writing

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Writing

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Writing

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I write about nature, mathematics, philosophy, artificial intelligence, or anything that crosses my mind. Here are a few of my recent favorite articles. For all my writing, visit my Medium

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I write about nature, mathematics, philosophy, artificial intelligence. For all my writing, visit my Medium

[/et_pb_text][/et_pb_column][/et_pb_row][et_pb_row use_custom_gutter=”on” gutter_width=”1″ admin_label=”articles” _builder_version=”4.5.1″ _module_preset=”default” background_enable_color=”off” width=”100%” max_width=”800px” height=”400px” custom_css_main_element=”display:flex;||flex-direction: row;||align-items:center;||height: 100%;” custom_css_main_element_last_edited=”on|desktop” custom_css_main_element_tablet=”display:flex;||flex-direction: column;||align-items:center;||height: 100%;”][et_pb_column type=”4_4″ _builder_version=”4.5.1″ _module_preset=”default”][et_pb_accordion admin_label=”Articles” _builder_version=”4.5.1″ _module_preset=”default” background_color=”rgba(0,0,0,0)” custom_margin=”||4px||false|false” custom_margin_tablet=”” custom_margin_phone=”33px||||false|false” custom_margin_last_edited=”on|phone” hover_enabled=”0″ toggle_font_size_tablet=”” toggle_font_size_phone=”16px” toggle_font_size_last_edited=”on|phone” closed_toggle_font_size_tablet=”” closed_toggle_font_size_phone=”16px” closed_toggle_font_size_last_edited=”on|phone” border_width_all=”0px” border_width_bottom=”3px” border_color_bottom=”#8c8c8c”][et_pb_accordion_item title=”How AI Can Learn from Genetics” _builder_version=”4.5.1″ _module_preset=”default” body_font=”B612 Mono||||||||” body_font_size=”13px” background_color=”rgba(0,0,0,0)” background_enable_color=”on” link_option_url=”https://adamdhalla.medium.com/how-ai-can-learn-from-genetics-b24f31adf494″ link_option_url_new_window=”on” border_radii=”off||||” toggle_font=”B612 Mono||||||||” toggle_font_size=”21px” open=”off”]

Coding an evolutionary search system in Python, and exploring how a solution can be found to the 8-queens problem using techniques that echo genetics and natural selection.

[/et_pb_accordion_item][et_pb_accordion_item title=”The Right Philosophy for our Times” _builder_version=”4.5.1″ _module_preset=”default” body_font=”B612 Mono||||||||” body_font_size=”13px” body_link_text_color=”#000000″ background_color=”rgba(0,0,0,0)” background_enable_color=”on” link_option_url=”https://medium.com/curious/the-right-philosophy-for-our-times-356b723b6ae4″ link_option_url_new_window=”on” border_radii=”off||||” toggle_font=”B612 Mono||||||||” toggle_font_size=”21px” open=”off”]

An introduction to the early 19th-century philosophy of Transcendentalism and why it’s ideas on environmentalism and self-reliance are so integral today. Featured and translated for Greek newspaper Orthos Logos.

[/et_pb_accordion_item][et_pb_accordion_item title=”The Illusion of Intelligence” _builder_version=”4.5.1″ _module_preset=”default” body_font=”B612 Mono||||||||” body_font_size=”13px” background_color=”rgba(0,0,0,0)” background_enable_color=”on” link_option_url=”https://adamdhalla.medium.com/the-illusion-of-intelligence-561ecd3a1f4e” link_option_url_new_window=”on” border_radii=”off||||” toggle_font=”B612 Mono||||||||” toggle_font_size=”21px” open=”off”]

Exploring the AI of the 60’s and 70’s – rule-based systems. How microworlds and a SHRDLU, a robot that could seemingly understand human language, was actually just a fantastic rule-follower. Coding a simple SHRDLU in Python.

[/et_pb_accordion_item][et_pb_accordion_item title=”Logistic Regression on CIFAR-10″ _builder_version=”4.5.1″ _module_preset=”default” body_font=”B612 Mono||||||||” body_font_size=”13px” background_color=”rgba(0,0,0,0)” background_enable_color=”on” link_option_url=”https://medium.com/ai-in-plain-english/complete-guide-image-classification-on-cifar-10-chapter-i-multiclass-logistic-regression-525daee0775b” link_option_url_new_window=”on” border_radii=”off||||” toggle_font=”B612 Mono||||||||” toggle_font_size=”21px” open=”off”]

Using Logistic Regression for an image classification problem in PyTorch, and why it can’t get past 40% accuracy. Explanations on cross-entropy loss, softmax, one-hot encoding, and general logistic regression knowledge.

[/et_pb_accordion_item][et_pb_accordion_item title=”The Calculus of Backpropagation” _builder_version=”4.5.1″ _module_preset=”default” body_font=”B612 Mono||||||||” body_font_size=”13px” background_color=”rgba(0,0,0,0)” background_enable_color=”on” link_option_url=”https://medium.com/ai-in-plain-english/calculus-for-backpropagation-doesnt-have-to-be-scary-16595d76e744″ link_option_url_new_window=”on” border_radii=”off||||” toggle_font=”B612 Mono||||||||” toggle_font_size=”21px” open=”off”]

An introduction of the math behind artificial neural network backpropagation, and the use of the chain rule of calculus in the context of backpropagation.

[/et_pb_accordion_item][et_pb_accordion_item title=”Gradient Descent for Univariate Linear Regression” _builder_version=”4.5.1″ _module_preset=”default” body_font=”B612 Mono||||||||” body_font_size=”13px” background_color=”rgba(0,0,0,0)” background_enable_color=”on” link_option_url=”https://medium.com/swlh/the-math-of-machine-learning-i-gradient-descent-with-univariate-linear-regression-2afbfb556131″ link_option_url_new_window=”on” border_radii=”off||||” toggle_font=”B612 Mono||||||||” toggle_font_size=”21px” open=”off”]

How Gradient Descent works both theoretically and mathematically, with an explanation of gradients from multivariable calculus and why they matter in the context of linear regression. Also, a complete derivation of the update rules for paramaters.

[/et_pb_accordion_item][et_pb_accordion_item title=”Evolution and Artificial Intelligence” _builder_version=”4.5.1″ _module_preset=”default” body_font=”B612 Mono||||||||” body_font_size=”13px” background_color=”rgba(0,0,0,0)” background_enable_color=”on” link_option_url=”https://medium.com/ai-in-plain-english/evolution-is-rife-with-good-metaphors-for-artificial-intelligence-b9887c25e14f” link_option_url_new_window=”on” border_radii=”off||||” toggle_font=”B612 Mono||||||||” toggle_font_size=”21px” open=”off”]

Viewing survival as one big cost function, exploring local minima through dead-end evolution, and comparing and contrasting various parallels of natural selection and artificial intelligence.

[/et_pb_accordion_item][et_pb_accordion_item title=”On Learning Deeply” _builder_version=”4.5.1″ _module_preset=”default” body_font=”B612 Mono||||||||” body_font_size=”13px” background_color=”rgba(0,0,0,0)” background_enable_color=”on” link_option_url=”https://adamdhalla.medium.com/on-learning-deeply-7efd9a4284a8″ link_option_url_new_window=”on” border_radii=”off||||” toggle_font=”B612 Mono||||||||” toggle_font_size=”21px” open=”off”]

An exploration into deep thinking – the benefits, and what it means to truly understand a subject. The war against memorization, taking notes, scheduling.

[/et_pb_accordion_item][et_pb_accordion_item title=”Recursive Least Squares in Python” open=”on” _builder_version=”4.5.1″ _module_preset=”default” body_font=”B612 Mono||||||||” body_font_size=”13px” background_color=”rgba(0,0,0,0)” background_enable_color=”on” link_option_url=”https://python.plainenglish.io/recursive-least-squares-b2407126c257″ link_option_url_new_window=”on” hover_enabled=”0″ border_radii=”off||||” toggle_font=”B612 Mono||||||||” toggle_font_size=”21px”]

How to come up with a more efficient version of the Normal Equations to use with datasets that are continuously increasing. Seeing how this arises from the Sherman-Morrison-Woodbury Formula in Numerical Linear Algebra. Coding in Python.

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