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10 Best Beer Gardens In Oxford

7 minute read

Published: August 07, 2018

If you are passing by Oxford as a tourist, a visiting student or have already been living there for a while, you cannot miss the enthralling excitement happening around the many bars and pubs scattered in the city. There are especially a significant selection of front and back gardens which offer the perfect place to drink a pint or a glass of Pimm's. I hope you'll love my hand-curated list of the most thrilling beer gardens one can find in The City of Dreaming Spires!

A Minimal Deep Learning Library Implementation In Python

5 minute read

Published: August 10, 2018

$ \newcommand{\R}{\mathbb{R}} \newcommand{\N}{\mathcal{N}} \newcommand{\svert}{~|~} \newcommand{\f}{\mathbf{f}} \newcommand{\x}{\mathbf{x}} \newcommand{\y}{\mathbf{y}} \newcommand{\z}{\mathbf{z}} \newcommand{\w}{\mathbf{w}} \newcommand{\W}{\mathbf{W}} \newcommand{\ba}{\mathbf{a}} \newcommand{\m}{\mathbf{m}} \newcommand{\ls}{\mathbf{l}} \newcommand{\bL}{\mathbf{L}} \newcommand{\X}{\mathbf{X}} \newcommand{\Y}{\mathbf{Y}} \newcommand{\p}{\mathbf{p}} \newcommand{\bepsilon}{\text{$\epsilon$}} \newcommand{\bgamma}{\text{$\gamma$}} \newcommand{\K}{\mathbf{K}} \newcommand{\diag}{\text{diag}} \newcommand{\argmin}{\text{argmin}} $

This tutorial introduces the main blocks needed to build a deep learning library in a few lines of Python. You'll learn how to build your (very) light-weight version of PyTorch!

A Minimal Deep Learning Library Implementation In Python

5 minute read

Published: August 10, 2018

$ \newcommand{\R}{\mathbb{R}} \newcommand{\N}{\mathcal{N}} \newcommand{\svert}{~|~} \newcommand{\f}{\mathbf{f}} \newcommand{\x}{\mathbf{x}} \newcommand{\y}{\mathbf{y}} \newcommand{\z}{\mathbf{z}} \newcommand{\w}{\mathbf{w}} \newcommand{\W}{\mathbf{W}} \newcommand{\ba}{\mathbf{a}} \newcommand{\m}{\mathbf{m}} \newcommand{\ls}{\mathbf{l}} \newcommand{\bL}{\mathbf{L}} \newcommand{\X}{\mathbf{X}} \newcommand{\Y}{\mathbf{Y}} \newcommand{\p}{\mathbf{p}} \newcommand{\bepsilon}{\text{$\epsilon$}} \newcommand{\bgamma}{\text{$\gamma$}} \newcommand{\K}{\mathbf{K}} \newcommand{\diag}{\text{diag}} \newcommand{\argmin}{\text{argmin}} $

This tutorial introduces the main blocks needed to build a deep learning library in a few lines of Python. You'll learn how to build your (very) light-weight version of PyTorch!

10 Best Beer Gardens In Oxford

7 minute read

Published: August 07, 2018

If you are passing by Oxford as a tourist, a visiting student or have already been living there for a while, you cannot miss the enthralling excitement happening around the many bars and pubs scattered in the city. There are especially a significant selection of front and back gardens which offer the perfect place to drink a pint or a glass of Pimm's. I hope you'll love my hand-curated list of the most thrilling beer gardens one can find in The City of Dreaming Spires!

10 Best Beer Gardens In Oxford

7 minute read

Published: August 07, 2018

If you are passing by Oxford as a tourist, a visiting student or have already been living there for a while, you cannot miss the enthralling excitement happening around the many bars and pubs scattered in the city. There are especially a significant selection of front and back gardens which offer the perfect place to drink a pint or a glass of Pimm's. I hope you'll love my hand-curated list of the most thrilling beer gardens one can find in The City of Dreaming Spires!

A Minimal Deep Learning Library Implementation In Python

5 minute read

Published: August 10, 2018

$ \newcommand{\R}{\mathbb{R}} \newcommand{\N}{\mathcal{N}} \newcommand{\svert}{~|~} \newcommand{\f}{\mathbf{f}} \newcommand{\x}{\mathbf{x}} \newcommand{\y}{\mathbf{y}} \newcommand{\z}{\mathbf{z}} \newcommand{\w}{\mathbf{w}} \newcommand{\W}{\mathbf{W}} \newcommand{\ba}{\mathbf{a}} \newcommand{\m}{\mathbf{m}} \newcommand{\ls}{\mathbf{l}} \newcommand{\bL}{\mathbf{L}} \newcommand{\X}{\mathbf{X}} \newcommand{\Y}{\mathbf{Y}} \newcommand{\p}{\mathbf{p}} \newcommand{\bepsilon}{\text{$\epsilon$}} \newcommand{\bgamma}{\text{$\gamma$}} \newcommand{\K}{\mathbf{K}} \newcommand{\diag}{\text{diag}} \newcommand{\argmin}{\text{argmin}} $

This tutorial introduces the main blocks needed to build a deep learning library in a few lines of Python. You'll learn how to build your (very) light-weight version of PyTorch!

An Efficient Soft-Margin Kernel SVM Implementation In Python

9 minute read

Published: August 08, 2018

$ \newcommand{\R}{\mathbb{R}} \newcommand{\N}{\mathcal{N}} \newcommand{\svert}{~|~} \newcommand{\f}{\mathbf{f}} \newcommand{\x}{\mathbf{x}} \newcommand{\y}{\mathbf{y}} \newcommand{\z}{\mathbf{z}} \newcommand{\w}{\mathbf{w}} \newcommand{\W}{\mathbf{W}} \newcommand{\ba}{\mathbf{a}} \newcommand{\m}{\mathbf{m}} \newcommand{\ls}{\mathbf{l}} \newcommand{\bL}{\mathbf{L}} \newcommand{\X}{\mathbf{X}} \newcommand{\Y}{\mathbf{Y}} \newcommand{\p}{\mathbf{p}} \newcommand{\bepsilon}{\text{$\epsilon$}} \newcommand{\bgamma}{\text{$\gamma$}} \newcommand{\K}{\mathbf{K}} \newcommand{\diag}{\text{diag}} \newcommand{\argmin}{\text{argmin}} $

This short tutorial aims at introducing support vector machine (SVM) methods from its mathematical formulation along with an efficient implementation in a few lines of Python! Do play with the full code hosted on my github page. I strongly recommend reading Support Vector Machine Solvers (from L. Bottou & C-J. Lin) for an in-depth cover of the topic, along with the LIBSVM library. The present post naturally follows this introduction on SVMs.

An Efficient Soft-Margin Kernel SVM Implementation In Python

9 minute read

Published: August 08, 2018

$ \newcommand{\R}{\mathbb{R}} \newcommand{\N}{\mathcal{N}} \newcommand{\svert}{~|~} \newcommand{\f}{\mathbf{f}} \newcommand{\x}{\mathbf{x}} \newcommand{\y}{\mathbf{y}} \newcommand{\z}{\mathbf{z}} \newcommand{\w}{\mathbf{w}} \newcommand{\W}{\mathbf{W}} \newcommand{\ba}{\mathbf{a}} \newcommand{\m}{\mathbf{m}} \newcommand{\ls}{\mathbf{l}} \newcommand{\bL}{\mathbf{L}} \newcommand{\X}{\mathbf{X}} \newcommand{\Y}{\mathbf{Y}} \newcommand{\p}{\mathbf{p}} \newcommand{\bepsilon}{\text{$\epsilon$}} \newcommand{\bgamma}{\text{$\gamma$}} \newcommand{\K}{\mathbf{K}} \newcommand{\diag}{\text{diag}} \newcommand{\argmin}{\text{argmin}} $

This short tutorial aims at introducing support vector machine (SVM) methods from its mathematical formulation along with an efficient implementation in a few lines of Python! Do play with the full code hosted on my github page. I strongly recommend reading Support Vector Machine Solvers (from L. Bottou & C-J. Lin) for an in-depth cover of the topic, along with the LIBSVM library. The present post naturally follows this introduction on SVMs.

An Efficient Soft-Margin Kernel SVM Implementation In Python

9 minute read

Published: August 08, 2018

$ \newcommand{\R}{\mathbb{R}} \newcommand{\N}{\mathcal{N}} \newcommand{\svert}{~|~} \newcommand{\f}{\mathbf{f}} \newcommand{\x}{\mathbf{x}} \newcommand{\y}{\mathbf{y}} \newcommand{\z}{\mathbf{z}} \newcommand{\w}{\mathbf{w}} \newcommand{\W}{\mathbf{W}} \newcommand{\ba}{\mathbf{a}} \newcommand{\m}{\mathbf{m}} \newcommand{\ls}{\mathbf{l}} \newcommand{\bL}{\mathbf{L}} \newcommand{\X}{\mathbf{X}} \newcommand{\Y}{\mathbf{Y}} \newcommand{\p}{\mathbf{p}} \newcommand{\bepsilon}{\text{$\epsilon$}} \newcommand{\bgamma}{\text{$\gamma$}} \newcommand{\K}{\mathbf{K}} \newcommand{\diag}{\text{diag}} \newcommand{\argmin}{\text{argmin}} $

This short tutorial aims at introducing support vector machine (SVM) methods from its mathematical formulation along with an efficient implementation in a few lines of Python! Do play with the full code hosted on my github page. I strongly recommend reading Support Vector Machine Solvers (from L. Bottou & C-J. Lin) for an in-depth cover of the topic, along with the LIBSVM library. The present post naturally follows this introduction on SVMs.