Morten Hjorth-Jensen
440 lines
27 KiB
HTML
440 lines
27 KiB
HTML
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<title>Week 40: Gradient descent methods (continued) and start Neural networks</title>
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<!-- tocinfo
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{'highest level': 2,
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'sections': [('Plans for week 40', 2, None, 'plans-for-week-40'),
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('Lecture Monday September 30, 2024',
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2,
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None,
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'lecture-monday-september-30-2024'),
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('Suggested readings and videos',
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2,
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None,
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'suggested-readings-and-videos'),
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('Lab sessions Tuesday and Wednesday',
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2,
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None,
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'lab-sessions-tuesday-and-wednesday'),
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('Summary from last week, using gradient descent methods, '
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'limitations',
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2,
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None,
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'summary-from-last-week-using-gradient-descent-methods-limitations'),
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('Overview video on Stochastic Gradient Descent',
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2,
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None,
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'overview-video-on-stochastic-gradient-descent'),
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('Batches and mini-batches', 2, None, 'batches-and-mini-batches'),
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('Stochastic Gradient Descent (SGD)',
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2,
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None,
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'stochastic-gradient-descent-sgd'),
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('Stochastic Gradient Descent',
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2,
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None,
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'stochastic-gradient-descent'),
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('Computation of gradients', 2, None, 'computation-of-gradients'),
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('SGD example', 2, None, 'sgd-example'),
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('The gradient step', 2, None, 'the-gradient-step'),
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('Simple example code', 2, None, 'simple-example-code'),
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('When do we stop?', 2, None, 'when-do-we-stop'),
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('Slightly different approach',
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2,
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None,
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'slightly-different-approach'),
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('Time decay rate', 2, None, 'time-decay-rate'),
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('Code with a Number of Minibatches which varies',
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2,
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None,
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'code-with-a-number-of-minibatches-which-varies'),
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('Replace or not', 2, None, 'replace-or-not'),
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('Momentum based GD', 2, None, 'momentum-based-gd'),
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('More on momentum based approaches',
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2,
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None,
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'more-on-momentum-based-approaches'),
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('Momentum parameter', 2, None, 'momentum-parameter'),
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('Second moment of the gradient',
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2,
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None,
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'second-moment-of-the-gradient'),
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('RMS prop', 2, None, 'rms-prop'),
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('"ADAM optimizer":"https://arxiv.org/abs/1412.6980"',
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2,
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None,
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'adam-optimizer-https-arxiv-org-abs-1412-6980'),
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('Algorithms and codes for Adagrad, RMSprop and Adam',
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2,
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None,
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'algorithms-and-codes-for-adagrad-rmsprop-and-adam'),
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('Practical tips', 2, None, 'practical-tips'),
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('Automatic differentiation',
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2,
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None,
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'automatic-differentiation'),
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('Using autograd', 2, None, 'using-autograd'),
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('Autograd with more complicated functions',
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2,
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None,
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'autograd-with-more-complicated-functions'),
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('More complicated functions using the elements of their '
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'arguments directly',
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2,
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None,
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'more-complicated-functions-using-the-elements-of-their-arguments-directly'),
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('Functions using mathematical functions from Numpy',
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2,
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None,
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'functions-using-mathematical-functions-from-numpy'),
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('More autograd', 2, None, 'more-autograd'),
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('And with loops', 2, None, 'and-with-loops'),
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('Using recursion', 2, None, 'using-recursion'),
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('Unsupported functions', 2, None, 'unsupported-functions'),
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('The syntax a.dot(b) when finding the dot product',
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2,
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None,
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'the-syntax-a-dot-b-when-finding-the-dot-product'),
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('Recommended to avoid', 2, None, 'recommended-to-avoid'),
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('Using Autograd with OLS', 2, None, 'using-autograd-with-ols'),
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('Same code but now with momentum gradient descent',
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2,
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None,
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'same-code-but-now-with-momentum-gradient-descent'),
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("But noen of these can compete with Newton's method",
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2,
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None,
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'but-noen-of-these-can-compete-with-newton-s-method'),
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('Including Stochastic Gradient Descent with Autograd',
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2,
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None,
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'including-stochastic-gradient-descent-with-autograd'),
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('Same code but now with momentum gradient descent',
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2,
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None,
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'same-code-but-now-with-momentum-gradient-descent'),
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('Similar (second order function now) problem but now with '
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'AdaGrad',
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2,
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None,
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'similar-second-order-function-now-problem-but-now-with-adagrad'),
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('RMSprop for adaptive learning rate with Stochastic Gradient '
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'Descent',
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2,
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None,
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'rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent'),
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('And finally "ADAM":"https://arxiv.org/pdf/1412.6980.pdf"',
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2,
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None,
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'and-finally-adam-https-arxiv-org-pdf-1412-6980-pdf'),
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('And Logistic Regression', 2, None, 'and-logistic-regression'),
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('Introducing "JAX":"https://jax.readthedocs.io/en/latest/"',
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2,
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None,
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'introducing-jax-https-jax-readthedocs-io-en-latest'),
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('Introduction to Neural networks',
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2,
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None,
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'introduction-to-neural-networks'),
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('Artificial neurons', 2, None, 'artificial-neurons'),
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('Neural network types', 2, None, 'neural-network-types'),
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('Feed-forward neural networks',
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2,
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None,
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'feed-forward-neural-networks'),
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('Convolutional Neural Network',
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2,
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None,
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'convolutional-neural-network'),
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('Recurrent neural networks',
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2,
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None,
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'recurrent-neural-networks'),
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('Other types of networks', 2, None, 'other-types-of-networks'),
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('Multilayer perceptrons', 2, None, 'multilayer-perceptrons'),
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('Why multilayer perceptrons?',
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2,
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None,
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'why-multilayer-perceptrons'),
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('Illustration of a single perceptron model and a '
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'multi-perceptron model',
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2,
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None,
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'illustration-of-a-single-perceptron-model-and-a-multi-perceptron-model'),
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('Examples of XOR, OR and AND gates',
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2,
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None,
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'examples-of-xor-or-and-and-gates'),
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('Does Logistic Regression do a better Job?',
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2,
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None,
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'does-logistic-regression-do-a-better-job'),
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('Adding Neural Networks', 2, None, 'adding-neural-networks'),
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('Mathematical model', 2, None, 'mathematical-model'),
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('Mathematical model', 2, None, 'mathematical-model'),
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('Mathematical model', 2, None, 'mathematical-model'),
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('Mathematical model', 2, None, 'mathematical-model'),
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('Mathematical model', 2, None, 'mathematical-model'),
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('Matrix-vector notation', 3, None, 'matrix-vector-notation'),
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|
('Matrix-vector notation and activation',
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3,
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None,
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'matrix-vector-notation-and-activation'),
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('Activation functions', 3, None, 'activation-functions'),
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('Activation functions, Logistic and Hyperbolic ones',
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3,
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None,
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'activation-functions-logistic-and-hyperbolic-ones'),
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('Relevance', 3, None, 'relevance')]}
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end of tocinfo -->
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<a class="navbar-brand" href="week40-bs.html">Week 40: Gradient descent methods (continued) and start Neural networks</a>
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</div>
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<ul class="nav navbar-nav navbar-right">
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
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<ul class="dropdown-menu">
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<!-- navigation toc: --> <li><a href="._week40-bs001.html#plans-for-week-40" style="font-size: 80%;"><b>Plans for week 40</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs002.html#lecture-monday-september-30-2024" style="font-size: 80%;"><b>Lecture Monday September 30, 2024</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs003.html#suggested-readings-and-videos" style="font-size: 80%;"><b>Suggested readings and videos</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs004.html#lab-sessions-tuesday-and-wednesday" style="font-size: 80%;"><b>Lab sessions Tuesday and Wednesday</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs005.html#summary-from-last-week-using-gradient-descent-methods-limitations" style="font-size: 80%;"><b>Summary from last week, using gradient descent methods, limitations</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs006.html#overview-video-on-stochastic-gradient-descent" style="font-size: 80%;"><b>Overview video on Stochastic Gradient Descent</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs007.html#batches-and-mini-batches" style="font-size: 80%;"><b>Batches and mini-batches</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs008.html#stochastic-gradient-descent-sgd" style="font-size: 80%;"><b>Stochastic Gradient Descent (SGD)</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs009.html#stochastic-gradient-descent" style="font-size: 80%;"><b>Stochastic Gradient Descent</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs010.html#computation-of-gradients" style="font-size: 80%;"><b>Computation of gradients</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs011.html#sgd-example" style="font-size: 80%;"><b>SGD example</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs012.html#the-gradient-step" style="font-size: 80%;"><b>The gradient step</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs013.html#simple-example-code" style="font-size: 80%;"><b>Simple example code</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs014.html#when-do-we-stop" style="font-size: 80%;"><b>When do we stop?</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs015.html#slightly-different-approach" style="font-size: 80%;"><b>Slightly different approach</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs016.html#time-decay-rate" style="font-size: 80%;"><b>Time decay rate</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs017.html#code-with-a-number-of-minibatches-which-varies" style="font-size: 80%;"><b>Code with a Number of Minibatches which varies</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs018.html#replace-or-not" style="font-size: 80%;"><b>Replace or not</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs019.html#momentum-based-gd" style="font-size: 80%;"><b>Momentum based GD</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs020.html#more-on-momentum-based-approaches" style="font-size: 80%;"><b>More on momentum based approaches</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs021.html#momentum-parameter" style="font-size: 80%;"><b>Momentum parameter</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs022.html#second-moment-of-the-gradient" style="font-size: 80%;"><b>Second moment of the gradient</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs023.html#rms-prop" style="font-size: 80%;"><b>RMS prop</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs024.html#adam-optimizer-https-arxiv-org-abs-1412-6980" style="font-size: 80%;"><b>"ADAM optimizer":"https://arxiv.org/abs/1412.6980"</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs025.html#algorithms-and-codes-for-adagrad-rmsprop-and-adam" style="font-size: 80%;"><b>Algorithms and codes for Adagrad, RMSprop and Adam</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs026.html#practical-tips" style="font-size: 80%;"><b>Practical tips</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs027.html#automatic-differentiation" style="font-size: 80%;"><b>Automatic differentiation</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs028.html#using-autograd" style="font-size: 80%;"><b>Using autograd</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs029.html#autograd-with-more-complicated-functions" style="font-size: 80%;"><b>Autograd with more complicated functions</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs030.html#more-complicated-functions-using-the-elements-of-their-arguments-directly" style="font-size: 80%;"><b>More complicated functions using the elements of their arguments directly</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs031.html#functions-using-mathematical-functions-from-numpy" style="font-size: 80%;"><b>Functions using mathematical functions from Numpy</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs032.html#more-autograd" style="font-size: 80%;"><b>More autograd</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs033.html#and-with-loops" style="font-size: 80%;"><b>And with loops</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs034.html#using-recursion" style="font-size: 80%;"><b>Using recursion</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs035.html#unsupported-functions" style="font-size: 80%;"><b>Unsupported functions</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs036.html#the-syntax-a-dot-b-when-finding-the-dot-product" style="font-size: 80%;"><b>The syntax a.dot(b) when finding the dot product</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs037.html#recommended-to-avoid" style="font-size: 80%;"><b>Recommended to avoid</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs038.html#using-autograd-with-ols" style="font-size: 80%;"><b>Using Autograd with OLS</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs042.html#same-code-but-now-with-momentum-gradient-descent" style="font-size: 80%;"><b>Same code but now with momentum gradient descent</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs040.html#but-noen-of-these-can-compete-with-newton-s-method" style="font-size: 80%;"><b>But noen of these can compete with Newton's method</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs041.html#including-stochastic-gradient-descent-with-autograd" style="font-size: 80%;"><b>Including Stochastic Gradient Descent with Autograd</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs042.html#same-code-but-now-with-momentum-gradient-descent" style="font-size: 80%;"><b>Same code but now with momentum gradient descent</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs043.html#similar-second-order-function-now-problem-but-now-with-adagrad" style="font-size: 80%;"><b>Similar (second order function now) problem but now with AdaGrad</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs044.html#rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent" style="font-size: 80%;"><b>RMSprop for adaptive learning rate with Stochastic Gradient Descent</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs045.html#and-finally-adam-https-arxiv-org-pdf-1412-6980-pdf" style="font-size: 80%;"><b>And finally "ADAM":"https://arxiv.org/pdf/1412.6980.pdf"</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs046.html#and-logistic-regression" style="font-size: 80%;"><b>And Logistic Regression</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs047.html#introducing-jax-https-jax-readthedocs-io-en-latest" style="font-size: 80%;"><b>Introducing "JAX":"https://jax.readthedocs.io/en/latest/"</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs048.html#introduction-to-neural-networks" style="font-size: 80%;"><b>Introduction to Neural networks</b></a></li>
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<!-- navigation toc: --> <li><a href="#artificial-neurons" style="font-size: 80%;"><b>Artificial neurons</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs050.html#neural-network-types" style="font-size: 80%;"><b>Neural network types</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs051.html#feed-forward-neural-networks" style="font-size: 80%;"><b>Feed-forward neural networks</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs052.html#convolutional-neural-network" style="font-size: 80%;"><b>Convolutional Neural Network</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs053.html#recurrent-neural-networks" style="font-size: 80%;"><b>Recurrent neural networks</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs054.html#other-types-of-networks" style="font-size: 80%;"><b>Other types of networks</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs055.html#multilayer-perceptrons" style="font-size: 80%;"><b>Multilayer perceptrons</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs056.html#why-multilayer-perceptrons" style="font-size: 80%;"><b>Why multilayer perceptrons?</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs057.html#illustration-of-a-single-perceptron-model-and-a-multi-perceptron-model" style="font-size: 80%;"><b>Illustration of a single perceptron model and a multi-perceptron model</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs058.html#examples-of-xor-or-and-and-gates" style="font-size: 80%;"><b>Examples of XOR, OR and AND gates</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs059.html#does-logistic-regression-do-a-better-job" style="font-size: 80%;"><b>Does Logistic Regression do a better Job?</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs060.html#adding-neural-networks" style="font-size: 80%;"><b>Adding Neural Networks</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs065.html#mathematical-model" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs065.html#mathematical-model" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs065.html#mathematical-model" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs065.html#mathematical-model" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs065.html#mathematical-model" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs066.html#matrix-vector-notation" style="font-size: 80%;"> Matrix-vector notation</a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs067.html#matrix-vector-notation-and-activation" style="font-size: 80%;"> Matrix-vector notation and activation</a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs068.html#activation-functions" style="font-size: 80%;"> Activation functions</a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs069.html#activation-functions-logistic-and-hyperbolic-ones" style="font-size: 80%;"> Activation functions, Logistic and Hyperbolic ones</a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs070.html#relevance" style="font-size: 80%;"> Relevance</a></li>
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</li>
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</ul>
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</div>
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</div>
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</div> <!-- end of navigation bar -->
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<div class="container">
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<p> </p><p> </p><p> </p> <!-- add vertical space -->
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<a name="part0049"></a>
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<!-- !split -->
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<h2 id="artificial-neurons" class="anchor">Artificial neurons </h2>
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<p>The field of artificial neural networks has a long history of
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development, and is closely connected with the advancement of computer
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science and computers in general. A model of artificial neurons was
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first developed by McCulloch and Pitts in 1943 to study signal
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processing in the brain and has later been refined by others. The
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general idea is to mimic neural networks in the human brain, which is
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composed of billions of neurons that communicate with each other by
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sending electrical signals. Each neuron accumulates its incoming
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signals, which must exceed an activation threshold to yield an
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output. If the threshold is not overcome, the neuron remains inactive,
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i.e. has zero output.
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</p>
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<p>This behaviour has inspired a simple mathematical model for an artificial neuron.</p>
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$$
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\begin{equation}
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y = f\left(\sum_{i=1}^n w_ix_i\right) = f(u)
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\tag{6}
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\end{equation}
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$$
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<p>Here, the output \( y \) of the neuron is the value of its activation function, which have as input
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a weighted sum of signals \( x_i, \dots ,x_n \) received by \( n \) other neurons.
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</p>
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<p>Conceptually, it is helpful to divide neural networks into four
|
|
categories:
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|
</p>
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<ol>
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|
<li> general purpose neural networks for supervised learning,</li>
|
|
<li> neural networks designed specifically for image processing, the most prominent example of this class being Convolutional Neural Networks (CNNs),</li>
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|
<li> neural networks for sequential data such as Recurrent Neural Networks (RNNs), and</li>
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|
<li> neural networks for unsupervised learning such as Deep Boltzmann Machines.</li>
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</ol>
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<p>In natural science, DNNs and CNNs have already found numerous
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applications. In statistical physics, they have been applied to detect
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phase transitions in 2D Ising and Potts models, lattice gauge
|
|
theories, and different phases of polymers, or solving the
|
|
Navier-Stokes equation in weather forecasting. Deep learning has also
|
|
found interesting applications in quantum physics. Various quantum
|
|
phase transitions can be detected and studied using DNNs and CNNs,
|
|
topological phases, and even non-equilibrium many-body
|
|
localization. Representing quantum states as DNNs quantum state
|
|
tomography are among some of the impressive achievements to reveal the
|
|
potential of DNNs to facilitate the study of quantum systems.
|
|
</p>
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|
|
|
<p>In quantum information theory, it has been shown that one can perform
|
|
gate decompositions with the help of neural.
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|
</p>
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|
|
|
<p>The applications are not limited to the natural sciences. There is a
|
|
plethora of applications in essentially all disciplines, from the
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humanities to life science and medicine.
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|
</p>
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<p>
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