## The Birth of Venus: Building a Deep Learning Computer From Scratch

In which I describe how I built a deep learning computer starting from nothing but a pile of hardware components and a dream.

In which I describe how I built a deep learning computer starting from nothing but a pile of hardware components and a dream.

I provide an aggregated and comprehensive list of tutorials I have made for fundamental concepts in machine learning.

I discuss decision trees which are very powerful, general-purpose models that are also interpretable.

I discuss market basket analysis, an unsupervised learning technique for understanding and quantifying the relationships between sets of items.

I discuss the k-nearest neighbors algorithm, a remarkably simple but effective machine learning model.

In which I describe commonly used techniques for evaluating machine learning models.

In which I discuss the technique of ensembling which is used to improve the performance of a single machine learning model by combining the power of several other models.

I discuss strategies such as cross-validation which are used for selecting best-performing machine learning models.

In which I discuss regularization, a strategy for controlling a model's generalizability to new datasets.

In which I give a primer on principal components analysis, a commonly used technique for dimensionality reduction in machine learning.

In which I discuss feature selection which is used in machine learning to help improve model generalization, reduce feature dimensionality, and do other useful things.

In which I describe the bias-variance tradeoff, one of the most important concepts underlying all of machine learning theory.

In which we investigate K-means clustering, a common unsupervised clustering technique for analyzing data.

I describe recurrent neural networks, a deep learning technique geared for tasks such as natural language processing.

We discuss convolutional neural networks, a deep learning model inspired by the human visual system that has rocked the state-of-the-art in computer vision tasks.

We investigate a motley of interesting properties and goodies of neural networks.

We begin a deep dive into deep learning by investigating feedforward neural networks.

We discuss support vector machines, a very powerful and versatile machine learning model.

I describe Naive Bayes, a commonly-used generative model for a variety of classification tasks.

I describe logistic-regression, one of the cornerstone algorithms of the modern-day machine learning toolkit.

I describe the basics of linear regression, one of the most common and widely used machine learning techniques.

I dive into the details of Fast R-CNN, an extension to the original R-CNN model that boasted 9x speedup over its predecessor as well as state-of-the-art object detection results.

A discussion of R-CNN, a historic object detection architecture combining region proposals with convolutional neural networks.

A discussion of fundamental deep learning algorithms people new to the field should learn along with a recommended course of study.

Given all the recent buzz around artificial intelligence, I discuss three reasons for why we are seeing such widespread interest in the field today.

A discussion of the most important skills necessary for being an effective machine learning engineer or data scientist.