{"id":24915,"date":"2021-04-13T23:10:04","date_gmt":"2021-04-14T03:10:04","guid":{"rendered":"http:\/\/bangla.salearningschool.com\/recent-posts\/implement-gradient-descend-root\/"},"modified":"2021-04-13T23:10:04","modified_gmt":"2021-04-14T03:10:04","slug":"implement-gradient-descend-root","status":"publish","type":"post","link":"http:\/\/bangla.sitestree.com\/?p=24915","title":{"rendered":"Implement Gradient Descend: #Root"},"content":{"rendered":"

"<\/p>\n

Gradient descent<\/strong> is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function usinggradient descent<\/strong>, one takes steps proportional to the negative of the gradient<\/strong> (or approximategradient<\/strong>) of the function at the current point.<\/p>\n

Gradient descent – Wikipedia<\/p>\n<\/h2>\n


\nhttps:\/\/en.wikipedia.org\/wiki\/Gradient_descent
\n<\/a><\/p>\n

" <\/h2>\n

Gradient Descend<\/h2>\n

# From calculation, it is expected that the local minimum occurs at x=9\/4<\/p>\n

"""<\/p>\n

cur_x = 6 # The algorithm starts at x=6<\/p>\n

gamma = 0.01 # step size multiplier<\/p>\n

precision = 0.00001<\/p>\n

previous_step_size = 1<\/p>\n

max_iters = 10000 # maximum number of iterations<\/p>\n

iters = 0 #iteration counter<\/p>\n

df = lambda x: 4 * x**3 – 9 * x**2<\/p>\n

while previous_step_size > precision and iters < max_iters:<\/p>\n

prev_x = cur_x<\/p>\n

cur_x -= gamma * df(prev_x)<\/p>\n

previous_step_size = abs(cur_x – prev_x)<\/p>\n

iters+=1<\/p>\n

print("The local minimum occurs at", cur_x)<\/p>\n

#The output for the above will be: (‘The local minimum occurs at’, 2.2499646074278457)<\/p>\n

"""<\/p>\n

#—-<\/p>\n

print(‘my part’)<\/p>\n

co_ef = 6<\/p>\n

iter = 0<\/p>\n

max_iter = 1000<\/p>\n

gamma = 0.001<\/p>\n

step = 1<\/p>\n

precision = 0.0000001<\/p>\n

df = lambda x: 4 * x * x * x – 9 * x * x<\/p>\n

while (iter <= max_iter) or (step >= precision ) :<\/p>\n

prev_co_ef = co_ef<\/p>\n

co_ef -= gamma * df (prev_co_ef)<\/p>\n

step = abs (prev_co_ef – co_ef)<\/p>\n

print(co_ef)<\/p>\n

From: https:\/\/sitestree.com\/implement-gradient-descend\/
Categories:Root
Tags:
Post Data:2019-01-05 15:17:29<\/p>\n

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" Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function usinggradient descent, one takes steps proportional to the negative of the gradient (or approximategradient) of the function at the current point. Gradient descent – Wikipedia https:\/\/en.wikipedia.org\/wiki\/Gradient_descent " Gradient Descend # From … <\/p>\n

Continue reading<\/a><\/p>\n","protected":false},"author":8,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1917],"tags":[],"class_list":["post-24915","post","type-post","status-publish","format-standard","hentry","category-fromsitestree-com","item-wrap"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack-related-posts":[{"id":14558,"url":"http:\/\/bangla.sitestree.com\/?p=14558","url_meta":{"origin":24915,"position":0},"title":"Implement Gradient Descend:","author":"Sayed","date":"January 5, 2019","format":false,"excerpt":"\" Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function usinggradient descent, one takes steps proportional to the negative of the gradient (or approximategradient) of the function at the current point. Gradient descent - Wikipedia https:\/\/en.wikipedia.org\/wiki\/Gradient_descent\u2026","rel":"","context":"In "Math and Statistics for Data Science, and Engineering"","block_context":{"text":"Math and Statistics for Data Science, and Engineering","link":"http:\/\/bangla.sitestree.com\/?cat=1908"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":6222,"url":"http:\/\/bangla.sitestree.com\/?p=6222","url_meta":{"origin":24915,"position":1},"title":"\u09b8\u09bf \u098f\u09b8 \u098f\u09b8 \u09e9 \u0997\u09cd\u09b0\u09cd\u09af\u09be\u09a1\u09bf\u09df\u09c7\u09a8\u09cd\u099f\u09b8 (CSS3 Gradients)","author":"Author-Check- Article-or-Video","date":"February 9, 2015","format":false,"excerpt":"CSS3 gradients \u09a6\u09c1\u099f\u09bf color \u098f\u09b0 \u09ae\u09a7\u09cd\u09af\u09c7 \u09b8\u0982\u09af\u09cb\u0997 \u09b8\u09cd\u09a5\u09b2 \u0995\u09c7 \u09ae\u09b8\u09c3\u09a3 \u0995\u09b0\u09c7\u0964 \u098f\u0987 \u0995\u09be\u099c \u099f\u09bf \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af \u0986\u09aa\u09a8\u09be\u0995\u09c7 \u098f\u0995\u099f\u09bf image \u09ac\u09cd\u09af\u09be\u09ac\u09b9\u09be\u09b0 \u0995\u09b0\u09a4\u09c7 \u09b9\u09ac\u09c7\u0964 \u09a4\u09ac\u09c7 CSS3 gradient \u09ac\u09cd\u09af\u09be\u09ac\u09b9\u09be\u09b0 \u0995\u09b0\u09c7 \u0986\u09aa\u09a8\u09bf download \u098f\u09ac\u0982 bandwidth usage \u0995\u09ae\u09be\u09a4\u09c7 \u09aa\u09be\u09b0\u09ac\u09c7\u09a8\u0964 \u098f\u0995\u0987\u09b8\u09be\u09a5\u09c7 gradient \u098f\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7 \u0995\u09b0\u09be element \u0997\u09c1\u09b2\u09bf \u09ad\u09be\u09b2 \u09a6\u09c7\u0996\u09be\u09df \u09af\u0996\u09a8 zoom \u0995\u09b0\u09be \u09b9\u09df, \u0995\u09c7\u09a8\u09a8\u09be gradient \u09b8\u09ac\u09b8\u09ae\u09df\u2026","rel":"","context":"In "\u09b8\u09bf \u098f\u09b8 \u098f\u09b8 \u099f\u09bf\u0989\u099f\u09cb\u09b0\u09bf\u09df\u09be\u09b2 (css tutorial)"","block_context":{"text":"\u09b8\u09bf \u098f\u09b8 \u098f\u09b8 \u099f\u09bf\u0989\u099f\u09cb\u09b0\u09bf\u09df\u09be\u09b2 (css tutorial)","link":"http:\/\/bangla.sitestree.com\/?cat=174"},"img":{"alt_text":"chrome","src":"https:\/\/i0.wp.com\/www.w3schools.com\/images\/compatible_chrome.gif?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":17362,"url":"http:\/\/bangla.sitestree.com\/?p=17362","url_meta":{"origin":24915,"position":2},"title":"Parameter Optimization in\/using Neural Network","author":"Sayed","date":"August 27, 2020","format":false,"excerpt":"Parameter optimization in neural networks https:\/\/www.deeplearning.ai\/ai-notes\/optimization\/ Coding Deep Learning for Beginners \u2014 Linear Regression (Part 2): Cost Function https:\/\/towardsdatascience.com\/coding-deep-learning-for-beginners-linear-regression-part-2-cost-function-49545303d29f CBMM Tutorial: Optimization Notes https:\/\/cbmm.mit.edu\/sites\/default\/files\/documents\/CBMM_Optimization_Notes_2017.html Gradient descent https:\/\/www.jeremyjordan.me\/gradient-descent\/ Hyper-parameter Tuning Techniques in Deep Learning https:\/\/towardsdatascience.com\/hyper-parameter-tuning-techniques-in-deep-learning-4dad592c63c8 Model Parameters and Hyperparameters in Machine Learning \u2014 What is the difference? https:\/\/towardsdatascience.com\/model-parameters-and-hyperparameters-in-machine-learning-what-is-the-difference-702d30970f6 Lecture 8: Optimization\u2026","rel":"","context":"In "\u09ac\u09cd\u09b2\u0997 \u0964 Blog"","block_context":{"text":"\u09ac\u09cd\u09b2\u0997 \u0964 Blog","link":"http:\/\/bangla.sitestree.com\/?cat=182"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":10400,"url":"http:\/\/bangla.sitestree.com\/?p=10400","url_meta":{"origin":24915,"position":3},"title":"Draws a circle with a gradient fill","author":"","date":"August 28, 2015","format":false,"excerpt":"GradientPaintExample.java Draws a circle with a gradient fill. 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