Sep2019 Oct Tutorials, Overviews
All (59) | Courses, Education (2) | Meetings (4) | News (3) | Opinions (16) | Top Stories, Tweets (6) | Tutorials, Overviews (27) | Webcasts & Webinars (1)
- There is No Such Thing as a Free Lunch: Part 2 – Building an intelligent Digital Assistant
- Oct 18, 2019.
In this second part we want to outline our own experience building an AI application and reflect on why we chose not to utilise deep learning as the core technology used.
- Writing Your First Neural Net in Less Than 30 Lines of Code with Keras
- Oct 18, 2019.
Read this quick overview of neural networks and learn how to implement your first in very few lines using Keras.
- Data Anonymization – History and Key Ideas
- Oct 17, 2019.
While effective anonymization technology remains elusive, understanding the history of this challenge can guide data science practitioners to address these important concerns through ethical and responsible use of sensitive information.
- How to Easily Deploy Machine Learning Models Using Flask
- Oct 17, 2019.
This post aims to make you get started with putting your trained machine learning models into production using Flask API.
- Probability Learning I: Bayes’ Theorem
- Oct 16, 2019.
Learn about one of the fundamental theorems of probability with an easy everyday example.
- The 5 Classification Evaluation Metrics Every Data Scientist Must Know
- Oct 16, 2019.
This post is about various evaluation metrics and how and when to use them.
- Research Guide for Video Frame Interpolation with Deep Learning
- Oct 15, 2019.
In this research guide, we’ll look at deep learning papers aimed at synthesizing video frames within an existing video.
- Three Things to Know About Reinforcement Learning
- Oct 14, 2019.
As an engineer, scientist, or researcher, you may want to take advantage of this new and growing technology, but where do you start? The best place to begin is to understand what the concept is, how to implement it, and whether it’s the right approach for a given problem.
- Using Neural Networks to Design Neural Networks: The Definitive Guide to Understand Neural Architecture Search
- Oct 14, 2019.
A recent survey outlined the main neural architecture search methods used to automate the design of deep learning systems.
- An Overview of Density Estimation
- Oct 14, 2019.
Density estimation is estimating the probability density function of the population from the sample. This post examines and compares a number of approaches to density estimation.
- Beyond Word Embedding: Key Ideas in Document Embedding
- Oct 11, 2019.
This literature review on document embedding techniques thoroughly covers the many ways practitioners develop rich vector representations of text -- from single sentences to entire books.
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Activation maps for deep learning models in a few lines of code
- Oct 10, 2019.
We illustrate how to show the activation maps of various layers in a deep CNN model with just a couple of lines of code. - Contributing to PyTorch: By someone who doesn’t know a ton about PyTorch
- Oct 9, 2019.
By the end of my week with the team, I managed to proudly cut two PRs on GitHub. I decided that I would write a blog post to knowledge share, not just to show that YES, you can too.
- Introduction to Artificial Neural Networks
- Oct 8, 2019.
In this article, we’ll try to cover everything related to Artificial Neural Networks or ANN.
- Know Your Data: Part 2
- Oct 8, 2019.
To build an effective learning model, it is must to understand the quality issues exist in data & how to detect and deal with it. In general, data quality issues are categories in four major sets.
- The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization
- Oct 7, 2019.
As a data scientist, your most important skill is creating meaningful visualizations to disseminate knowledge and impact your organization or client. These seven principals will guide you toward developing charts with clarity, as exemplified with data from a recent KDnuggets poll.
- OpenAI Tried to Train AI Agents to Play Hide-And-Seek but Instead They Were Shocked by What They Learned
- Oct 7, 2019.
OpenAI trained agents in a simple game of hide-and-seek and learned many other different skills in the process.
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10 Free Top Notch Natural Language Processing Courses
- Oct 7, 2019.
Are you looking to learn natural language processing? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to learning NLP and its varied topics. - The Last SQL Guide for Data Analysis You’ll Ever Need
- Oct 4, 2019.
This is it: the last SQL guide for data analysis you'll ever need! OK, maybe it’s actually the first. But it’ll give you a solid head start.
- Research Guide for Neural Architecture Search
- Oct 4, 2019.
In this guide, we will explore a range of research papers that have sought to solve the challenging task of automating neural network design.
- 5 Fundamental AI Principles
- Oct 3, 2019.
While AI may appear magical at times, these five principles will help guide you to avoid pitfalls when leveraging this tech.
- Recreating Imagination: DeepMind Builds Neural Networks that Spontaneously Replay Past Experiences
- Oct 3, 2019.
DeepMind researchers created a model to be able to replay past experiences in a way that simulate the mechanisms in the hippocampus.
- Choosing the Right Clustering Algorithm for your Dataset
- Oct 2, 2019.
Applying a clustering algorithm is much easier than selecting the best one. Each type offers pros and cons that must be considered if you’re striving for a tidy cluster structure.
- Data Preparation for Machine learning 101: Why it’s important and how to do it
- Oct 2, 2019.
As data scientists who are the brains behind the AI-based innovations, you need to understand the significance of data preparation to achieve the desired level of cognitive capability for your models. Let’s begin.
- Multi-Task Learning – ERNIE 2.0: State-of-the-Art NLP Architecture Intuitively Explained
- Oct 2, 2019.
The tech giant Baidu unveiled its state-of-the-art NLP architecture ERNIE 2.0 earlier this year, which scored significantly higher than XLNet and BERT on all tasks in the GLUE benchmark. This major breakthrough in NLP takes advantage of a new innovation called “Continual Incremental Multi-Task Learning”.
- Sentiment and Emotion Analysis for Beginners: Types and Challenges
- Oct 1, 2019.
There are three types of emotion AI, and their combinations. In this article, I’ll briefly go through these three types and the challenges of their real-life applications.
- Clustering Metrics Better Than the Elbow Method
- Oct 1, 2019.
We show what metric to use for visualizing and determining an optimal number of clusters much better than the usual practice — elbow method.
