2018 Dec Tutorials, Overviews
All (86) | Courses, Education (3) | Meetings (5) | News (14) | Opinions (28) | Top Stories, Tweets (9) | Tutorials, Overviews (23) | Webcasts & Webinars (4)
- Good Feature Building Techniques and Tricks for Kaggle - Dec 31, 2018.
A selection of top tips to obtain great results on Kaggle leaderboards, including useful code examples showing how best to use Latitude and Longitude features.
- Papers with Code: A Fantastic GitHub Resource for Machine Learning - Dec 31, 2018.
Looking for papers with code? If so, this GitHub repository, a clearinghouse for research papers and their corresponding implementation code, is definitely worth checking out.
- Comparison of the Top Speech Processing APIs - Dec 28, 2018.
There are two main tasks in speech processing. First one is to transform speech to text. The second is to convert the text into human speech. We will describe the general aspects of each API and then compare their main features in the table.
- Synthetic Data Generation: A must-have skill for new data scientists - Dec 27, 2018.
A brief rundown of methods/packages/ideas to generate synthetic data for self-driven data science projects and deep diving into machine learning methods.
- Deep learning in Satellite imagery - Dec 26, 2018.
This article outlines possible sources of satellite imagery, what its properties are and how this data can be utilised using R.
- BERT: State of the Art NLP Model, Explained - Dec 26, 2018.
BERT’s key technical innovation is applying the bidirectional training of Transformer, a popular attention model, to language modelling. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks.
- A Guide to Decision Trees for Machine Learning and Data Science - Dec 24, 2018.
What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure.
- Six Steps to Master Machine Learning with Data Preparation - Dec 21, 2018.
To prepare data for both analytics and machine learning initiatives teams can accelerate machine learning and data science projects to deliver an immersive business consumer experience that accelerates and automates the data-to-insight pipeline by following six critical steps.
- 10 More Must-See Free Courses for Machine Learning and Data Science - Dec 20, 2018.
Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.
- Introduction to Statistics for Data Science - Dec 17, 2018.
This tutorial helps explain the central limit theorem, covering populations and samples, sampling distribution, intuition, and contains a useful video so you can continue your learning.
- Implementing ResNet with MXNET Gluon and Comet.ml for Image Classification - Dec 14, 2018.
Whether MXNet is an entirely new framework for you or you have used the MXNet backend while training your Keras models, this tutorial illustrates how to build an image recognition model with an MXNet resnet_v1 model.
- State of Deep Learning and Major Advances: H2 2018 Review - Dec 13, 2018.
In this post we summarise some of the key developments in deep learning in the second half of 2018, before briefly discussing the road ahead for the deep learning community.
- Solve any Image Classification Problem Quickly and Easily - Dec 13, 2018.
This article teaches you how to use transfer learning to solve image classification problems. A practical example using Keras and its pre-trained models is given for demonstration purposes.
- Keras Hyperparameter Tuning in Google Colab Using Hyperas - Dec 12, 2018.
In this post, I will show you how you can tune the hyperparameters of your existing keras models using Hyperas and run everything in a Google Colab Notebook.
- Automated Web Scraping in R - Dec 11, 2018.
How to automatically web scrape periodically so you can analyze timely/frequently updated data.
- Introduction to Named Entity Recognition - Dec 11, 2018.
Named Entity Recognition is a tool which invariably comes handy when we do Natural Language Processing tasks. Read on to find out how.
- A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more - Dec 7, 2018.
A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.
- The Machine Learning Project Checklist - Dec 7, 2018.
In an effort to further refine our internal models, this post will present an overview of Aurélien Géron's Machine Learning Project Checklist, as seen in his bestselling book, "Hands-On Machine Learning with Scikit-Learn & TensorFlow."
- Common mistakes when carrying out machine learning and data science - Dec 6, 2018.
We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid!
- Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies - Dec 6, 2018.
The aim of this article is to give you a good understanding of existing, traditional model interpretation methods, their limitations and challenges. We will also cover the classic model accuracy vs. model interpretability trade-off and finally take a look at the major strategies for model interpretation.
- Four Techniques for Outlier Detection - Dec 6, 2018.
There are many techniques to detect and optionally remove outliers from a dataset. In this blog post, we show an implementation in KNIME Analytics Platform of four of the most frequently used - traditional and novel - techniques for outlier detection.
- How to build a data science project from scratch - Dec 5, 2018.
A demonstration using an analysis of Berlin rental prices, covering how to extract data from the web and clean it, gaining deeper insights, engineering of features using external APIs, and more.
- Handling Imbalanced Datasets in Deep Learning - Dec 4, 2018.
It’s important to understand why we should do it so that we can be sure it’s a valuable investment. Class balancing techniques are only really necessary when we actually care about the minority classes.