A model of the cashier’s brain with arbitrary initial weights

فهرست عناوین اصلی در این پاورپوینت

فهرست عناوین اصلی در این پاورپوینت

● Learning with linear neurons
● Prehistory
● Nervous Systems as Logical Circuits
● The Perceptron
● Linear neurons
● A motivating example
● Two ways to solve the equations
● The cashier’s brain
● A model of the cashier’s brain
with arbitrary initial weights
● Behavior of the iterative learning procedure
● Deriving the delta rule
● The error surface
● Online versus batch learning
● Adding biases
● Preprocessing the input vectors
● Is preprocessing cheating?
● Statistical and ANN Terminology
● Transfer functions
● Healthcare Applications of ANNs
● Classification Applications of ANNs
● Time Series Applications of ANNs
● Advantages of Using ANNs
● Problem with the Perceptron
● The Fall of the Perceptron
● The connectivity of a perceptron
● Binary threshold neurons
● The perceptron convergence procedure
● Weight space
● Why the learning procedure works
● What perceptrons cannot do
● What can perceptrons do?
● The N-bit even parity task
● Why connectedness is hard to compute
● Distinguishing T from C in any orientation and position
● Beyond perceptrons
● Sequential Perception
● Fisher Linear Discrimination
● A Regularized Fisher LDA
● Linear regression
● Ordinary Least Squares (OLS)
● Minimize the sum squared error
● Alternative derivation
● LMS Algorithm
(Least Mean Squares)
● Beyond lines and planes
● Geometric interpretation
● Ordinary Least Squares [summary] ● Probabilistic interpretation

نوع زبان: انگلیسی حجم: 2.09 مگا بایت
نوع فایل: اسلاید پاورپوینت تعداد اسلایدها: 59 صفحه
سطح مطلب: نامشخص پسوند فایل: ppt
گروه موضوعی: زمان استخراج مطلب: 2019/05/15 08:58:43

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عبارات مهم استفاده شده در این مطلب

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., weight, neuron, input, portion, linear, sum, output, price, error, learn, rule,

توجه: این مطلب در تاریخ 2019/05/15 08:58:43 به صورت خودکار از فضای وب آشکار توسط موتور جستجوی پاورپوینت جمع آوری شده است و در صورت اعلام عدم رضایت تهیه کننده ی آن، طبق قوانین سایت از روی وب گاه حذف خواهد شد. این مطلب از وب سایت زیر استخراج شده است و مسئولیت انتشار آن با منبع اصلی است.

https://www.cs.tau.ac.il/~nin/Courses/NC05/SingLayerPerc.ppt

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عبارات پرتکرار و مهم در این اسلاید عبارتند از: ., weight, neuron, input, portion, linear, sum, output, price, error, learn, rule,

مشاهده محتوای متنیِ این اسلاید ppt

مشاهده محتوای متنیِ این اسلاید ppt

learning with linear neurons adapted from lectures by geoffrey hinton and others updated by n. intrator may ۲ ۷ prehistory w.s. mcculloch w. pitts ۱۹۴۳ . a logical calculus of the ideas immanent in nervous activity bulletin of mathematical biophysics ۵ ۱۱۵ ۱۳۷. x y ۲ ۱ ۲ y ۱ x ۱ if sum else ۱ inputs weights sum output ۱ this seminal paper pointed out that simple artificial neurons could be made to perform basic logical operations such as and or and not. nervous systems as logical circuits groups of these neuronal logic gates could carry out any computation even though each neuron was very limited. x y ۱ ۱ ۱ y ۱ x ۱ if sum else ۱ inputs weights sum output ۱ ۱ ۱ could computers built from these simple units reproduce the computational power of biological brains were biological neurons performing logical operations the perceptron it obeyed the following rule if the sum of the weighted inputs exceeds a threshold output ۱ else output ۱. frank rosenblatt ۱۹۶۲ . principles of neurodynamics spartan new york ny. subsequent progress was inspired by the invention of learning rules inspired by ideas from neuroscience… rosenblatt’s perceptron could automatically learn to categorise or classify input vectors into types. ۱ if σ inputi weighti threshold ۱ if σ inputi weighti threshold linear neurons the neuron has a real valued output which is a weighted sum of its inputs the aim of learning is to minimize the discrepancy between the desired output and the actual output how de we measure the discrepancies do we update the weights after every training case why don’t we solve it analytically neuron’s estimate of the desired output input vector weight vector a motivating example each day you get lunch at the cafeteria. your diet consists of fish chips and beer. you get several portions of each the cashier only tells you the total price of the meal after several days you should be able to figure out the price of each portion. each meal price gives a linear constraint on the prices of the portions two ways to solve the equations the obvious approach is just to solve a set of simultaneous linear equations one per meal. but we want a method that could be implemented in a neural network. the prices of the portions are like the weights in of a linear neuron. we will start with guesses for the weights and then adjust the guesses to give a better fit to the prices given by the cashier. the cashier’s brain price of meal ۸۵ portions of fish portions of chips portions of beer ۱۵ ۵ ۱ ۲ ۵ ۳ linear neuron a model of the cashier’s brain with arbitrary initial weights residual error ۳۵ the learning rule is with a learning rate of ۱ ۳۵ the weight changes are ۲ ۵ ۳ this gives new weights of ۷ ۱ ۸ notice that the weight for chips got worse price of meal ۵ portions of fish portions of chips portions of beer ۵ ۵ ۵ ۲ ۵ ۳ behavior of the iterative learning procedure do the updates to the weights always make them get closer to their correct values no does the online version of the learning procedure eventually get the right answer yes if the learning rate gradually decreases in the appropriate way. how quickly do the weights converge to their correct values it can be very slow if two input dimensions are highly correlated e.g. ketchup and chips . can the iterative procedure be generalized to much more complicated multi layer non linear nets yes deriving the delta rule define the error as the squared residuals summed over all training cases now differentiate to get error derivatives for weights the batch delta rule changes the weights in proportion to their error derivatives summed over all training cases the error surface the error surface lies in a space with a horizontal axis for each weight and one vertical axis for the error. for a linear neuron it is a quadratic bowl. vertical cross sections are parabolas. horizontal cross sections are ellipses. e w۱ w۲ online versus batch learning batch learning does steepest descent on the error surface online learning zig zags around the direction of steepest descent w۱ w۲ w۱ w۲ constraint from training case ۱ constraint from training case ۲ adding biases a linear neuron is a more flexible model if we include a bias. we can avoid having to figure out a separate learning rule for the bias by using a trick a bias is exactly equivalent to a weight on an extra input line that always has an activity of ۱. preprocessing the input vectors instead of trying to predict the answer directly from the raw inputs we could start by extracting a layer of features . sensible if we already know that certain combinations of input values would be useful the features are equivalent to a layer of …

کلمات کلیدی پرکاربرد در این اسلاید پاورپوینت: ., weight, neuron, input, portion, linear, sum, output, price, error, learn, rule,

این فایل پاورپوینت شامل 59 اسلاید و به زبان انگلیسی و حجم آن 2.09 مگا بایت است. نوع قالب فایل ppt بوده که با این لینک قابل دانلود است. این مطلب برگرفته از سایت زیر است و مسئولیت انتشار آن با منبع اصلی می باشد که در تاریخ 2019/05/15 08:58:43 استخراج شده است.

https://www.cs.tau.ac.il/~nin/Courses/NC05/SingLayerPerc.ppt

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