- 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.
Pages: 1 2
Classification, Computer Vision, Image Recognition, Keras, Python
- 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 Machine Learning, Google, Google Colab, Hyperparameter, Keras, Python
- 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.
Pages: 1 2
NLP, Python, Text Classification
Here are the most popular Python IDEs / Editors - Dec 7, 2018.
We report on the most popular IDE and Editors, based on our poll. Jupyter is the favorite across all regions and employment types, but there is competition for no. 2 and no. 3 spots.
IDE, Jupyter, Poll, Programming, PyCharm, Python, Visual Studio Code
- 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.
DBSCAN, Knime, Outliers, Python
- 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.
Balancing Classes, Datasets, Deep Learning, Keras, Python
Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools - Dec 3, 2018.
We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields.
Big Data, Data Visualization, Deep Learning, Jupyter, Machine Learning, Python, R, Tableau
- Sales Forecasting Using Facebook’s Prophet - Nov 28, 2018.
In this tutorial we’ll use Prophet, a package developed by Facebook to show how one can achieve this.
Facebook, Python, Sales, Time Series
- Word Morphing – an original idea - Nov 20, 2018.
In this post, we describe how to utilise word2vec's embeddings and A* search algorithm to morph between words.
NLP, Python, Text Classification
- Mastering The New Generation of Gradient Boosting - Nov 15, 2018.
Catboost, the new kid on the block, has been around for a little more than a year now, and it is already threatening XGBoost, LightGBM and H2O.
Boosting, Gradient Boosting, Machine Learning, Python
What is the Best Python IDE for Data Science? - Nov 14, 2018.
Before you start learning Python, choose the IDE that suits you the best. We examine many available tools, their pros and cons, and suggest how to choose the best Python IDE for you.
Data Science, IDE, Jupyter, Programming, Python
- Healthcare Analytics Made Simple - Nov 12, 2018.
Finally, a book on Python healthcare machine learning techniques is here! Healthcare Analytics Made Simple does just what the title says: it makes healthcare data science simple and approachable for everyone.
Analytics, Book, Healthcare, Pandas, Python
- Multi-Class Text Classification with Doc2Vec & Logistic Regression - Nov 9, 2018.
Doc2vec is an NLP tool for representing documents as a vector and is a generalizing of the word2vec method. In order to understand doc2vec, it is advisable to understand word2vec approach.
Logistic Regression, NLP, Python, Text Classification
- Introduction to PyTorch for Deep Learning - Nov 7, 2018.
In this tutorial, you’ll get an introduction to deep learning using the PyTorch framework, and by its conclusion, you’ll be comfortable applying it to your deep learning models.
Deep Learning, Neural Networks, Python, PyTorch
- Text Preprocessing in Python: Steps, Tools, and Examples - Nov 6, 2018.
We outline the basic steps of text preprocessing, which are needed for transferring text from human language to machine-readable format for further processing. We will also discuss text preprocessing tools.
Pages: 1 2
Data Preparation, NLP, Python, Text Analysis, Text Mining, Tokenization
- Building Surveillance System Using USB Camera and Wireless-Connected Raspberry Pi - Nov 6, 2018.
Read this post to learn how to build a surveillance system using a USB camera plugged into Raspberry Pi (RPi) which is connected a PC using its wireless interface.
Pages: 1 2
Computer Vision, Python, Raspberry Pi, Security, Video recognition
Top 13 Python Deep Learning Libraries - Nov 2, 2018.
Part 2 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
Caffe, Deep Learning, GitHub, MXNet, Python, PyTorch, TensorFlow, Theano
- Multi-Class Text Classification Model Comparison and Selection - Nov 1, 2018.
This is what we are going to do today: use everything that we have presented about text classification in the previous articles (and more) and comparing between the text classification models we trained in order to choose the most accurate one for our problem.
Pages: 1 2
Modeling, NLP, Python, Text Classification
- Introduction to Deep Learning with Keras - Oct 29, 2018.
In this article, we’ll build a simple neural network using Keras. Now let’s proceed to solve a real business problem: an insurance company wants you to develop a model to help them predict which claims look fraudulent.
Pages: 1 2
Deep Learning, Keras, Neural Networks, Python
SQL, Python, & R in One Platform - Oct 26, 2018.
No more jumping between applications. Mode Studio combines a SQL editor, Python and R notebooks, and a visualization builder in one platform.
Data Visualization, Mode Analytics, Python, R, SQL
- Notes on Feature Preprocessing: The What, the Why, and the How - Oct 26, 2018.
This article covers a few important points related to the preprocessing of numeric data, focusing on the scaling of feature values, and the broad question of dealing with outliers.
Data Preparation, Data Preprocessing, numpy, Python, scikit-learn, SciPy
- Naive Bayes from Scratch using Python only – No Fancy Frameworks - Oct 25, 2018.
We provide a complete step by step pythonic implementation of naive bayes, and by keeping in mind the mathematical & probabilistic difficulties we usually face when trying to dive deep in to the algorithmic insights of ML algorithms, this post should be ideal for beginners.
Pages: 1 2
Machine Learning, Naive Bayes, Python
- Get a 2–6x Speed-up on Your Data Pre-processing with Python - Oct 23, 2018.
Get a 2–6x speed-up on your pre-processing with these 3 lines of code!
Data Preprocessing, Efficiency, Programming, Python
- Evaluating the Business Value of Predictive Models in Python and R - Oct 11, 2018.
In these blogs for R and python we explain four valuable evaluation plots to assess the business value of a predictive model. We show how you can easily create these plots and help you to explain your predictive model to non-techies.
Pages: 1 2
Business Value, Data Visualization, Lift charts, Predictive Models, Python, R
10 Best Mobile Apps for Data Scientist / Data Analysts - Oct 10, 2018.
A collection of useful mobile applications that will help enhance your vital data science and analytic skills. These free apps can improve your listening abilities, logical skills, basic leadership qualities and more.
Apps, Data Scientist, Mobile, Python
- Top 8 Python Machine Learning Libraries - Oct 9, 2018.
Part 1 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
GitHub, Keras, Machine Learning, Python
- Basic Image Data Analysis Using Python – Part 4 - Oct 5, 2018.
Accessing the internal component of digital images using Python packages helps the user understand its properties, as well as its nature.
Computer Vision, Image Processing, Python
- Linear Regression in the Wild - Oct 3, 2018.
We take a look at how to use linear regression when the dependent variables have measurement errors.
Algorithms, Linear Regression, Python
How to Create a Simple Neural Network in Python - Oct 2, 2018.
The best way to understand how neural networks work is to create one yourself. This article will demonstrate how to do just that.
Machine Learning, Neural Networks, Python
- Basic Image Data Analysis Using Python – Part 3 - Sep 28, 2018.
Accessing the internal component of digital images using Python packages becomes more convenient to help understand its properties, as well as nature.
Computer Vision, Image Processing, numpy, Python
- Visualising Geospatial data with Python using Folium - Sep 27, 2018.
Folium is a powerful data visualization library in Python that was built primarily to help people visualize geospatial data. With Folium, one can create a map of any location in the world if its latitude and longitude values are known. This guide will help you get started.
Data Visualization, Geospatial, GitHub, Python
- Raspberry Pi IoT Projects for Fun and Profit - Sep 27, 2018.
In this post, I will explain how to run an IoT project from the command line, without graphical interface, using Ubuntu Core in a Raspberry Pi 3.
Pages: 1 2
Data Science, IoT, Python, Raspberry Pi
- Deep Learning Framework Power Scores 2018 - Sep 24, 2018.
Who’s on top in usage, interest, and popularity?
CNTK, Deep Learning, fast.ai, Java, Keras, MXNet, Python, PyTorch, TensorFlow, Theano
- Data Augmentation For Bounding Boxes: Rethinking image transforms for object detection - Sep 19, 2018.
Data Augmentation is one way to battle this shortage of data, by artificially augmenting our dataset. In fact, the technique has proven to be so successful that it's become a staple of deep learning systems.
Pages: 1 2
Deep Learning, Image Recognition, Neural Networks, Object Detection, Python
- Iterative Initial Centroid Search via Sampling for k-Means Clustering - Sep 12, 2018.
Thinking about ways to find a better set of initial centroid positions is a valid approach to optimizing the k-means clustering process. This post outlines just such an approach.
Clustering, K-means, Python, Sampling, scikit-learn
- Machine Learning for Text Classification Using SpaCy in Python - Sep 11, 2018.
In this post, we will demonstrate how text classification can be implemented using spaCy without having any deep learning experience.
NLP, Python, Text Analytics, Text Classification, Text Mining
Journey to Machine Learning – 100 Days of ML Code - Sep 7, 2018.
A personal account from Machine Learning enthusiast Avik Jain on his experiences of #100DaysOfMLCode, a challenge that encourages beginners to code and study machine learning for at least an hour, every day for 100 days.
GitHub, K-nearest neighbors, Machine Learning, Python, SVM
Ultimate Guide to Getting Started with TensorFlow - Sep 6, 2018.
Including video and written tutorials, beginner code examples, useful tricks, helpful communities, books, jobs and more - this is the ultimate guide to getting started with TensorFlow.
Deep Learning, Dropout, Python, TensorFlow
- Financial Data Analysis – Data Processing 1: Loan Eligibility Prediction - Sep 4, 2018.
In this first part I show how to clean and remove unnecessary features. Data processing is very time-consuming, but better data would produce a better model.
Data Preprocessing, Data Processing, Finance, Python
- An End-to-End Project on Time Series Analysis and Forecasting with Python - Sep 3, 2018.
Time series are widely used for non-stationary data, like economic, weather, stock price, and retail sales in this post. We will demonstrate different approaches for forecasting retail sales time series.
Forecasting, Python, Time Series, Trend Detection
- Multi-Class Text Classification with Scikit-Learn - Aug 27, 2018.
The vast majority of text classification articles and tutorials on the internet are binary text classification such as email spam filtering and sentiment analysis. Real world problem are much more complicated than that.
NLP, Python, scikit-learn, Text Classification, Text Mining
- 9 Things You Should Know About TensorFlow - Aug 22, 2018.
A summary of the key points from the Google Cloud Next in San Francisco, "What’s New with TensorFlow?", including neural networks, TensorFlow Lite, data pipelines and more.
Deep Learning, Google, Keras, Machine Learning, Python, TensorFlow
- Basic Statistics in Python: Probability - Aug 21, 2018.
At the most basic level, probability seeks to answer the question, "What is the chance of an event happening?" To calculate the chance of an event happening, we also need to consider all the other events that can occur.
Normal Distribution, Probability, Python, Statistics
- Why Automated Feature Engineering Will Change the Way You Do Machine Learning - Aug 20, 2018.
Automated feature engineering will save you time, build better predictive models, create meaningful features, and prevent data leakage.
Automated Machine Learning, Feature Engineering, Machine Learning, Python
- Introduction to Fraud Detection Systems - Aug 17, 2018.
Using the Python gradient boosting library LightGBM, this article introduces fraud detection systems, with code samples included to help you get started.
Fraud Detection, Gradient Boosting, Python
Auto-Keras, or How You can Create a Deep Learning Model in 4 Lines of Code - Aug 17, 2018.
Auto-Keras is an open source software library for automated machine learning. Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models.
Automated Machine Learning, Keras, Neural Networks, Python
- A Crash Course in MXNet Tensor Basics & Simple Automatic Differentiation - Aug 16, 2018.
This is an overview of some basic functionality of the MXNet ndarray package for creating tensor-like objects, and using the autograd package for performing automatic differentiation.
GPU, MXNet, Python, Tensor
- An Introduction to t-SNE with Python Example - Aug 15, 2018.
In this post we’ll give an introduction to the exploratory and visualization t-SNE algorithm. t-SNE is a powerful dimension reduction and visualization technique used on high dimensional data.
Clustering, Data Visualization, PCA, Python, t-SNE
- Setting up your AI Dev Environment in 5 Minutes - Aug 13, 2018.
Whether you're a novice data science enthusiast setting up TensorFlow for the first time, or a seasoned AI engineer working with terabytes of data, getting your libraries, packages, and frameworks installed is always a struggle. Learn how datmo, an open source python package, helps you get started in minutes.
AI, datmo, Development, Docker, Machine Learning, Python, TensorFlow
- Optimization 101 for Data Scientists - Aug 8, 2018.
We show how to use optimization strategies to make the best possible decision.
Football, Julia, Optimization, Python, R, Sports
- Programming Best Practices For Data Science - Aug 7, 2018.
In this post, I'll go over the two mindsets most people switch between when doing programming work specifically for data science: the prototype mindset and the production mindset.
Best Practices, Data Science, Pandas, Programming, Python
- Only Numpy: Implementing GANs and Adam Optimizer using Numpy - Aug 6, 2018.
This post is an implementation of GANs and the Adam optimizer using only Python and Numpy, with minimal focus on the underlying maths involved.
GANs, Generative Adversarial Network, Neural Networks, numpy, Optimization, Python
- WTF is TF-IDF? - Aug 2, 2018.
Relevant words are not necessarily the most frequent words since stopwords like “the”, “of” or “a” tend to occur very often in many documents.
Information Retrieval, Python, Text Analytics, Text Mining, TF-IDF
Basic Statistics in Python: Descriptive Statistics - Aug 1, 2018.
This article covers defining statistics, descriptive statistics, measures of central tendency, and measures of spread. This article assumes no prior knowledge of statistics, but does require at least a general knowledge of Python.
Descriptive Analytics, Python, Statistics
- Intuitive Ensemble Learning Guide with Gradient Boosting - Jul 30, 2018.
This tutorial discusses the importance of ensemble learning with gradient boosting as a study case.
Ensemble Methods, Gradient Boosting, Python
- Remote Data Science: How to Send R and Python Execution to SQL Server from Jupyter Notebooks - Jul 27, 2018.
Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around.
Jupyter, Machine Learning, Microsoft, Python, R, SQL, SQL Server
Genetic Algorithm Implementation in Python - Jul 24, 2018.
This tutorial will implement the genetic algorithm optimization technique in Python based on a simple example in which we are trying to maximize the output of an equation.
Algorithms, Genetic Algorithm, Python
Cookiecutter Data Science: How to Organize Your Data Science Project - Jul 24, 2018.
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
Data Science, Programming, Project, Python
- Comparison of Top 6 Python NLP Libraries - Jul 23, 2018.
Today, we want to outline and compare the most popular and helpful natural language processing libraries, based on our experience.
NLP, Python
- Receiver Operating Characteristic Curves Demystified (in Python) - Jul 20, 2018.
In this blog, I will reveal, step by step, how to plot an ROC curve using Python. After that, I will explain the characteristics of a basic ROC curve.
Machine Learning, Metrics, Python, ROC-AUC
- Explaining the 68-95-99.7 rule for a Normal Distribution - Jul 19, 2018.
This post explains how those numbers were derived in the hope that they can be more interpretable for your future endeavors.
Data Analysis, Data Science, Normal Distribution, Python, Statistics
- 5 Quick and Easy Data Visualizations in Python with Code - Jul 18, 2018.
This post provides an overview of a small number of widely used data visualizations, and includes code in the form of functions to implement each in Python using Matplotlib.
Data Visualization, Matplotlib, Python
- Basic Image Processing in Python, Part 2 - Jul 17, 2018.
We explain how to easily access and manipulate the internal components of digital images using Python and give examples from satellite image processing.
Computer Vision, Image Processing, numpy, Python
- Basic Image Data Analysis Using Numpy and OpenCV – Part 1 - Jul 10, 2018.
Accessing the internal component of digital images using Python packages becomes more convenient to understand its properties as well as nature.
Computer Vision, Image Processing, numpy, OpenCV, Python
- Analyze a Soccer (Football) Game Using Tensorflow Object Detection and OpenCV - Jul 10, 2018.
For the data scientist within you let's use this opportunity to do some analysis on soccer clips. With the use of deep learning and opencv we can extract interesting insights from video clips
Football, Image Recognition, Object Detection, OpenCV, Python, Soccer, TensorFlow, Video recognition, World Cup
- Text Classification & Embeddings Visualization Using LSTMs, CNNs, and Pre-trained Word Vectors - Jul 5, 2018.
In this tutorial, I classify Yelp round-10 review datasets. After processing the review comments, I trained three model in three different ways and obtained three word embeddings.
Convolutional Neural Networks, Keras, LSTM, NLP, Python, Text Classification, Word Embeddings
- Deep Quantile Regression - Jul 3, 2018.
Most Deep Learning frameworks currently focus on giving a best estimate as defined by a loss function. Occasionally something beyond a point estimate is required to make a decision. This is where a distribution would be useful. This article will purely focus on inferring quantiles.
Deep Learning, Hyperparameter, Keras, Neural Networks, Python, Regression
- Inside the Mind of a Neural Network with Interactive Code in Tensorflow - Jun 29, 2018.
Understand the inner workings of neural network models as this post covers three related topics: histogram of weights, visualizing the activation of neurons, and interior / integral gradients.
Pages: 1 2
Convolutional Neural Networks, Image Recognition, Neural Networks, Python, TensorFlow
- Building a Basic Keras Neural Network Sequential Model - Jun 29, 2018.
The approach basically coincides with Chollet's Keras 4 step workflow, which he outlines in his book "Deep Learning with Python," using the MNIST dataset, and the model built is a Sequential network of Dense layers. A building block for additional posts.
Keras, MNIST, Neural Networks, Python
Top 20 Python Libraries for Data Science in 2018 - Jun 27, 2018.
Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it's difficult to distinguish one particular leader at the moment.
Pages: 1 2
Bokeh, Data Science, Keras, Matplotlib, NLTK, numpy, Pandas, Plotly, Python, PyTorch, scikit-learn, SciPy, Seaborn, TensorFlow, XGBoost
- How to Execute R and Python in SQL Server with Machine Learning Services - Jun 25, 2018.
Machine Learning Services in SQL Server eliminates the need for data movement - you can install and run R/Python packages to build Deep Learning and AI applications on data in SQL Server.
Azure ML, Machine Learning, Microsoft, Python, R, SQL, SQL Server
- Step Forward Feature Selection: A Practical Example in Python - Jun 18, 2018.
When it comes to disciplined approaches to feature selection, wrapper methods are those which marry the feature selection process to the type of model being built, evaluating feature subsets in order to detect the model performance between features, and subsequently select the best performing subset.
Feature Selection, Machine Learning, Python
- Generating Text with RNNs in 4 Lines of Code - Jun 14, 2018.
Want to generate text with little trouble, and without building and tuning a neural network yourself? Let's check out a project which allows you to "easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code."
Donald Trump, LSTM, NLP, Python, Recurrent Neural Networks, Twitter
- Packaging and Distributing Your Python Project to PyPI for Installation Using pip - Jun 11, 2018.
This tutorial will explain the steps required to package your Python projects, distribute them in distribution formats using steptools, upload them into the Python Package Index (PyPI) repository using twine, and finally installation using Python installers such as pip and conda.
Pages: 1 2
Distribution, Project, Python
- DIY Deep Learning Projects - Jun 8, 2018.
Inspired by the great work of Akshay Bahadur in this article you will see some projects applying Computer Vision and Deep Learning, with implementations and details so you can reproduce them on your computer.
Computer Vision, Data Science, Deep Learning, LinkedIn, Neural Networks, OpenCV, Python
The 6 components of Open-Source Data Science/ Machine Learning Ecosystem; Did Python declare victory over R? - Jun 6, 2018.
We find 6 tools form the modern open source Data Science / Machine Learning ecosystem; examine whether Python declared victory over R; and review which tools are most associated with Deep Learning and Big Data.
Anaconda, Apache Spark, Data Science, Keras, Machine Learning, Open Source, Poll, Python, R, RapidMiner, Scala, scikit-learn, TensorFlow
- The Keras 4 Step Workflow - Jun 4, 2018.
In his book "Deep Learning with Python," Francois Chollet outlines a process for developing neural networks with Keras in 4 steps. Let's take a look at this process with a simple example.
Francois Chollet, Keras, Neural Networks, Python, Workflow
- Overview of Dash Python Framework from Plotly for building dashboards - May 31, 2018.
Introduction to Dash framework from Plotly, reactive framework for building dashboards in Python. Tech talk covers basics and more advanced topics like custom component and scaling.
Dashboard, Data Analytics, Data Visualization, Plotly, Python
- Learn AI and Data Science rapidly based only on high school math – KDnuggets Offer - May 25, 2018.
This 3-month program, created by Ajit Jaokar, who teaches at Oxford, is interactive and delivered by video. Coding examples are in Python. Places limited - check special KDnuggets rate.
AI, Ajit Jaokar, Data Science Education, Mathematics, Online Education, Python
Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018: Trends and Analysis - May 22, 2018.
Python continues to eat away at R, RapidMiner gains, SQL is steady, Tensorflow advances pulling along Keras, Hadoop drops, Data Science platforms consolidate, and more.
Pages: 1 2
Anaconda, Data Mining Software, Data Science Platform, Hadoop, Keras, Poll, Python, R, RapidMiner, SQL, TensorFlow, Trends
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.
Computer Vision, Image Recognition, Neural Networks, Object Detection, Python, PyTorch, YOLO
- 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.
GANs, Generative Adversarial Network, GitHub, Neural Networks, Python, Rubens Zimbres, TensorFlow
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.
Pages: 1 2
API, Convolutional Neural Networks, Dropout, Flask, Neural Networks, Python, RESTful API, TensorFlow
- 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.
Python, PyTorch
- 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.
GPU, Python, PyTorch, Tensor
- Torus for Docker-First Data Science - May 8, 2018.
To help data science teams adopt Docker and apply DevOps best practices to streamline machine learning delivery pipelines, we open-sourced a toolkit based on the popular cookiecutter project structure.
Data Science, DevOps, Docker, Machine Learning Engineer, Open Source, Python
- Top Data Science, Machine Learning Courses from Udemy – May 2018 - May 8, 2018.
Learn Machine Learning, Data Science, Python, Azure Machine Learning, and more with Udemy Mother's Day $9.99 sale - get top courses from leading instructors.
Azure ML, Data Science, Machine Learning, Python, Udemy
- Apache Spark : Python vs. Scala - May 4, 2018.
When it comes to using the Apache Spark framework, the data science community is divided in two camps; one which prefers Scala whereas the other preferring Python. This article compares the two, listing their pros and cons.
Apache Spark, Java, Python, Scala
- 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.
Data Science, Linear Algebra, Mathematics, numpy, Python
- Hands-on: Intro to Python for Data Analysis - May 2, 2018.
Learn one of the top languages used in data science and machine learning with this new hands-on course by TDWI Online Learning.
Data Analysis, Online Education, Python, TDWI
- 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.
Data Preparation, Data Preprocessing, NLP, Python, Text Analytics, Text Mining
- KDnuggets™ News 18:n18, May 2: Blockchain Explained in 7 Python Functions; Data Science Dirty Secret; Choosing the Right Evaluation Metric - May 2, 2018.
Also: Building Convolutional Neural Network using NumPy from Scratch; Data Science Interview Guide; Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model; Jupyter Notebook for Beginners: A Tutorial
Blockchain, Convolutional Neural Networks, Data Science, Machine Learning, Metrics, numpy, Python
- 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.
Pages: 1 2
Data Analysis, GitHub, Jupyter, Matplotlib, Python
- 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.
Facebook, Feature Engineering, NLP, Python
Blockchain Explained in 7 Python Functions - Apr 27, 2018.
It wasn’t until I wrote my own simple Blockchain, that I truly understood what it is and the potential applications for it. So without further ado, lets set up our 7 functions!
Blockchain, Encryption, Python
Building Convolutional Neural Network using NumPy from Scratch - Apr 26, 2018.
In this article, CNN is created using only NumPy library. Just three layers are created which are convolution (conv for short), ReLU, and max pooling.
Convolutional Neural Networks, Image Recognition, Neural Networks, numpy, Python
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model - Apr 25, 2018.
The GloVe model stands for Global Vectors which is an unsupervised learning model which can be used to obtain dense word vectors similar to Word2Vec.
Deep Learning, Feature Engineering, NLP, Python, Text Mining
- Neural Network based Startup Name Generator - Apr 20, 2018.
How to build a recurrent neural network to generate suggestions for your new company’s name.
Neural Networks, Python, Startups
- Python Regular Expressions Cheat Sheet - Apr 19, 2018.
The tough thing about learning data is remembering all the syntax. While at Dataquest we advocate getting used to consulting the Python documentation, sometimes it's nice to have a handy reference, so we've put together this cheat sheet to help you out!
Cheat Sheet, Programming, Python, Text Analytics
- Robust Word2Vec Models with Gensim & Applying Word2Vec Features for Machine Learning Tasks - Apr 17, 2018.
The gensim framework, created by Radim Řehůřek consists of a robust, efficient and scalable implementation of the Word2Vec model.
Feature Engineering, NLP, Python, Word Embeddings, word2vec
- Getting Started with PyTorch Part 1: Understanding How Automatic Differentiation Works - Apr 11, 2018.
PyTorch has emerged as a major contender in the race to be the king of deep learning frameworks. What makes it really luring is it’s dynamic computation graph paradigm.
Pages: 1 2
Deep Learning, Neural Networks, Python, PyTorch
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The Skip-gram Model - Apr 10, 2018.
Just like we discussed in the CBOW model, we need to model this Skip-gram architecture now as a deep learning classification model such that we take in the target word as our input and try to predict the context words.
Deep Learning, Feature Engineering, NLP, Python, Text Mining, Word Embeddings
- Why You Should Start Using .npy Files More Often - Apr 3, 2018.
In this article, we demonstrate the utility of using native NumPy file format .npy over CSV for reading large numerical data set. It may be an useful trick if the same CSV data file needs to be read many times.
numpy, Python
- Using Tensorflow Object Detection to do Pixel Wise Classification - Mar 29, 2018.
Tensorflow recently added new functionality and now we can extend the API to determine pixel by pixel location of objects of interest. So when would we need this extra granularity?
Classification, Image Recognition, Object Detection, Python, TensorFlow
- Understanding Feature Engineering: Deep Learning Methods for Text Data - Mar 28, 2018.
Newer, advanced strategies for taming unstructured, textual data: In this article, we will be looking at more advanced feature engineering strategies which often leverage deep learning models.
Deep Learning, Feature Engineering, NLP, Python, Text Mining
Text Data Preprocessing: A Walkthrough in Python - Mar 26, 2018.
This post will serve as a practical walkthrough of a text data preprocessing task using some common Python tools.
Data Preparation, Data Preprocessing, NLP, Python, Text Analytics, Text Mining
- Quick Feature Engineering with Dates Using fast.ai - Mar 16, 2018.
The fast.ai library is a collection of supplementary wrappers for a host of popular machine learning libraries, designed to remove the necessity of writing your own functions to take care of some repetitive tasks in a machine learning workflow.
fast.ai, Feature Engineering, Machine Learning, Pandas, Python, Time Series
- Web Scraping with Python: Illustration with CIA World Factbook - Mar 16, 2018.
In this article, we show how to use Python libraries and HTML parsing to extract useful information from a website and answer some important analytics questions afterwards.
Analytics, CIA, Python, Web Scraping
- Creating a simple text classifier using Google CoLaboratory - Mar 15, 2018.
Google CoLaboratory is Google’s latest contribution to AI, wherein users can code in Python using a Chrome browser in a Jupyter-like environment. In this article I have shared a method, and code, to create a simple binary text classifier using Scikit Learn within Google CoLaboratory environment.
Google, Google Colab, Jupyter, Python, Text Classification
- A Beginner’s Guide to Data Engineering – Part II - Mar 15, 2018.
In this post, I share more technical details on how to build good data pipelines and highlight ETL best practices. Primarily, I will use Python, Airflow, and SQL for our discussion.
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AirBnB, Data Engineering, Data Science, ETL, Pipeline, Python, SQL
Top 5 Best Jupyter Notebook Extensions - Mar 13, 2018.
Check out these 5 Jupyter notebook extensions to help increase your productivity.
Jupyter, Python
- 5 Fantastic Practical Natural Language Processing Resources - Feb 22, 2018.
This post presents 5 practical resources for getting a start in natural language processing, covering a wide array of topics and approaches.
Deep Learning, Keras, LSTM, Neural Networks, NLP, NLTK, Python
- Top 20 Python AI and Machine Learning Open Source Projects - Feb 20, 2018.
We update the top AI and Machine Learning projects in Python. Tensorflow has moved to the first place with triple-digit growth in contributors. Scikit-learn dropped to 2nd place, but still has a very large base of contributors.
GitHub, Machine Learning, Open Source, Python, scikit-learn, TensorFlow
Deep Learning Development with Google Colab, TensorFlow, Keras & PyTorch - Feb 20, 2018.
Now you can develop deep learning applications with Google Colaboratory - on the free Tesla K80 GPU - using Keras, Tensorflow and PyTorch.
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Deep Learning, Google, Google Colab, Keras, Python, PyTorch, TensorFlow
- 3 Essential Google Colaboratory Tips & Tricks - Feb 12, 2018.
Google Colaboratory is a promising machine learning research platform. Here are 3 tips to simplify its usage and facilitate using a GPU, installing libraries, and uploading data files.
Google, Google Colab, Python, TensorFlow, Tips
- 5 Machine Learning Projects You Should Not Overlook - Feb 8, 2018.
It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
Bayesian, Gradient Boosting, Keras, Machine Learning, Overlook, PHP, Python, scikit-learn
5 Fantastic Practical Machine Learning Resources - Feb 6, 2018.
This post presents 5 fantastic practical machine learning resources, covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks.
Deep Learning, fast.ai, Gluon, Machine Learning, MOOC, MXNet, Python
- A Simple Starter Guide to Build a Neural Network - Feb 5, 2018.
This guide serves as a basic hands-on work to lead you through building a neural network from scratch. Most of the mathematical concepts and scientific decisions are left out.
Machine Learning, Neural Networks, Python, PyTorch
- Web Scraping Tutorial with Python: Tips and Tricks - Feb 1, 2018.
This post is intended for people who are interested to know about the common design patterns, pitfalls and rules related to the web scraping.
BeautifulSoup, Python, Tips, Web Scraping
- Using AutoML to Generate Machine Learning Pipelines with TPOT - Jan 29, 2018.
This post will take a different approach to constructing pipelines. Certainly the title gives away this difference: instead of hand-crafting pipelines and hyperparameter optimization, and performing model selection ourselves, we will instead automate these processes.
Automated Machine Learning, Hyperparameter, Optimization, Pipeline, Python, scikit-learn, Workflow
- Managing Machine Learning Workflows with Scikit-learn Pipelines Part 3: Multiple Models, Pipelines, and Grid Searches - Jan 24, 2018.
In this post, we will be using grid search to optimize models built from a number of different types estimators, which we will then compare and properly evaluate the best hyperparameters that each model has to offer.
Data Preprocessing, Hyperparameter, Optimization, Pipeline, Python, scikit-learn, Workflow
- Using Genetic Algorithm for Optimizing Recurrent Neural Networks - Jan 22, 2018.
In this tutorial, we will see how to apply a Genetic Algorithm (GA) for finding an optimal window size and a number of units in Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN).
Automated Machine Learning, Genetic Algorithm, Keras, Neural Networks, Python, Recurrent Neural Networks
- Managing Machine Learning Workflows with Scikit-learn Pipelines Part 2: Integrating Grid Search - Jan 19, 2018.
Another simple yet powerful technique we can pair with pipelines to improve performance is grid search, which attempts to optimize model hyperparameter combinations.
Data Preprocessing, Hyperparameter, Optimization, Pipeline, Python, scikit-learn, Workflow
- Gradient Boosting in TensorFlow vs XGBoost - Jan 18, 2018.
For many Kaggle-style data mining problems, XGBoost has been the go-to solution since its release in 2016. It's probably as close to an out-of-the-box machine learning algorithm as you can get today.
Gradient Boosting, Python, TensorFlow, XGBoost
- Is Learning Rate Useful in Artificial Neural Networks? - Jan 15, 2018.
This article will help you understand why we need the learning rate and whether it is useful or not for training an artificial neural network. Using a very simple Python code for a single layer perceptron, the learning rate value will get changed to catch its idea.
Hyperparameter, Neural Networks, Python
- Simple Ways Of Working With Medium To Big Data Locally - Dec 27, 2017.
An overview of the installation and implementation of simple techniques for working with large datasets in your machine.
Big Data, iPhone, Python, R, SAS
- Getting Started with TensorFlow: A Machine Learning Tutorial - Dec 19, 2017.
A complete and rigorous introduction to Tensorflow. Code along with this tutorial to get started with hands-on examples.
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Machine Learning, Python, TensorFlow
- Accelerating Algorithms: Considerations in Design, Algorithm Choice and Implementation - Dec 18, 2017.
If you are trying to make your algorithms run faster, you may want to consider reviewing some important points on design and implementation.
ActiveState, Algorithms, Implementation, Python
- Building an Audio Classifier using Deep Neural Networks - Dec 15, 2017.
Using a deep convolutional neural network architecture to classify audio and how to effectively use transfer learning and data-augmentation to improve model accuracy using small datasets.
Acoustics, Audio, Deep Learning, Python, Speech, Speech Recognition, Transfer Learning
- How to Generate FiveThirtyEight Graphs in Python - Dec 14, 2017.
In this post, we'll help you. Using Python's matplotlib and pandas, we'll see that it's rather easy to replicate the core parts of any FiveThirtyEight (FTE) visualization.
Data Visualization, Dataquest, FiveThirtyEight, Python
