logistic regression step by step, and then extend to multinomial one. He will show how easy it's with Spark to parallelize this iterative algorithm by utilizing the in-memory RDD cache to scale horizontally (the numbers of training data.) However, there is mathematical limitation on scaling vertically (the numbers of training features) while many recent applications from document classification and computational linguistics are of this type. He will talk about how to address this problem by L-BFGS optimizer instead of Newton optimizer.
Bio:
DB Tsai is a machine learning engineer working at Alpine Data Labs. He is recently working with Spark MLlib team to add support of L-BFGS optimizer and multinomial logistic regression in the upstream. He also led the Apache Spark development at Alpine Data Labs. Before joining Alpine Data labs, he was working on large-scale optimization of optical quantum circuits at Stanford as a PhD student.
The document contains the solutions to several algorithm questions. It begins by discussing a recursive Fibonacci number program, calculating that it performs Gn = Fn - 1 additions for the nth term. It then provides two more efficient algorithms using dynamic programming with an array or matrix exponentiation with O(log n) time. Subsequent questions discuss quicksort's O(log n) space complexity, deriving a closed formula for a divide-and-conquer recurrence, and proving properties about the minimum of |x-xi| and maximum product partition of a number.
The document discusses asymptotic notation, which is used to describe how algorithms scale as the input size increases. It defines Big-O, Big-Omega, and Big-Theta notations to classify functions based on their growth rates. Specifically, it provides the mathematical definitions of O, Ω, and Θ notations and examples of classifying common functions like polynomials, logarithms, and exponentials using this notation. It also covers properties like transitivity and relations between the different notations.
TensorFlow and Deep Learning Tips and TricksBen Ball
Presented at https://www.meetup.com/TensorFlow-and-Deep-Learning-Singapore/events/241183195/ . Tips and Tricks for using Tensorflow with Deep Reinforcement Learning.
See our blog for more information at http://prediction-machines.com/blog/
Reinforcement learning is a machine learning technique where an agent learns how to behave in an environment by receiving rewards or punishments for its actions. The goal of the agent is to learn an optimal policy that maximizes long-term rewards. Reinforcement learning can be applied to problems like game playing, robot control, scheduling, and economic modeling. The reinforcement learning process involves an agent interacting with an environment to learn through trial-and-error using state, action, reward, and policy. Common algorithms include Q-learning which uses a Q-table to learn the optimal action-selection policy.
The document discusses techniques for parameter selection and sensitivity analysis when estimating parameters from observational data. It introduces local sensitivity analysis based on derivatives to determine how sensitive model outputs are to individual parameters. Global sensitivity analysis techniques like ANOVA (analysis of variance) are also discussed, which quantify how parameter uncertainties contribute to uncertainty in model outputs. The ANOVA approach uses a Sobol decomposition to represent models as sums of parameter main effects and interactions, allowing variance-based sensitivity indices to be defined that quantify the influence of individual parameters and groups of parameters.
Efficient Scalar Multiplication for Ate Based Pairing over KSS Curve of Embed...Md. Al-Amin Khandaker Nipu
Efficiency of the next generation pairing based security pro- tocols rely not only on the faster pairing calculation but also on efficient scalar multiplication on higher degree rational points. In this paper we proposed a scalar multiplication technique in the context of Ate based pairing with Kachisa-Schaefer-Scott (KSS) pairing friendly curves with embedding degree k = 18 at the 192-bit security level. From the system- atically obtained characteristics p, order r and Frobenious trace t of KSS curve, which is given by certain integer z also known as mother parame- ter, we exploit the relation #E(Fp) = p+1−t mod r by applying Frobe- nius mapping with rational point to enhance the scalar multiplication. In addition we proposed z-adic representation of scalar s. In combination of Frobenious mapping with multi-scalar multiplication technique we ef- ficiently calculate scalar multiplication by s. Our proposed method can achieve 3 times or more than 3 times faster scalar multiplication com- pared to binary scalar multiplication, sliding-window and non-adjacent form method.
Overview on Optimization algorithms in Deep LearningKhang Pham
Overview on function optimization in general and in deep learning. The slides cover from basic algorithms like batch gradient descent, stochastic gradient descent to the state of art algorithm like Momentum, Adagrad, RMSprop, Adam.
Similar to Playing Atari with Deep Reinforcement Learning (18)
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Se...郁凱 黃
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning
- Author: Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang
- Origin: https://papers.nips.cc/paper/5421-deep-learning-for-real-time-atari-game-play-using-offline-monte-carlo-tree-search-planning
- Related: https://github.com/number9473/nn-algorithm/issues/251
Human-level control through deep reinforcement learning郁凱 黃
Human-level control through deep reinforcement learning
- Author: Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg & Demis Hassabis
- Origin: https://www.nature.com/articles/nature14236
- https://github.com/number9473/nn-algorithm/issues/252
Ring loss: Convex Feature Normalization for Face Recognition郁凱 黃
Ring loss is a feature normalization approach for deep networks that augments standard loss functions like softmax. It encourages the norm of sample features to be a learned parameter R rather than enforcing hard normalization. This soft normalization helps address issues with imbalanced classification margins and disconnect between training and testing metrics due to variation in feature norms. Experiments on large face recognition datasets show Ring loss improves performance compared to softmax, especially for low resolution images where feature norms are typically lower. It achieves state-of-the-art results on benchmarks like LFW, IJB-A, MegaFace, and CFP.
A Revisit of Feature Learning on CNN-based Face Recognition郁凱 黃
A Revisit of Feature Learning on CNN-based Face Recognition.
Y. Sun, X. Wang, and X. Tang. Deep learning face representation from predicting 10,000 classes. In CVPR , 2014.
K. He, X. Zhang, S. Ren, J. Sun. Deep Residual Learning for Image Recognition.
Y. Sun, X. Wang, X. Tang. Deep Learning Face Representation by Joint Identification-Verification.
F. Schroff, D. Kalenichenko, J. Philbin. FaceNet: A Unified Embedding for Face Recognition and Clustering.
Y. Wen, K. Zhang, Z. Li, Y. Qiao. A Discriminative Feature Learning Approachg.
W. Liu, Y. Wen, Z. Yu, M. Yang. Large-Margin Softmax Loss for Convolutional Neural Networks.
W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, L. Song. SphereFace: Deep Hypersphere Embedding for Face Recognition.
Topic: design a puzzle game on smart phone
Akatsuki Hackthon 2015 心得: http://joyhuang9473.github.io/2015/09/07/akatsuki-hackathon-2015.html
Our Work - Ginger & GingerMan: http://joyhuang9473.github.io/project-general/project-game/2015/09/05/project-ginger-and-gingerman.html
This document provides an introduction and outline for a presentation on FreeBSD commands. It discusses how FreeBSD is Unix-like and provides an overview of commands related to users, networking, file ownership and access permissions. As an example, it demonstrates how to use the cp command to copy a file, listing the original file, running the cp command, and then listing both the original and copied file.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
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Playing Atari with Deep Reinforcement Learning
1. Playing Atari with Deep
Reinforcement Learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller.
NIPS Deep Learning Workshop 2013
Yu Kai Huang
2. Outline
● Reinforcement Learning
● Markov Decision Process
○ State, Action(Policy), Reward
○ Value function, Bellman Equation
● Optimal Policy
○ Bellman Optimality Equation
○ Q-learning
○ Deep Q-learning Network
● Experiments
○ Training and Stability
○ Evaluation
8. ● No supervisor, only a reward
signal.
● Feedback is delayed, not
instantaneous.
● Time really matters.
● Agent’s actions affect the
subsequent data it receives.
Reinforcement Learning
Image from
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/1-1-A-RL/
9. Reinforcement Learning
● State: the current situation that
the agent is in.
○ e.g. moving (position,
velocity, acceleration,...)
Image from https://homes.cs.washington.edu/~todorov/papers/TassaIROS12.pdf
10. Reinforcement Learning
● State: the current situation that
the agent is in.
○ e.g. moving (position,
velocity, acceleration,...)
● Action: a command that agent
can give in the game.
○ e.g. ↑, ↓, ←, →
Image from https://homes.cs.washington.edu/~todorov/papers/TassaIROS12.pdf
11. Reinforcement Learning
● State: the current situation that
the agent is in.
○ e.g. moving (position,
velocity, acceleration,...)
● Action: a command that agent
can give in the game.
○ e.g. ↑, ↓, ←, →
● Reward: given after performing
an action.
○ e.g. +1, -100
Image from https://homes.cs.washington.edu/~todorov/papers/TassaIROS12.pdf
12. Reinforcement Learning
● Full observability: agent directly
observes environment state.
● Agent state = environment state
= information state
● Formally, this is a Markov
Decision Process (MDP).
Image from https://homes.cs.washington.edu/~todorov/papers/TassaIROS12.pdf
14. Markov Decision Process
● Markov decision processes formally describe an environment for
reinforcement learning.
● Where the environment is fully observable.
● Almost all RL problems can be formalised as MDPs.
15. Markov Decision Process: State
● An MDP is a directed graph which has states for its nodes and edges which
describe transitions between Markov states.
○ State Transition Matrix
● Markov Property: “The future is independent of the past given the present”
○ The current state summarizes all past states.
○ e.g., if we only know the position of the ball but not its velocity, its state is
no longer Markov.
Image from http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf
17. Markov Decision Process: Policy
● A policy fully defines the behaviour of an agent.
Image from http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf
18. Markov Decision Process: Reward and Return
● Each time you make a transition into a state, you receive a reward.
● Agents should learn to maximize cumulative future reward.
○ Return
○ Discount factor
Image from http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf
19. Markov Decision Process: Value function
Image from http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf
20. Markov Decision Process: Bellman Equation
● if we know the value of the next state, we can know the value of the current
state.
Image from http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf
21. Markov Decision Process: Bellman Equation
Image from http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf,
https://www.cs.cmu.edu/~katef/DeepRLControlCourse/lectures/lecture2_mdps.pdf
22. Markov Decision Process: Bellman Equation
Image from http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf,
https://www.cs.cmu.edu/~katef/DeepRLControlCourse/lectures/lecture2_mdps.pdf
23. Markov Decision Process: Bellman Equation
Image from http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf,
https://www.cs.cmu.edu/~katef/DeepRLControlCourse/lectures/lecture2_mdps.pdf
43. Deep Q-learning Network
● Data Preprocessing: “The raw frames are preprocessed by first converting
their RGB representation to gray-scale and down-sampling it to a 110×84
[...] cropping an 84 × 84 region of the image [...].”
● Model Architecture
○ Input size: 84x84x4
○ Ouput size: 4 (←, →, x, B)
○ layers:
■ conv1(16, (8, 8), strides=(4, 4))
■ conv2(32, (4, 4), strides=(2, 2))
■ Dense(256)
■ Dense(4)
Image from https://becominghuman.ai/lets-build-an-atari-ai-part-0-intro-to-rl-9b2c5336e0ec
44. Deep Q-learning Network
● Experience Replay
○ “we store the agent’s experiences at each time-step, et = (st, at, rt, st+1)
in a data-set D = e1, ..., eN , pooled over many episodes into a replay
memory.”
51. Main Evaluation
● A trial: 5,000 training episodes, followed by 500 evaluation episodes.
● Average performance across 30 trials.
Image from https://arxiv.org/pdf/1312.5602.pdf
52. Ref.
[1] Jaromír Janisch: LET’S MAKE A DQN: THEORY https://jaromiru.com/2016/09/27/lets-make-a-dqn-theory/#fn-38-6
[2] Venelin Valkov: Solving an MDP with Q-Learning from scratch — Deep Reinforcement Learning for Hackers (Part 1)
https://medium.com/@curiousily/solving-an-mdp-with-q-learning-from-scratch-deep-reinforcement-learning-for-hackers-part-
1-45d1d360c120
[3] Flood Sung: Deep Reinforcement Learning 基础知识(DQN方面 https://blog.csdn.net/songrotek/article/details/50580904
[4] Flood Sung: 增强学习Reinforcement Learning经典算法梳理1:policy and value iteration
https://blog.csdn.net/songrotek/article/details/51378582
[5] mmc2015: reinforcement learning,增强学习:Policy Evaluation,Policy Iteration,Value Iteration,Dynamic Programming
https://blog.csdn.net/mmc2015/article/details/52859611
53. Ref.
[6] Gai's Blog: 增强学习 Reinforcement learning part 1 - Introduction https://bluesmilery.github.io/blogs/481fe3af/
[7] Gai's Blog: 增强学习 Reinforcement learning part 2 - Markov Decision Process
https://bluesmilery.github.io/blogs/e4dc3fbf/
[8] Gai's Blog: 增强学习 Reinforcement learning part 3 - Planning by Dynamic Programming
https://bluesmilery.github.io/blogs/b96003ba/
[9] Rowan McAllister: Introduction to Reinforcement Learning http://mlg.eng.cam.ac.uk/rowan/files/rl/01_mdps.pdf
[10] Adrien Lucas Ecoffet: Beat Atari with Deep Reinforcement Learning! (Part 0: Intro to RL)
https://becominghuman.ai/lets-build-an-atari-ai-part-0-intro-to-rl-9b2c5336e0ec
[11] Joshgreaves: Everything You Need to Know to Get Started in Reinforcement Learning
https://joshgreaves.com/reinforcement-learning/introduction-to-reinforcement-learning/
[12] Katerina Fragkiadaki: Deep Reinforcement Learning and Control: Markov Decision Processes
https://www.cs.cmu.edu/~katef/DeepRLControlCourse/lectures/lecture2_mdps.pdf