start.ipynb

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    "<p style=\"font-size:30px; text-align:center\"><b>Machine Learning in Finance</b></p>"
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   "source": [
    "<div style=\"text-align: right\">(C) <a href=\"https://github.com/niknow\">Nikolai Nowaczyk</a>, <a href=\"https://github.com/Lapsilago\">Jörg Kienitz</a> 2021</div>"
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   "source": [
    "# Regression"
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   "source": [
    "## [Linear Regression](regression_revisited/regression_revisited.ipynb)\n",
    "A recap of linear regression - a core fundamental of machine learning."
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   "source": [
    "## [Local Regression](local_regression/local_regression.ipynb)\n",
    "Local regression is a refinement of linear regression that adapts the model at each point of the prediction."
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   "source": [
    "## [Gaussian Process Regression](gaussian_process_regression/gaussian_process_regression.ipynb)\n",
    "An advanced regression technique that produces not only predictions, but also confidence bounds around them."
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   "source": [
    "## [Dynamically Controlled Kernel Estimation (DCKE)](dynamically_controlled_kernel_estimation/dynamically_controlled_kernel_estimation.ipynb)\n",
    "A combination of local regression, control variates and Gaussian process regression to estimate conditional expectations. The method is model free, data-driven an particularly suited for financial applications."
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   "source": [
    "# Neural Network Topologies"
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   "source": [
    "## [Multilayer Perceptrons (MLP)](neural_network_intro/neural_network_intro_model_setup.ipynb)\n",
    "Introduction to the most common form of artificial neural networks (ANN)."
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   "source": [
    "## [Long-Term-Short-Term-Memory networks (LSTM)](lstm_intro/lstm_intro.ipynb)\n",
    "Introduction to LSTMs, a popular form of recurrent neural networks (RNNs)."
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   "source": [
    "# Network Training & Optimization Techniques"
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   "source": [
    "## [Newton's Method](newton_gradient_backprop/newton.ipynb)\n",
    "A recap of the Newton's method."
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   "source": [
    "## [Gradient Descent](newton_gradient_backprop/gradient_descent.ipynb)\n",
    "Mathematical foundations and basics of gradient descent."
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   "source": [
    "## [Backpropagation](newton_gradient_backprop/backpropagation.ipynb)\n",
    "Derivation of the backpropagation algorithm."
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   "metadata": {},
   "source": [
    "## [Adjoint Method](newton_gradient_backprop/adjoint.ipynb)\n",
    "Relationship between backpropagation and the adjoint method."
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