2019 Nov Tutorials, Overviews
All (89) | Courses, Education (1) | Meetings (1) | News (6) | Opinions (21) | Top Stories, Tweets (9) | Tutorials, Overviews (45) | Webcasts & Webinars (6)
- Markov Chains: How to Train Text Generation to Write Like George R. R. Martin - Nov 29, 2019.
Read this article on training Markov chains to generate George R. R. Martin style text.
- Lit BERT: NLP Transfer Learning In 3 Steps - Nov 29, 2019.
PyTorch Lightning is a lightweight framework which allows anyone using PyTorch to scale deep learning code easily while making it reproducible. In this tutorial we’ll use Huggingface's implementation of BERT to do a finetuning task in Lightning.
- Getting Started with Automated Text Summarization - Nov 28, 2019.
This article will walk through an extractive text summarization process, using a simple word frequency approach, implemented in Python.
- The Future of Careers in Data Science & Analysis - Nov 27, 2019.
As the fields of data science and analysis continue to expand, the next crop of bright minds is always needed. Learn more about the nuances of these jobs and find where you can fit in for a rewarding and interesting career.
- Spark NLP 101: LightPipeline - Nov 27, 2019.
A Pipeline is specified as a sequence of stages, and each stage is either a Transformer or an Estimator. These stages are run in order, and the input DataFrame is transformed as it passes through each stage. Now let’s see how this can be done in Spark NLP using Annotators and Transformers.
- Machine Learning 101: The What, Why, and How of Weighting - Nov 26, 2019.
Weighting is a technique for improving models. In this article, learn more about what weighting is, why you should (and shouldn’t) use it, and how to choose optimal weights to minimize business costs.
- Content-based Recommender Using Natural Language Processing (NLP) - Nov 26, 2019.
A guide to build a content-based movie recommender model based on NLP.
- Probability Learning: Naive Bayes - Nov 26, 2019.
This post will describe various simplifications of Bayes' Theorem, that make it more practical and applicable to real world problems: these simplifications are known by the name of Naive Bayes. Also, to clarify everything we will see a very illustrative example of how Naive Bayes can be applied for classification.
- Automated Machine Learning Project Implementation Complexities - Nov 22, 2019.
To demonstrate the implementation complexity differences along the AutoML highway, let's have a look at how 3 specific software projects approach the implementation of just such an AutoML "solution," namely Keras Tuner, AutoKeras, and automl-gs.
- Text Encoding: A Review - Nov 22, 2019.
We will focus here exactly on that part of the analysis that transforms words into numbers and texts into number vectors: text encoding.
- Python, Selenium & Google for Geocoding Automation: Free and Paid - Nov 21, 2019.
This tutorial will take you through two options that have automated the geocoding process for the user using Python, Selenium and Google Geocoding API.
- Neural Networks 201: All About Autoencoders - Nov 21, 2019.
Autoencoders can be a very powerful tool for leveraging unlabeled data to solve a variety of problems, such as learning a "feature extractor" that helps build powerful classifiers, finding anomalies, or doing a Missing Value Imputation.
- Python Tuples and Tuple Methods - Nov 21, 2019.
Brush up on your Python basics with this post on creating, using, and manipulating tuples.
- Pro Tips: How to deal with Class Imbalance and Missing Labels - Nov 20, 2019.
Your spectacularly-performing machine learning model could be subject to the common culprits of class imbalance and missing labels. Learn how to handle these challenges with techniques that remain open areas of new research for addressing real-world machine learning problems.
- Deep Learning for Image Classification with Less Data - Nov 20, 2019.
In this blog I will be demonstrating how deep learning can be applied even if we don’t have enough data.
- The Math Behind Bayes - Nov 19, 2019.
This post will be dedicated to explaining the maths behind Bayes Theorem, when its application makes sense, and its differences with Maximum Likelihood.
- Generalization in Neural Networks - Nov 18, 2019.
When training a neural network in deep learning, its performance on processing new data is key. Improving the model's ability to generalize relies on preventing overfitting using these important methods.
- The Reinforcement-Learning Methods that Allow AlphaStar to Outcompete Almost All Human Players at StarCraft II - Nov 18, 2019.
The new AlphaStar achieved Grandmaster level at StarCraft II overcoming some of the limitations of the previous version. How did it do it?
- GitHub Repo Raider and the Automation of Machine Learning - Nov 18, 2019.
Since X never, ever marks the spot, this article raids the GitHub repos in search of quality automated machine learning resources. Read on for projects and papers to help understand and implement AutoML.
- Tips for a cost-effective machine learning project - Nov 15, 2019.
Spoiler: you don’t need a VM running 24/7 to handle 16 requests a day.
- Python Lists and List Manipulation - Nov 15, 2019.
In Python, lists store an ordered collection of items which can be of different types. This post is an overview of lists and their manipulation.
- How to Visualize Data in Python (and R) - Nov 14, 2019.
Producing accessible data visualizations is a key data science skill. The following guidelines will help you create the best representations of your data using R and Python's Pandas library.
- Topics Extraction and Classification of Online Chats - Nov 14, 2019.
This article provides covers how to automatically identify the topics within a corpus of textual data by using unsupervised topic modelling, and then apply a supervised classification algorithm to assign topic labels to each textual document by using the result of the previous step as target labels.
- Testing Your Machine Learning Pipelines - Nov 14, 2019.
Let’s take a look at traditional testing methodologies and how we can apply these to our data/ML pipelines.
- Transfer Learning Made Easy: Coding a Powerful Technique - Nov 13, 2019.
While the revolution of deep learning now impacts our daily lives, these networks are expensive. Approaches in transfer learning promise to ease this burden by enabling the re-use of trained models -- and this hands-on tutorial will walk you through a transfer learning technique you can run on your laptop.
- Beginners Guide to the Three Types of Machine Learning - Nov 13, 2019.
The following article is an introduction to classification and regression — which are known as supervised learning — and unsupervised learning — which in the context of machine learning applications often refers to clustering — and will include a walkthrough in the popular python library scikit-learn.
- Understanding NLP and Topic Modeling Part 1 - Nov 12, 2019.
In this post, we seek to understand why topic modeling is important and how it helps us as data scientists.
- Research Guide for Depth Estimation with Deep Learning - Nov 12, 2019.
In this guide, we’ll look at papers aimed at solving the problems of depth estimation using deep learning.
- The Complete Data Science LinkedIn Profile Guide - Nov 11, 2019.
With so many Data Scientists showing up on LinkedIn, it's time to make sure your profile is top-notch because your talent is still highly sought after. Recruitment specialists want to find you fast, and this guide will help you create the best profile to feature your expertise.
- Facebook Adds This New Framework to It’s Reinforcement Learning Arsenal - Nov 11, 2019.
ReAgent is a new framework that streamlines the implementation of reasoning systems.
- Understanding Boxplots - Nov 8, 2019.
A boxplot. It can tell you about your outliers and what their values are. It can also tell you if your data is symmetrical, how tightly your data is grouped, and if and how your data is skewed.
- Orchestrating Dynamic Reports in Python and R with Rmd Files - Nov 8, 2019.
Do you want to extract csv files with Python and visualize them in R? How does preparing everything in R and make conclusions with Python sound? Both are possible if you know the right libraries and techniques. Here, we’ll walk through a use-case using both languages in one analysis
- Data Cleaning and Preprocessing for Beginners - Nov 7, 2019.
Careful preprocessing of data for your machine learning project is crucial. This overview describes the process of data cleaning and dealing with noise and missing data.
- Set Operations Applied to Pandas DataFrames - Nov 7, 2019.
In this tutorial, we show how to apply mathematical set operations (union, intersection, and difference) to Pandas DataFrames with the goal of easing the task of comparing the rows of two datasets.
- How to Create a Vocabulary for NLP Tasks in Python - Nov 7, 2019.
This post will walkthrough a Python implementation of a vocabulary class for storing processed text data and related metadata in a manner useful for subsequently performing NLP tasks.
- An Eight-Step Checklist for An Analytics Project - Nov 6, 2019.
Follow these eight headings of an audit sheet that business analysts should address before submitting the results of their analytics project. One recommended approach is to rewrite each step as a question, answer it, and then attach it to your project.
- Research Guide: Advanced Loss Functions for Machine Learning Models - Nov 6, 2019.
This guide explores research centered on a variety of advanced loss functions for machine learning models.
- 10 Free Must-read Books on AI - Nov 5, 2019.
Artificial Intelligence continues to fill the media headlines while scientists and engineers rapidly expand its capabilities and applications. With such explosive growth in the field, there is a great deal to learn. Dive into these 10 free books that are must-reads to support your AI study and work.
- Probability Learning: Maximum Likelihood - Nov 5, 2019.
The maths behind Bayes will be better understood if we first cover the theory and maths underlying another fundamental method of probabilistic machine learning: Maximum Likelihood. This post will be dedicated to explaining it.
- Designing Your Neural Networks - Nov 4, 2019.
Check out this step-by-step walk through of some of the more confusing aspects of neural nets to guide you to making smart decisions about your neural network architecture.
- Customer Segmentation Using K Means Clustering - Nov 4, 2019.
Customer Segmentation can be a powerful means to identify unsatisfied customer needs. This technique can be used by companies to outperform the competition by developing uniquely appealing products and services.
- Facebook Has Been Quietly Open Sourcing Some Amazing Deep Learning Capabilities for PyTorch - Nov 4, 2019.
The new release of PyTorch includes some impressive open source projects for deep learning researchers and developers.
- Top Machine Learning Software Tools for Developers - Nov 1, 2019.
As a developer who is excited about leveraging machine learning for faster and more effective development, these software tools are worth trying out.
- Build an Artificial Neural Network From Scratch: Part 1 - Nov 1, 2019.
This article focused on building an Artificial Neural Network using the Numpy Python library.
- What is Machine Learning on Code? - Nov 1, 2019.
Not only can MLonCode help companies streamline their codebase and software delivery processes, but it also helps organizations better understand and manage their engineering talents.