2019 Jan Tutorials, Overviews
All (102) | Courses, Education (8) | Meetings (12) | News (10) | Opinions (28) | Top Stories, Tweets (10) | Tutorials, Overviews (27) | Webcasts & Webinars (7)
- ELMo: Contextual Language Embedding - Jan 31, 2019.
Create a semantic search engine using deep contextualised language representations from ELMo and why context is everything in NLP.
- Random forests® explained intuitively - Jan 30, 2019.
A detailed explanation of random forests, with real life use cases, a discussion into when a random forest is a poor choice relative to other algorithms, and looking at some of the advantages of using random forest.
- Building an image search service from scratch - Jan 30, 2019.
By the end of this post, you should be able to build a quick semantic search model from scratch, no matter the size of your dataset.
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7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition - Jan 29, 2019.
With a new year upon us, I thought it would be a good time to revisit the concept and put together a new learning path for mastering machine learning with Python. With these 7 steps you can master basic machine learning with Python! - Airbnb Rental Listings Dataset Mining - Jan 28, 2019.
An Exploratory Analysis of Airbnb’s Data to understand the rental landscape in New York City.
- Machine Learning Security - Jan 25, 2019.
We take a look at how malicious actors can break machine learning models and what some of the best practices are when it comes to stopping them.
- Data Science Project Flow for Startups - Jan 24, 2019.
The aim of this post, then, is to present the characteristic project flow that I have identified in the working process of both my colleagues and myself in recent years. Hopefully, this can help both data scientists and the people working with them to structure data science projects in a way that reflects their uniqueness.
- How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy - Jan 23, 2019.
We explain how to retrieve estimates of a model's performance using scoring metrics, before taking a look at finding and diagnosing the potential problems of a machine learning algorithm.
- Building AI to Build AI: The Project That Won the NeurIPS AutoML Challenge - Jan 23, 2019.
This is an overview of designing a computer program capable of developing predictive models without any manual intervention that are trained & evaluated in a lifelong machine learning setting in NeurIPS 2018 AutoML3 Challenge.
- Logistic Regression: A Concise Technical Overview - Jan 23, 2019.
Logistic Regression is a Regression technique that is used when we have a categorical outcome (2 or more categories). Logistic Regression is one of the most easily interpretable classification techniques in a Data Scientist’s portfolio.
- 2018’s Top 7 R Packages for Data Science and AI - Jan 22, 2019.
This is a list of the best packages that changed our lives this year, compiled from my weekly digests.
- 2018’s Top 7 Python Libraries for Data Science and AI - Jan 21, 2019.
This is a list of the best libraries that changed our lives this year, compiled from my weekly digests.
- Automated Machine Learning in Python - Jan 18, 2019.
An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.
- Comparing Machine Learning Models: Statistical vs. Practical Significance - Jan 18, 2019.
Is model A or B more accurate? Hmm… In this blog post, I’d love to share my recent findings on model comparison.
- How to build an API for a machine learning model in 5 minutes using Flask, by Tim Elfrink - Jan 17, 2019.
Flask is a micro web framework written in Python. It can create a REST API that allows you to send data, and receive a prediction as a response.
- Word Embeddings & Self-Supervised Learning, Explained - Jan 16, 2019.
There are many algorithms to learn word embeddings. Here, we consider only one of them: word2vec, and only one version of word2vec called skip-gram, which works well in practice.
- Ontology and Data Science - Jan 16, 2019.
In simple words, one can say that ontology is the study of what there is. But there is another part to that definition that will help us in the following sections, and that is ontology is usually also taken to encompass problems about the most general features and relations of the entities which do exist.
- How to solve 90% of NLP problems: a step-by-step guide, by Emmanuel Ameisen - Jan 14, 2019.
Read this insightful, step-by-step article on how to use machine learning to understand and leverage text.
- End To End Guide For Machine Learning Projects - Jan 14, 2019.
Let’s imagine you are attempting to work on a machine learning project. This article will provide you with the step to step guide on the process that you can follow to implement a successful project.
- Practical Apache Spark in 10 Minutes - Jan 11, 2019.
Check out this series of articles on Apache Spark. Each part is a 10 minute tutorial on a particular Apache Spark topic. Read on to get up to speed using Spark.
- Python Patterns: max Instead of if - Jan 10, 2019.
I often have to loop over a set of objects to find the one with the greatest score. You can use an if statement and a placeholder, but there are more elegant ways!
- Top 10 Books on NLP and Text Analysis - Jan 9, 2019.
When it comes to choosing the right book, you become immediately overwhelmed with the abundance of possibilities. In this review, we have collected our Top 10 NLP and Text Analysis Books of all time, ranging from beginners to experts.
- NLP Overview: Modern Deep Learning Techniques Applied to Natural Language Processing - Jan 8, 2019.
Trying to keep up with advancements at the overlap of neural networks and natural language processing can be troublesome. That's where the today's spotlighted resource comes in.
- Comparison of the Text Distance Metrics - Jan 7, 2019.
There are many different approaches of how to compare two texts (strings of characters). Each has its own advantages and disadvantages and is good only for a range of specific use cases.
- What to do when your training and testing data come from different distributions - Jan 4, 2019.
However, sometimes only a limited amount of data from the target distribution can be collected. It may not be sufficient to build the needed train/dev/test sets. What to do in such a case? Let us discuss some ideas!
- The Backpropagation Algorithm Demystified - Jan 2, 2019.
A crucial aspect of machine learning is its ability to recognize error margins and to interpret data more precisely as rising numbers of datasets are fed through its neural network. Commonly referred to as backpropagation, it is a process that isn’t as complex as you might think.
- 3 More Google Colab Environment Management Tips - Jan 2, 2019.
This is a short collection of lessons learned using Colab as my main coding learning environment for the past few months. Some tricks are Colab specific, others as general Jupyter tips, and still more are filesystem related, but all have proven useful for me.
7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition
How to build an API for a machine learning model in 5 minutes using Flask
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