2018 May Tutorials, Overviews
All (114) | Courses, Education (9) | Meetings (18) | News, Features (8) | Opinions, Interviews (30) | Top Stories, Tweets (10) | Tutorials, Overviews (32) | Webcasts & Webinars (7)
- On the contribution of neural networks and word embeddings in Natural Language Processing - May 31, 2018.
In this post I will try to explain, in a very simplified way, how to apply neural networks and integrate word embeddings in text-based applications, and some of the main implicit benefits of using neural networks and word embeddings in NLP.
- Introduction to Content Personalization - May 30, 2018.
The basics of user experience and content personalization. The way to target your audience more precisely and effectively.
- Improving the Performance of a Neural Network - May 30, 2018.
There are many techniques available that could help us achieve that. Follow along to get to know them and to build your own accurate neural network.
- 6 Tips for Effective Visualization with Tableau - May 29, 2018.
We analyse principles for effective data visualization in Tableau, including color gradients, avoiding crowded dashboards, Tableau marks and more.
- A Beginner’s Guide to the Data Science Pipeline - May 29, 2018.
On one end was a pipe with an entrance and at the other end an exit. The pipe was also labeled with five distinct letters: "O.S.E.M.N."
- 10 More Free Must-Read Books for Machine Learning and Data Science - May 28, 2018.
Summer, summer, summertime. Time to sit back and unwind. Or get your hands on some free machine learning and data science books and get your learn on. Check out this selection to get you started.
- Top 20 R Libraries for Data Science in 2018 - May 25, 2018.
We have prepared an infographic of Top 20 R packages for data science, which covers the libraries main features and GitHub activities, as all of the libraries are open-source.
- Modelling Time Series Processes using GARCH - May 25, 2018.
To go into the turbulent seas of volatile data and analyze it in a time changing setting, ARCH models were developed.
- How to tackle common data cleaning issues in R - May 24, 2018.
R is a great choice for manipulating, cleaning, summarizing, producing probability statistics, and so on. In addition, it's not going away anytime soon, it is platform independent, so what you create will run almost anywhere, and it has awesome help resources.
- Deep Learning With Apache Spark: Part 2 - May 23, 2018.
In this article I’ll continue the discussion on Deep Learning with Apache Spark. I will focus entirely on the DL pipelines library and how to use it from scratch.
- Generative Adversarial Neural Networks: Infinite Monkeys and The Great British Bake Off - May 22, 2018.
Adversarial Neural Networks are oddly named since they actually cooperate to make things.
- Optimization Using R - May 18, 2018.
Optimization is a technique for finding out the best possible solution for a given problem for all the possible solutions. Optimization uses a rigorous mathematical model to find out the most efficient solution to the given problem.
- An Introduction to Deep Learning for Tabular Data - May 17, 2018.
This post will discuss a technique that many people don’t even realize is possible: the use of deep learning for tabular data, and in particular, the creation of embeddings for categorical variables.
- How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1 - May 17, 2018.
The best way to go about learning object detection is to implement the algorithms by yourself, from scratch. This is exactly what we'll do in this tutorial.
- GANs in TensorFlow from the Command Line: Creating Your First GitHub Project - May 16, 2018.
In this article I will present the steps to create your first GitHub Project. I will use as an example Generative Adversarial Networks.
- Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API - May 15, 2018.
In this tutorial, a CNN is to be built, and trained and tested against the CIFAR10 dataset. To make the model remotely accessible, a Flask Web application is created using Python to receive an uploaded image and return its classification label using HTTP.
- A Brief Introduction to Wikidata - May 15, 2018.
Like Wikipedia, there are all kinds of data stored in Wikidata. As such, when you are looking for a specific dataset or if you want to answer a curious question, it can be a good start looking for that data at Wikidata first.
- Simple Derivatives with PyTorch - May 14, 2018.
PyTorch includes an automatic differentiation package, autograd, which does the heavy lifting for finding derivatives. This post explores simple derivatives using autograd, outside of neural networks.
- PyTorch Tensor Basics - May 11, 2018.
This is an introduction to PyTorch's Tensor class, which is reasonably analogous to Numpy's ndarray, and which forms the basis for building neural networks in PyTorch.
- The Executive Guide to Data Science and Machine Learning - May 10, 2018.
This article provides a short introductory guide for executives curious about data science or commonly used terms they may encounter when working with their data team. It may also be of interest to other business professionals who are collaborating with data teams or trying to learn data science within their unit.
- Deep learning scaling is predictable, empirically - May 10, 2018.
This study starts with a simple question: “how can we improve the state of the art in deep learning?”
- Top 7 Data Science Use Cases in Finance - May 10, 2018.
We have prepared a list of data science use cases that have the highest impact on the finance sector. They cover very diverse business aspects from data management to trading strategies, but the common thing for them is the huge prospects to enhance financial solutions.
- Detecting Breast Cancer with Deep Learning - May 9, 2018.
Breast cancer is the most common invasive cancer in women, and the second main cause of cancer death in women, after lung cancer. In this article I will build a WideResNet based neural network to categorize slide images into two classes, one that contains breast cancer and other that doesn’t using Deep Learning Studio.
- How I Used CNNs and Tensorflow and Lost a Silver Medal in Kaggle Challenge - May 8, 2018.
I joined the competition a month before it ended, eager to explore how to use Deep Natural Language Processing (NLP) techniques for this problem. Then came the deception. And I will tell you how I lost my silver medal in that competition.
- 5 Reasons Logistic Regression should be the first thing you learn when becoming a Data Scientist - May 8, 2018.
Learn Logistic Regression first to become familiar with the pipeline and not being overwhelmed with fancy algorithms.
- Skewness vs Kurtosis – The Robust Duo - May 4, 2018.
Kurtosis and Skewness are very close relatives of the “data normalized statistical moment” family – Kurtosis being the fourth and Skewness the third moment, and yet they are often used to detect very different phenomena in data. At the same time, it is typically recommendable to analyse the outputs of both together to gather more insight and understand the nature of the data better.
- Boost your data science skills. Learn linear algebra. - May 3, 2018.
The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms.
- Getting Started with spaCy for Natural Language Processing - May 2, 2018.
spaCy is a Python natural language processing library specifically designed with the goal of being a useful library for implementing production-ready systems. It is particularly fast and intuitive, making it a top contender for NLP tasks.
- 50+ Useful Machine Learning & Prediction APIs, 2018 Edition - May 1, 2018.
Extensive list of 50+ APIs in Face and Image Recognition ,Text Analysis, NLP, Sentiment Analysis, Language Translation, Machine Learning and prediction.
- Data Science vs Machine Learning vs Data Analytics vs Business Analytics - May 1, 2018.
This article gives a broad overview of data science and the various fields within it, including business analytics, data analytics, business intelligence, advanced analytics, machine learning, and AI.
- Jupyter Notebook for Beginners: A Tutorial - May 1, 2018.
The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects. Although it is possible to use many different programming languages within Jupyter Notebooks, this article will focus on Python as it is the most common use case.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: FastText - May 1, 2018.
Overall, FastText is a framework for learning word representations and also performing robust, fast and accurate text classification. The framework is open-sourced by Facebook on GitHub.