Jun2019 Jul Tutorials, Overviews
All (45) | Meetings (2) | News (3) | Opinions (11) | Top Stories, Tweets (6) | Tutorials, Overviews (22) | Webcasts & Webinars (1)
- Scaling a Massive State-of-the-art Deep Learning Model in Production
- Jul 15, 2019.
A new NLP text writing app based on OpenAI's GPT-2 aims to write with you -- whenever you ask. Find out how the developers setup and deployed their model into production from an engineer working on the team.
- The Hackathon Guide for Aspiring Data Scientists
- Jul 15, 2019.
This article is an overview of how to prepare for a hackathon as an aspiring data scientist, highlighting the 4 reasons why you should take part in one, along with a series of tips for participation.
- Introducing Gen: MIT’s New Language That Wants to be the TensorFlow of Programmable Inference
- Jul 12, 2019.
Researchers from MIT recently unveiled a new probabilistic programming language named Gen, a language which allow researchers to write models and algorithms from multiple fields where AI techniques are applied without having to deal with equations or manually write high-performance code.
- Pre-training, Transformers, and Bi-directionality
- Jul 12, 2019.
Bidirectional Encoder Representations from Transformers BERT (Devlin et al., 2018) is a language representation model that combines the power of pre-training with the bi-directionality of the Transformer’s encoder (Vaswani et al., 2017). BERT improves the state-of-the-art performance on a wide array of downstream NLP tasks with minimal additional task-specific training.
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Training a Neural Network to Write Like Lovecraft
- Jul 11, 2019.
In this post, the author attempts to train a neural network to generate Lovecraft-esque prose, known to be awkward and irregular at best. Did it end in success? If not, any suggestions on how it might have? Read on to find out. - 10 Simple Hacks to Speed up Your Data Analysis in Python
- Jul 11, 2019.
This article lists some curated tips for working with Python and Jupyter Notebooks, covering topics such as easily profiling data, formatting code and output, debugging, and more. Hopefully you can find something useful within.
- A Gentle Guide to Starting Your NLP Project with AllenNLP
- Jul 10, 2019.
For those who aren’t familiar with AllenNLP, I will give a brief overview of the library and let you know the advantages of integrating it to your project.
- Practical Speech Recognition with Python: The Basics
- Jul 9, 2019.
Do you fear implementing speech recognition in your Python apps? Read this tutorial for a simple approach to getting practical with speech recognition using open source Python libraries.
- Annotated Heatmaps of a Correlation Matrix in 5 Simple Steps
- Jul 9, 2019.
A heatmap is a graphical representation of data in which data values are represented as colors. That is, it uses color in order to communicate a value to the reader. This is a great tool to assist the audience towards the areas that matter the most when you have a large volume of data.
- Collaborative Evolutionary Reinforcement Learning
- Jul 8, 2019.
Intel Researchers created a new approach to RL via Collaborative Evolutionary Reinforcement Learning (CERL) that combines policy gradient and evolution methods to optimize, exploit, and explore challenges.
- XGBoost and Random Forest with Bayesian Optimisation
- Jul 8, 2019.
This article will explain how to use XGBoost and Random Forest with Bayesian Optimisation, and will discuss the main pros and cons of these methods.
- Classifying Heart Disease Using K-Nearest Neighbors
- Jul 8, 2019.
I have written this post for the developers and assumes no background in statistics or mathematics. The focus is mainly on how the k-NN algorithm works and how to use it for predictive modeling problems.
- State of AI Report 2019
- Jul 5, 2019.
This year's "State of AI Report" has been released. Read it to find out about the latest in AI research, talent, industry, and politics form the past 12 months.
- Top 8 Data Science Use Cases in Construction
- Jul 5, 2019.
This article considers several of the most efficient and productive data science use cases in the construction industry.
- 5 Probability Distributions Every Data Scientist Should Know
- Jul 4, 2019.
Having an understanding of probability distributions should be a priority for data scientists. Make sure you know what you should by reviewing this post on the subject.
- NLP vs. NLU: from Understanding a Language to Its Processing
- Jul 3, 2019.
As AI progresses and the technology becomes more sophisticated, we expect existing techniques to evolve. With these changes, will the well-founded natural language processing give way to natural language understanding? Or, are the two concepts subtly distinct to hold their own niche in AI?
- Building a Recommender System, Part 2
- Jul 3, 2019.
This post explores an technique for collaborative filtering which uses latent factor models, a which naturally generalizes to deep learning approaches. Our approach will be implemented using Tensorflow and Keras.
- Examining the Transformer Architecture – Part 2: A Brief Description of How Transformers Work
- Jul 2, 2019.
As The Transformer may become the new NLP standard, this review explores its architecture along with a comparison to existing approaches by RNN.
- 4 Most Popular Alternative Data Sources Explained
- Jul 2, 2019.
Alternative data is the new game changer. To start with alternative data, people might even wonder from where you can get hold of alternative data that can give such a competitive advantage. This post details 4 alternative data sources that you can exploit to the fullest.
- Seven Key Dimensions to Help You Understand Artificial Intelligence Environments
- Jul 2, 2019.
Understanding an AI environment is an incredibly complex task but there are several key dimensions that provide clarity on that reasoning.
- How do you check the quality of your regression model in Python?
- Jul 2, 2019.
Linear regression is rooted strongly in the field of statistical learning and therefore the model must be checked for the ‘goodness of fit’. This article shows you the essential steps of this task in a Python ecosystem.
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XLNet Outperforms BERT on Several NLP Tasks
- Jul 1, 2019.
XLNet is a new pretraining method for NLP that achieves state-of-the-art results on several NLP tasks.
