Jun
2019 Jul Tutorials, Overviews
All (94) | Meetings (4) | News (8) | Opinions (25) | Top Stories, Tweets (11) | Tutorials, Overviews (44) | Webcasts & Webinars (2)
- Five Command Line Tools for Data Science
- Jul 31, 2019.
You can do more data science than you think from the terminal.
- Ten more random useful things in R you may not know about
- Jul 31, 2019.
I had a feeling that R has developed as a language to such a degree that many of us are using it now in completely different ways. This means that there are likely to be numerous tricks, packages, functions, etc that each of us use, but that others are completely unaware of, and would find useful if they knew about them.
- Understanding Tensor Processing Units
- Jul 30, 2019.
The Tensor Processing Unit (TPU) is Google's custom tool to accelerate machine learning workloads using the TensorFlow framework. Learn more about what TPUs do and how they can work for you.
- P-values Explained By Data Scientist
- Jul 30, 2019.
This article is designed to give you a full picture from constructing a hypothesis testing to understanding p-value and using that to guide our decision making process.
- Here’s how you can accelerate your Data Science on GPU
- Jul 30, 2019.
Data Scientists need computing power. Whether you’re processing a big dataset with Pandas or running some computation on a massive matrix with Numpy, you’ll need a powerful machine to get the job done in a reasonable amount of time.
-
Top 10 Best Podcasts on AI, Analytics, Data Science, Machine Learning
- Jul 29, 2019.
Check out our latest Top 10 Most Popular Data Science and Machine Learning podcasts available on iTunes. Stay up to date in the field with these recent episodes and join in with the current data conversations. - 7 Tips for Dealing With Small Data
- Jul 29, 2019.
At my workplace, we produce a lot of functional prototypes for our clients. Because of this, I often need to make Small Data go a long way. In this article, I’ll share 7 tips to improve your results when prototyping with small datasets.
-
Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras
- Jul 26, 2019.
Different neural network architectures excel in different tasks. This particular article focuses on crafting convolutional neural networks in Python using TensorFlow and Keras. - A Gentle Introduction to Noise Contrastive Estimation
- Jul 25, 2019.
Find out how to use randomness to learn your data by using Noise Contrastive Estimation with this guide that works through the particulars of its implementation.
- Top Certificates and Certifications in Analytics, Data Science, Machine Learning and AI
- Jul 25, 2019.
Here are the top certificates and certifications in Analytics, AI, Data Science, Machine Learning and related areas.
- This New Google Technique Help Us Understand How Neural Networks are Thinking
- Jul 24, 2019.
Recently, researchers from the Google Brain team published a paper proposing a new method called Concept Activation Vectors (CAVs) that takes a new angle to the interpretability of deep learning models.
- Easy, One-Click Jupyter Notebooks
- Jul 24, 2019.
All of the setup for software, networking, security, and libraries is automatically taken care of by the Saturn Cloud system. Data Scientists can then focus on the actual Data Science and not the tedious infrastructure work that falls around it
- Kaggle Kernels Guide for Beginners: A Step by Step Tutorial
- Jul 23, 2019.
This is an attempt to hold the hands of a complete beginner and walk them through the world of Kaggle Kernels — for them to get started.
- From Data Pre-processing to Optimizing a Regression Model Performance
- Jul 19, 2019.
All you need to know about data pre-processing, and how to build and optimize a regression model using Backward Elimination method in Python.
- Bayesian deep learning and near-term quantum computers: A cautionary tale in quantum machine learning
- Jul 19, 2019.
This blog post is an overview of quantum machine learning written by the author of the paper Bayesian deep learning on a quantum computer. In it, we explore the application of machine learning in the quantum computing space. The authors of this paper hope that the results of the experiment help influence the future development of quantum machine learning.
- The Evolution of a ggplot
- Jul 18, 2019.
A step-by-step tutorial showing how to turn a default ggplot into an appealing and easily understandable data visualization in R.
- Big Data for Insurance
- Jul 18, 2019.
The insurance industry has always been quite conservative; however, the adoption of new technologies is not just a modern trend but a necessity to maintain the competitive pace. In the modern digital era, Big Data technologies help to process vast amounts of information, increase workflow efficiency, and reduce operational costs. Learn more about the benefits of Big Data for insurance from our material.
- Adapters: A Compact and Extensible Transfer Learning Method for NLP
- Jul 18, 2019.
Adapters obtain comparable results to BERT on several NLP tasks while achieving parameter efficiency.
- A Summary of DeepMind’s Protein Folding Upset at CASP13
- Jul 17, 2019.
Learn how DeepMind dominated the last CASP competition for advancing protein folding models. Their approach using gradient descent is today's state of the art for predicting the 3D structure of a protein knowing only its comprising amino acid compounds.
- How to Make Stunning 3D Plots for Better Storytelling
- Jul 17, 2019.
3D Plots built in the right way for the right purpose are always stunning. In this article, we’ll see how to make stunning 3D plots with R using ggplot2 and rayshader.
- Computer Vision for Beginners: Part 1
- Jul 17, 2019.
Image processing is performing some operations on images to get an intended manipulation. Think about what we do when we start a new data analysis. We do some data preprocessing and feature engineering. It’s the same with image processing.
- Dealing with categorical features in machine learning
- Jul 16, 2019.
Many machine learning algorithms require that their input is numerical and therefore categorical features must be transformed into numerical features before we can use any of these algorithms.
- 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.
- 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.
- 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.
Learn Deep Learning with this Free Course from Yann LeCun