Dec2019 Jan Tutorials, Overviews
All (55) | Courses, Education (6) | Meetings (8) | News (5) | Opinions (14) | Top Stories, Tweets (6) | Tutorials, Overviews (13) | Webcasts & Webinars (3)
- 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
- 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!
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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!
- Approaches to Text Summarization: An Overview
- Jan 3, 2019.
This article will present the main approaches to text summarization currently employed, as well as discuss some of their characteristics.
- 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.