366 lines
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HTML
366 lines
20 KiB
HTML
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<!-- navigation toc: --> <li><a href="._week40-bs001.html#lecture-monday-september-29-2025" style="font-size: 80%;"><b>Lecture Monday September 29, 2025</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs002.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-bs003.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-bs004.html#logistic-regression-from-last-week" style="font-size: 80%;"><b>Logistic Regression, from last week</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs005.html#classification-problems" style="font-size: 80%;"><b>Classification problems</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs006.html#optimization-and-deep-learning" style="font-size: 80%;"><b>Optimization and Deep learning</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs007.html#basics" style="font-size: 80%;"><b>Basics</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs008.html#two-parameters" style="font-size: 80%;"><b>Two parameters</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs009.html#maximum-likelihood" style="font-size: 80%;"><b>Maximum likelihood</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs010.html#the-cost-function-rewritten" style="font-size: 80%;"><b>The cost function rewritten</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs011.html#minimizing-the-cross-entropy" style="font-size: 80%;"><b>Minimizing the cross entropy</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs012.html#a-more-compact-expression" style="font-size: 80%;"><b>A more compact expression</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs013.html#extending-to-more-predictors" style="font-size: 80%;"><b>Extending to more predictors</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs014.html#including-more-classes" style="font-size: 80%;"><b>Including more classes</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs015.html#more-classes" style="font-size: 80%;"><b>More classes</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs016.html#optimization-the-central-part-of-any-machine-learning-algortithm" style="font-size: 80%;"><b>Optimization, the central part of any Machine Learning algortithm</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs017.html#revisiting-our-logistic-regression-case" style="font-size: 80%;"><b>Revisiting our Logistic Regression case</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs018.html#the-equations-to-solve" style="font-size: 80%;"><b>The equations to solve</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs019.html#solving-using-newton-raphson-s-method" style="font-size: 80%;"><b>Solving using Newton-Raphson's method</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs020.html#example-code-for-logistic-regression" style="font-size: 80%;"><b>Example code for Logistic Regression</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs020.html#synthetic-data-generation" style="font-size: 80%;"> Synthetic data generation</a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs021.html#using-scikit-learn" style="font-size: 80%;"><b>Using <b>Scikit-learn</b></b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs022.html#using-the-correlation-matrix" style="font-size: 80%;"><b>Using the correlation matrix</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs023.html#discussing-the-correlation-data" style="font-size: 80%;"><b>Discussing the correlation data</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs024.html#other-measures-in-classification-studies" style="font-size: 80%;"><b>Other measures in classification studies</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs025.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-bs027.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-bs028.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-bs029.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-bs030.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-bs031.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-bs032.html#multilayer-perceptrons" style="font-size: 80%;"><b>Multilayer perceptrons</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs033.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-bs034.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-bs035.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-bs036.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-bs037.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-bs042.html#mathematical-model" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs042.html#mathematical-model" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs042.html#mathematical-model" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs042.html#mathematical-model" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs042.html#mathematical-model" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs043.html#matrix-vector-notation" style="font-size: 80%;"> Matrix-vector notation</a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs044.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-bs045.html#activation-functions" style="font-size: 80%;"> Activation functions</a></li>
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<!-- navigation toc: --> <li><a href="._week40-bs046.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-bs047.html#relevance" style="font-size: 80%;"> Relevance</a></li>
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</ul>
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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="part0026"></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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$$
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\begin{equation}
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y = f\left(\sum_{i=1}^n w_ix_i\right) = f(u)
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\tag{1}
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\end{equation}
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$$
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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
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categories:
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</p>
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<ol>
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<li> general purpose neural networks for supervised learning,</li>
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<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
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theories, and different phases of polymers, or solving the
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Navier-Stokes equation in weather forecasting. Deep learning has also
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found interesting applications in quantum physics. Various quantum
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phase transitions can be detected and studied using DNNs and CNNs,
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topological phases, and even non-equilibrium many-body
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localization. Representing quantum states as DNNs quantum state
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tomography are among some of the impressive achievements to reveal the
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potential of DNNs to facilitate the study of quantum systems.
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</p>
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<p>In quantum information theory, it has been shown that one can perform
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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
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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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<li><a href="._week40-bs047.html">48</a></li>
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