As each year ends, I try to pause and reflect on some of the year’s themes and how poorly I predicted them. The goal of this exercise is to relearn some of the lessons we relearn every year: Mainly that nobody, including myself, has any idea how macroeconomic and sociopolitical events will play out, nor how they will affect the markets. The best we can do is predict that past inertia will continue into the future – e.g. the stock market will return about 7.0%. We know this is an illusion since, within any chaotic, complex system, inertia is ephemeral at best, but it’s a nice illusion.
This will be the first post in a series of posts on some advanced Python features. Future topics will include:
- Context Managers
This post continues our series on Berkshire Hathaway’s performance from 1977 to today. Shareholder letters can be found here.
Last time, we explored how a Convolutional Neural Network could be trained to recognize and classify patterns in an image. With a slight modification, a CNN could also be trained to generate new images. But what if we were given a series of frames in an animation and wanted our CNN to predict the next frame? We could feed it a bunch of two frame pairs and see if it could learn that after frame ‘a’ usually came frame ‘b’ but this wouldn’t work that great.
What we really need is a neural network that is able to learn from longer sequences of data. For example, if all the previous frames show a ball flying in an arc, the neural network might be able to lean how quickly the ball is moving in each subsequent time period and make a prediction on the next frame based off that. This is where Recurrent Neural Networks (RNN) come in.
Today, we’ll be conceptualizing and exploring RNN’s by building a deep neural network that functions as part of an end-to-end machine translation pipeline. Our completed pipeline will accept English text as input and return the French translation as output. You can follow along with the code here.
In 1962, Warren Buffett began purchasing shares of Berkshire Hathaway – a downsizing textile manufacturing company. In 1965, after feeling slighted by management, Buffett acquired control of Berkshire. Shortly after, Berkshire purchased an insurance company and began using its float to fund investments and acquisitions. Using this as a launching pad, Berkshire’s share price rose from $8 in 1962 to $276,800 in 2017 (20% annualized).
This post continues our series on that performance. Our goal is to gain some insight into one of the most successful investment vehicles in history. Warren Buffett’s shareholder letters can be found here.
Links to Berkshire’s past years: 1977
Last time, we explored how a simple MLP neural network could be used to classify the MNIST dataset. Today, we will work on a messier problem. We will use a modified version of the Stanford dogs dataset to train a neural network that can classify dog breeds. Since inter-class variations are small, and an obscure detail could be the deciding factor, we will need a model that can capture more detail. This is where convolutional neural networks (CNN) come in.
As always, we will start by explaining some of the high-level concepts. You can follow along with the code here.
In 1962, Warren Buffett began purchasing shares of Berkshire Hathaway – a textile manufacturing company in the process of downsizing. In 1965, after feeling slighted by management, Buffett acquired control of Berkshire. In 1967, Berkshire purchased an insurance company and began using its float to fund investments. Through acquisitions and investments, Berkshire’s share prices would appreciate from $8 in 1962 to $276,800 in 2017 (20% annualized).
This post begins my series on Berkshire Hathaway. The goal is to gain some insight into one of the most successful companies in modern history. We will be looking at Warren Buffett’s shareholder letters which can be found here.
The resiliency of the economic expansion continued throughout the third quarter. GDP growth remained steady, unemployment remained low, the US dollar weakened, and the Fed remained on track for its second interest rate hike of the year. On the other hand, geopolitical concerns increased, the chance of tax reform decreased, and market valuations rose to a level that leaves little room for error.
As always, I have no idea what the near-term future will bring. This post is merely an attempt to see if we are closer to the top or the bottom of the economic cycle.
In this post, we will attempt to conceptualize Deep Neural Networks (DNN) and apply one to a common problem. We’ll train a version of a DNN called a Multilayer Perceptron (or vanilla network) to classify images from the MNIST database. The MNIST database contains 70,000 handwritten digits from 0-9 and is one of the most famous datasets in machine learning. If all this sounds confusing so far, don’t worry we’ll start at the beginning.
If you want to follow along with the code, the notebook can be found here.
Now Inc. (NYSE: DNOW) hit $11.85 today and I began to build a position. This document contains an independent analysis of the company. Information on my investment philosophy can be found here.