Image Classification in R (Using Keras) Recently, I found this amazing tutorial on using Keras on Building powerful image classification models using very little data by Francois Chollet. This is hands-down the best tutorial that I have found on Image Classifcation with Deep learning; however, it’s in Python. Here’s my R implementation with a few tweaks. Setup Download train.zip Image Dataset from Kaggle’s Dog’s vs Cats Competition On your Terminal (Mac Terminal): ## For demonstration purposes I unzip the file from the Downloads to Documents unzip ~/Downloads/train.
Setup Setting up a Jupyter notebook on an AWS ec2 instance to be able to run even when you close your laptop has to be one of the most useful tools ever! However, there’s not enough information online on how to do it. From bits and pieces from all over the internet, here’s how you do it step by step.. Step 1: Setting up SSH Make sure you have an ssh key first.
Playoffs in the NBA just started, and I hear reporters on the news talking about chemistry all the time. However, what exactly is chemistry? What determines if teammates have good chemistry? I decide to google “NBA Chemistry”, and the consensus online is “chemistry”, is actually really hard to define, because it is not quantified yet. So, how do you measure chemistry, when it could mean a million different variables? I decided to take a stab at it by using machine learning methods.
Below is my first exploratory analysis Kernel that I submitted on Kaggle. First Kernel Hello! I plan to look at solved murders. Eventually, I plan to make a prediction on the relationship of the perpetrator to the victim; however, this kernel I will explore perpetrator sex to the victim, the weapon used on the victim import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt pd.
Metis My first week as a student Data Scientist at Metis NYC has been challenging but also, incredible! I had high expectations coming in (after reading so many reviews); however, Metis has exceeded my expectations. Since starting, my routine has been: 6:15 - 8:00 Wake up, Walk Dog, Do some coding 8:00 - 9:00 Take the LIRR to NYC 9:00 - 9:30 Do some code until lectures start 9:30 - 12 Lectures