Tutorials, Overviews
DataCamp - Easiest Way to Learn Data Science
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Learning R? Take this Intro to R for Data Science Tutorial. 
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Learning Python? Take this Intro to Python for Data Science Tutorial. |
Check also these fantastic posts:
R Learning Path: From beginner to expert in R in 7 steps
Comprehensive Guide to Learning Python for Data Science
Latest:
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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. -
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. -
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.
September:
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Know Your Data: Part 1
This article will introduce the different type of data sets, data object and attributes. - DeepMind Has Quietly Open Sourced Three New Impressive Reinforcement Learning Frameworks
- Using Time Series Encodings to Discover Baseball History’s Most Interesting Seasons
- What is Hierarchical Clustering?
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The Future of Analytics and Data Science
Learn about the current and future issues of data science and possible solutions from this interview with IADSS Co-founder, Dr. Usama Fayyad following his keynote speech at ODSC Boston 2019.
- Natural Language in Python using spaCy: An Introduction
- Customer Segmentation for R Users
- Beta Distribution: What, When & How
- Automatic Version Control for Data Scientists
- A 2019 Guide for Automatic Speech Recognition
- A Single Function to Streamline Image Classification with Keras
- Introducing IceCAPS: Microsoft’s Framework for Advanced Conversation Modeling
- A Gentle Introduction to PyTorch 1.2
- Automate Hyperparameter Tuning for Your Models
- Scikit-Learn & More for Synthetic Dataset Generation for Machine Learning
- Applying Data Science to Cybersecurity Network Attacks & Events
- The 5 Sampling Algorithms every Data Scientist need to know
- Reddit Post Classification
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Explore the world of Bioinformatics with Machine Learning
The article contains a brief introduction of Bioinformatics and how a machine learning classification algorithm can be used to classify the type of cancer in each patient by their gene expressions.
- BERT, RoBERTa, DistilBERT, XLNet: Which one to use?
- How Bad is Multicollinearity?
- 5 Step Guide to Scalable Deep Learning Pipelines with d6tflow
- What is Machine Behavior?
- Version Control for Data Science: Tracking Machine Learning Models and Datasets
- The State of Transfer Learning in NLP
- Ensemble Methods for Machine Learning: AdaBoost
- A Friendly Introduction to Support Vector Machines
- Can graph machine learning identify hate speech in online social networks?
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Train sklearn 100x Faster
As compute gets cheaper and time to market for machine learning solutions becomes more critical, we’ve explored options for speeding up model training. One of those solutions is to combine elements from Spark and scikit-learn into our own hybrid solution. - Scikit-Learn vs mlr for Machine Learning
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The 5 Graph Algorithms That Data Scientists Should Know
In this post, I am going to be talking about some of the most important graph algorithms you should know and how to implement them using Python.
- A 2019 Guide to Speech Synthesis with Deep Learning
- OpenStreetMap Data to ML Training Labels for Object Detection
- How DeepMind and Waymo are Using Evolutionary Competition to Train Self-Driving Vehicles
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10 Great Python Resources for Aspiring Data Scientists
This is a collection of 10 interesting resources in the form of articles and tutorials for the aspiring data scientist new to Python, meant to provide both insight and practical instruction when starting on your journey. - Build Your First Voice Assistant
- An Easy Introduction to Machine Learning Recommender Systems
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Python Libraries for Interpretable Machine Learning
In the following post, I am going to give a brief guide to four of the most established packages for interpreting and explaining machine learning models.
- An Overview of Topics Extraction in Python with Latent Dirichlet Allocation
- Beyond Neurons: Five Cognitive Functions of the Human Brain that we are Trying to Recreate with Artificial Intelligence
- Automate your Python Scripts with Task Scheduler: Windows Task Scheduler to Scrape Alternative Data
- Top 10 Data Science Use Cases in Energy and Utilities
