May2018 Jun Tutorials, Overviews
All (73) | Courses, Education (6) | Meetings (5) | News, Features (6) | Opinions, Interviews (20) | Top Stories, Tweets (6) | Tutorials, Overviews (26) | Webcasts & Webinars (4)
- 7 Simple Data Visualizations You Should Know in R
- Jun 22, 2018.
This post presents a selection of 7 essential data visualizations, and how to recreate them using a mix of base R functions and a few common packages.
- Simple Tips for PostgreSQL Query Optimization
- Jun 22, 2018.
A single query optimization tip can boost your database performance by 100x. Although we usually advise our customers to use these tips to optimize analytic queries (such as aggregation ones), this post is still very helpful for any other type of query.
- An Intuitive Introduction to Gradient Descent
- Jun 21, 2018.
This post provides a good introduction to Gradient Descent, covering the intuition, variants and choosing the learning rate.
- Detecting Sarcasm with Deep Convolutional Neural Networks
- Jun 21, 2018.
Detection of sarcasm is important in other areas such as affective computing and sentiment analysis because such expressions can flip the polarity of a sentence.
- Deep Learning Best Practices – Weight Initialization
- Jun 21, 2018.
In this blog I am going to talk about the issues related to initialization of weight matrices and ways to mitigate them. Before that, let’s just cover some basics and notations that we will be using going forward.
- The 5 Clustering Algorithms Data Scientists Need to Know
- Jun 20, 2018.
Today, we’re going to look at 5 popular clustering algorithms that data scientists need to know and their pros and cons!
- Data Science Predicting The Future
- Jun 19, 2018.
In this article we will expand on the knowledge learnt from the last article - The What, Where and How of Data for Data Science - and consider how data science is applied to predict the future.
- Choosing the Right Metric for Evaluating Machine Learning Models — Part 2
- Jun 19, 2018.
This will focus on commonly used metrics in classification, why should we prefer some over others with context.
- Natural Language Processing Nuggets: Getting Started with NLP
- Jun 19, 2018.
Check out this collection of NLP resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.
- Step Forward Feature Selection: A Practical Example in Python
- Jun 18, 2018.
When it comes to disciplined approaches to feature selection, wrapper methods are those which marry the feature selection process to the type of model being built, evaluating feature subsets in order to detect the model performance between features, and subsequently select the best performing subset.
- IoT on AWS: Machine Learning Models and Dashboards from Sensor Data
- Jun 15, 2018.
I developed my first IoT project using my notebook as an IoT device and AWS IoT as infrastructure, with this "simple" idea: collect CPU Temperature from my Notebook running on Ubuntu, send to Amazon AWS IoT, save data, make it available for Machine Learning models and dashboards.
- Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health
- Jun 14, 2018.
After reading this, you’ll be back to fantasies of you + PyTorch eloping into the sunset while your Recurrent Networks achieve new accuracies you’ve only read about on Arxiv.
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Generating Text with RNNs in 4 Lines of Code
- Jun 14, 2018.
Want to generate text with little trouble, and without building and tuning a neural network yourself? Let's check out a project which allows you to "easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code." - How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning
- Jun 13, 2018.
An end-to-end example of how to build a system that can search objects semantically.
- The What, Where and How of Data for Data Science
- Jun 12, 2018.
Here we will take data science apart and build it back up to a coherent and manageable concept. Bear with us!
- A Better Stats 101
- Jun 12, 2018.
Statistics encourages us to think systemically and recognize that variables normally do not operate in isolation, and that an effect usually has multiple causes. Some call this multivariate thinking. Statistics is particularly useful for uncovering the Why.
- 5 Machine Learning Projects You Should Not Overlook, June 2018
- Jun 12, 2018.
Here is a new installment of 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
- Packaging and Distributing Your Python Project to PyPI for Installation Using pip
- Jun 11, 2018.
This tutorial will explain the steps required to package your Python projects, distribute them in distribution formats using steptools, upload them into the Python Package Index (PyPI) repository using twine, and finally installation using Python installers such as pip and conda.
- DIY Deep Learning Projects
- Jun 8, 2018.
Inspired by the great work of Akshay Bahadur in this article you will see some projects applying Computer Vision and Deep Learning, with implementations and details so you can reproduce them on your computer.
- Command Line Tricks For Data Scientists
- Jun 7, 2018.
Aspiring to master the command line should be on every developer’s list, especially data scientists. Learning the ins and outs of your terminal will undeniably make you more productive.
- Audience Segmentation
- Jun 6, 2018.
The process of audience segmentation is not about just statistics, it’s about finding your ideal clients and choosing the right way of interaction with them.
- Introduction to Game Theory (Part 1)
- Jun 6, 2018.
Check out this game theory basics post for an introduction to Two-player Sequential games — Dominant Strategies, Nash Equilibrium, and Cooperation vs. Defection.
- Human Interpretable Machine Learning (Part 1) — The Need and Importance of Model Interpretation
- Jun 6, 2018.
A brief introduction into machine learning model interpretation.
- Three techniques to improve machine learning model performance with imbalanced datasets
- Jun 5, 2018.
The primary objective of this project was to handle data imbalance issue. In the following subsections, I describe three techniques I used to overcome the data imbalance problem.
- The Keras 4 Step Workflow
- Jun 4, 2018.
In his book "Deep Learning with Python," Francois Chollet outlines a process for developing neural networks with Keras in 4 steps. Let's take a look at this process with a simple example.
- Using Linear Regression for Predictive Modeling in R
- Jun 1, 2018.
In this post, we’ll use linear regression to build a model that predicts cherry tree volume from metrics that are much easier for folks who study trees to measure.