- 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.
- KDnuggets™ News 18:n21, May 23: Python eats away at R; Top 2018 Analytics, Data Science, Machine Learning tools; 9 Must-have skills for a Data Scientist - May 23, 2018.
Also How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch; Frameworks for Approaching the Machine Learning Process.
- 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.
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- Top Stories, May 14-20: Data Science vs Machine Learning vs Data Analytics vs Business Analytics; Implement a YOLO Object Detector from Scratch in PyTorch - May 21, 2018.
Also: An Introduction to Deep Learning for Tabular Data; 9 Must-have skills you need to become a Data Scientist, updated; GANs in TensorFlow from the Command Line: Creating Your First GitHub Project; Complete Guide to Build ConvNet HTTP-Based Application
- Kernel Machine Learning (KernelML) - Generalized Machine Learning Algorithm - May 18, 2018.
This article introduces a pip Python package called KernelML, created to give analysts and data scientists a generalized machine learning algorithm for complex loss functions and non-linear coefficients.
- 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.
- KDnuggets™ News 18:n20, May 16: PyTorch Tensor Basics; Data Science in Finance; Executive Guide to Data Science - May 16, 2018.
PyTorch Tensor Basics; Top 7 Data Science Use Cases in Finance; The Executive Guide to Data Science and Machine Learning; Data Augmentation: How to use Deep Learning when you have Limited Data
- 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.
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- 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.
- Unleash a faster Python on Your Data. - May 10, 2018.
Get real performance results and download the free Intel(r) Distribution for Python that includes everything you need for blazing-fast computing, analytics, machine learning, and more.
- 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.
- 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.
- 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.
- WTF is a Tensor?!? - May 7, 2018.
A tensor is a container which can house data in N dimensions, along with its linear operations, though there is nuance in what tensors technically are and what we refer to as tensors in practice.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
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- 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.
- Pair Finance: Python Developer - Apr 30, 2018.
Seeking a Python Developer to work on a completely new and innovative product we are building, along with a small team of other experienced developers, and to collaborate on an iterative design process from a basic prototype to the first production version.
- Choosing the Right Metric for Evaluating Machine Learning Models – Part 1 - Apr 27, 2018.
Each machine learning model is trying to solve a problem with a different objective using a different dataset and hence, it is important to understand the context before choosing a metric.
- 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!
- 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.
- 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.
- KDnuggets™ News 18:n17, Apr 25: Python Regular Expressions Cheat Sheet; Deep Learning With Apache Spark; Building a Question Answering Model - Apr 25, 2018.
Also: Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step; Presto for Data Scientists - SQL on anything; Why Deep Learning is perfect for NLP (Natural Language Processing); Top 16 Open Source Deep Learning Libraries and Platforms
- How I Unknowingly Contributed To Open Source - Apr 24, 2018.
This article explains what is meant by the term 'open source' and why all data scientists should be a part of it.
- Swiftapply – Automatically efficient pandas apply operations - Apr 24, 2018.
Using Swiftapply, easily apply any function to a pandas dataframe in the fastest available manner.
- 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.
- 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!
- 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.
- 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.
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- 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.
- Top Data Science, Machine Learning Courses from Udemy – April 2018 - Apr 5, 2018.
Udemy April $10.99 sale is now going on top courses from leading instructors and learn Machine Learning, Data Science, Python, Azure Machine Learning, and more.
- 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.
- 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?
- 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.
- 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.
- 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.
- 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.
- 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.
- 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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- Top 5 Best Jupyter Notebook Extensions - Mar 13, 2018.
Check out these 5 Jupyter notebook extensions to help increase your productivity.
- Top Data Science, Machine Learning Courses from Udemy – March 2018 - Mar 12, 2018.
Udemy St Patrick's Day $11.99 sale on top courses from leading instructors and learn Machine Learning, Data Science, Python, Azure Machine Learning, and more.
- KDnuggets™ News 18:n10, Mar 7: Functional Programming in Python; Surviving Your Data Science Interview; Easy Image Recognition with Google Tensorflow - Mar 7, 2018.
- TDWI Chicago, May 6-11: Get Your Hands Dirty With Data – KDnuggets Offer - Mar 2, 2018.
Attend the Hands-on Lab series and bring practical skills back from Chicago. Save 30% through March 16 with priority code KD30.
- Unleash a faster Python on your data - Mar 1, 2018.
Get real performance results and download the free Intel Distribution for Python that includes everything you need for blazing-fast computing, analytics, machine learning, and more.
- Top KDnuggets tweets, Feb 21-27: Top 20 Python #AI and #MachineLearning Open Source Projects; Intro to Reinforcement Learning Algorithms - Feb 28, 2018.
Also: #NeuralNetwork #AI is simple. So... Stop pretending; 5 Free Resources for Getting Started with #DeepLearning for Natural Language Pro; Want a Job in #Data? Learn This
- Introduction to Functional Programming in Python - Feb 28, 2018.
Python facilitates different approaches to writing code, and while an object-oriented approach is common, an alternative and useful style of writing code is functional programming.
Pages: 1 2
- 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.
- 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.
- 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.
Pages: 1 2
- KDnuggets™ News 18:n07, Feb 14: 5 Machine Learning Projects You Should Not Overlook; Intro to Python Ensembles - Feb 14, 2018.
5 Machine Learning Projects You Should Not Overlook; Introduction to Python Ensembles; Which Machine Learning Algorithm be used in year 2118?; Fast.ai Lesson 1 on Google Colab (Free GPU)
- 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.
- Introduction to Python Ensembles - Feb 9, 2018.
In this post, we'll take you through the basics of ensembles — what they are and why they work so well — and provide a hands-on tutorial for building basic ensembles.
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- 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!
- KDnuggets™ News 18:n06, Feb 7: 5 Fantastic Practical Machine Learning Resources; 8 Must-Know Neural Network Architectures - Feb 7, 2018.
5 Fantastic Practical Machine Learning Resources; The 8 Neural Network Architectures Machine Learning Researchers Need to Learn; Generalists Dominate Data Science; Avoid Overfitting with Regularization; Understanding Learning Rates and How It Improves Performance in Deep Learning
- 2018 Predictions for the Analytics & Data Science Hiring Market - Feb 6, 2018.
What do you think of this year’s predictions? Do you see any new tools on the horizon, or do you believe data science popularity is due for a reckoning of sorts?
- 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.
- 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.
- 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.
- 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.
- 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.
- Using Excel with Pandas - Jan 23, 2018.
In this tutorial, we are going to show you how to work with Excel files in pandas, covering computer setup, reading in data from Excel files into pandas, data exploration in pandas, and more.
Pages: 1 2
- 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).
- 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.
- Are you monitoring your machine learning systems? - Jan 18, 2018.
How are you monitoring your Python applications? Take the short survey - the results will be published on KDnuggets and you will get all the details.
- 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.
- 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.
- Top Data Science, Machine Learning Courses from Udemy - Jan 5, 2018.
Enjoy the New Year sale on top courses from leading instructors and learn Machine Learning, Data Science, Python, Azure Machine Learning, and more.
- 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.
- Top KDnuggets tweets, Dec 13-19: The Art of Learning Data Science; Data Science, ML Main Developments, Key Trends - Dec 20, 2017.
The Art of Learning #DataScience; How to Generate FiveThirtyEight Graphs in #Python; #TensorFlow for Short-Term Stocks Prediction; 15 Mathematics MOOCs for #DataScience.
- KDnuggets™ News 17:n48, Dec 20: Machine Learning 2017 Key Trends; New Poll: When is AGI Coming?; AI Year End Roundup - Dec 20, 2017.
Machine Learning & Artificial Intelligence: Main Developments in 2017 and Key Trends in 2018; New Poll: When will Artificial General Intelligence (AGI) be achieved?; Xavier Amatriain's Machine Learning and Artificial Intelligence Year-end Roundup; How to Generate FiveThirtyEight Graphs in Python; Transitioning to Data Science: How to become a data scientist
- $5 Data science eBooks and videos from Packt - Dec 19, 2017.
Check Packt $5 sale on every ebook and video, including many great titles on Data Analysis, Machine Learning, Python, Deep Learning, and more - sale runs until Jan 15, 2018.
- 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.
Pages: 1 2
- 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.
- 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.
- Best Data Science, Machine Learning Courses from Udemy, only $10 until Dec 21 - Dec 14, 2017.
Holiday Dev & IT sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until Dec 21, 2017.
- 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.
- TensorFlow for Short-Term Stocks Prediction - Dec 12, 2017.
In this post you will see an application of Convolutional Neural Networks to stock market prediction, using a combination of stock prices with sentiment analysis.
- Robust Algorithms for Machine Learning - Dec 11, 2017.
This post mentions some of the advantages of implementing robust, non-parametric methods into our Machine Learning frameworks and models.
- Today I Built a Neural Network During My Lunch Break with Keras - Dec 8, 2017.
So yesterday someone told me you can build a (deep) neural network in 15 minutes in Keras. Of course, I didn’t believe that at all. So the next day I set out to play with Keras on my own data.
- Unleash a faster Python on your data - Dec 7, 2017.
Get real performance results and download the free Intel® Distribution for Python that includes everything you need for blazing-fast computing, analytics, machine learning, and more. Use Intel Python with existing code, and you’re all set for a significant performance boost.
- Managing Machine Learning Workflows with Scikit-learn Pipelines Part 1: A Gentle Introduction - Dec 7, 2017.
Scikit-learn's Pipeline class is designed as a manageable way to apply a series of data transformations followed by the application of an estimator.
- Web Scraping for Data Science with Python - Dec 6, 2017.
We take a quick look at how web scraping can be useful in the context of data science projects, eg to construct a social graph based of S&P 500 companies, using Python and Gephi.
- Exploring Recurrent Neural Networks - Dec 1, 2017.
We explore recurrent neural networks, starting with the basics, using a motivating weather modeling problem, and implement and train an RNN in TensorFlow.
- Machine Learning with Optimus on Apache Spark - Nov 30, 2017.
The way most Machine Learning models work on Spark are not straightforward, and they need lots of feature engineering to work. That’s why we created the feature engineering section inside the Optimus Data Frame Transformer.
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- Why You Should Forget ‘for-loop’ for Data Science Code and Embrace Vectorization - Nov 29, 2017.
Data science needs fast computation and transformation of data. NumPy objects in Python provides that advantage over regular programming constructs like for-loop. How to demonstrate it in few easy lines of code?
- How To Unit Test Machine Learning Code - Nov 28, 2017.
One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time.
- Taming the Python Visualization Jungle, Nov 29 Webinar - Nov 22, 2017.
Python has a ton of plotting libraries—but which ones should you use? And how should you go about choosing them? This webinar shows you key starting points and demonstrates how to solve a range of common problems.
- How (& Why) Data Scientists and Data Engineers Should Share a Platform - Nov 17, 2017.
Sharing one platform has some obvious benefits for Data Science and Data Engineering teams, but technical, language and process challenges often make this a challenge. Learn how one company implemented single cloud platform for R, Python and other workloads – and some of the unexpected benefits they discovered along the way.
- Best Data Science, Machine Learning Courses from Udemy, only $10 until Nov 28- Black Friday/Cybermonday sale - Nov 17, 2017.
Black Friday/Cybermonday sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until Nov 28, 2017.
- Top 10 Videos on Deep Learning in Python - Nov 17, 2017.
Playlists, individual tutorials (not part of a playlist) and online courses on Deep Learning (DL) in Python using the Keras, Theano, TensorFlow and PyTorch libraries. Assumes no prior knowledge. These videos cover all skill levels and time constraints!
- The Python Graph Gallery - Nov 16, 2017.
Welcome to the Python Graph Gallery, a website that displays hundreds of python charts with their reproducible code snippets.
- PySpark SQL Cheat Sheet: Big Data in Python - Nov 16, 2017.
PySpark is a Spark Python API that exposes the Spark programming model to Python - With it, you can speed up analytic applications. With Spark, you can get started with big data processing, as it has built-in modules for streaming, SQL, machine learning and graph processing.
Pages: 1 2
- TensorFlow: What Parameters to Optimize? - Nov 9, 2017.
Learning TensorFlow Core API, which is the lowest level API in TensorFlow, is a very good step for starting learning TensorFlow because it let you understand the kernel of the library. Here is a very simple example of TensorFlow Core API in which we create and train a linear regression model.
- Top KDnuggets tweets, Nov 01-07: Airbnb develops an #AI which converts design into source code - Nov 8, 2017.
Also: One LEGO at a time: Explaining the #Math of How #NeuralNetworks Learn; 6 Books Every #DataScientist Should Keep Nearby; Direct from Sebastian Raschka #Python #MachineLearning book, new edition.
- Tips for Getting Started with Text Mining in R and Python - Nov 8, 2017.
This article opens up the world of text mining in a simple and intuitive way and provides great tips to get started with text mining.
- 7 Steps to Mastering Deep Learning with Keras - Oct 30, 2017.
Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible.
- Best Data Science, Machine Learning Courses from Udemy (only $12 until Oct 31) - Oct 27, 2017.
Fall sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $12 until Oct 31, 2017.
- Ranking Popular Deep Learning Libraries for Data Science - Oct 23, 2017.
We rank 23 open-source deep learning libraries that are useful for Data Science. The ranking is based on equally weighing its three components: Github and Stack Overflow activity, as well as Google search results.
- Data Science Bootcamp in Zurich, Switzerland, January 15 – April 6, 2018 - Oct 12, 2017.
Come to the land of chocolate and Data Science where the local tech scene is booming and the jobs are a plenty. Learn the most important concepts from top instructors by doing and through projects. Use code KDNUGGETS to save.
- Best practices of orchestrating Python and R code in ML projects - Oct 12, 2017.
Instead of arguing about Python vs R I will examine the best practices of integrating both languages in one data science project.
Pages: 1 2
- How I started with learning AI in the last 2 months - Oct 9, 2017.
The relevance of a full stack developer will not be enough in the changing scenario of things. In the next two years, full stack will not be full stack without AI skills.
- Neural Networks: Innumerable Architectures, One Fundamental Idea - Oct 4, 2017.
At the end of this post, you’ll be able to implement a neural network to identify handwritten digits using the MNIST dataset and have a rough time idea about how to build your own neural networks.
Pages: 1 2
- Top 10 Videos on Machine Learning in Finance - Sep 29, 2017.
Talks, tutorials and playlists – you could not get a more gentle introduction to Machine Learning (ML) in Finance. Got a quick 4 minutes or ready to study for hours on end? These videos cover all skill levels and time constraints!
- Tensorflow Tutorial, Part 2 – Getting Started - Sep 28, 2017.
This tutorial will lay a solid foundation to your understanding of Tensorflow, the leading Deep Learning platform. The second part shows how to get started, install, and build a small test case.
- Keras Cheat Sheet: Deep Learning in Python - Sep 27, 2017.
Keras is a Python deep learning library for Theano and TensorFlow. The package is easy to use and powerful, as it provides users with a high-level neural networks API to develop and evaluate deep learning models.
Pages: 1 2
- Spark – The Definitive Guide – exclusive preview - Sep 25, 2017.
Get an exclusive preview of "Spark: The Definitive Guide" from Databricks! Learn how Spark runs on a cluster, see examples in SQL, Python and Scala, Learn about Structured Streaming and Machine Learning and more.
- Python Data Preparation Case Files: Group-based Imputation - Sep 25, 2017.
The second part in this series addresses group-based imputation for dealing with missing data values. Check out why finding group means can be a more formidable action than overall means, and see how to accomplish it in Python.
- The Easy Button for R & Python on Spark, Webinar Oct 18 - Sep 22, 2017.
Learn five solid reasons to use managed services for Cloudera for R, Python and other advanced analytics on Spark & Hadoop in the cloud.
- Putting Machine Learning in Production - Sep 22, 2017.
In machine learning, going from research to production environment requires a well designed architecture. This blog shows how to transfer a trained model to a prediction server.
- 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets - Sep 22, 2017.
This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools.
Pages: 1 2 3
- Top KDnuggets tweets, Sep 13-19: Top Books on NLP; What Else Can AI Guess From Your Face? - Sep 20, 2017.
Also: The Ten Fallacies of Data Science; #Python #Pandas tips and tricks; Geoff Hinton says we need to start all over.
- 5 Machine Learning Projects You Can No Longer Overlook – Episode VI - Sep 20, 2017.
Deep learning, data preparation, data visualization, oh my! Check out the latest installation of '5 Machine Learning Projects You Can No Longer Overlook' for insight on... well, what machine learning projects you can no longer overlook.
- Keras Tutorial: Recognizing Tic-Tac-Toe Winners with Neural Networks - Sep 18, 2017.
In this tutorial, we will build a neural network with Keras to determine whether or not tic-tac-toe games have been won by player X for given endgame board configurations. Introductory neural network concerns are covered.
- Best Data Science, Machine Learning Courses from Udemy (only $12 until Sep 20) - Sep 14, 2017.
Back-to-school sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $12 until Sep 20, 2017.
- Python Data Preparation Case Files: Removing Instances & Basic Imputation - Sep 14, 2017.
This is the first of 3 posts to cover imputing missing values in Python using Pandas. The slowest-moving of the series (out of necessity), this first installment lays out the task and data at the risk of boring you. The next 2 posts cover group- and regression-based imputation.
- Top KDnuggets tweets, Sep 06-12: Visualizing Cross-validation Code; Intro to #Blockchain and #BigData - Sep 13, 2017.
Also: WTF #Python - A collection of interesting and tricky Python examples; Thoughts after taking @AndrewYNg #Deeplearning #ai course; Another #Keras Tutorial For #NeuralNetwork Beginners.
- KDnuggets™ News 17:n35, Sep 13: Putting the “Science” Back in Data Science; Python vs. R: And the leader is… - Sep 13, 2017.
Putting the "Science" Back in Data Science; Python vs R - Who Is Really Ahead in Data Science, Machine Learning; I built a chatbot in 2 hours and this is what I learned; Are Data Lakes Fake News?; Python Overtaking R?
- Python vs R – Who Is Really Ahead in Data Science, Machine Learning? - Sep 12, 2017.
We examine Google Trends, job trends, and more and note that while Python has only a small advantage among current Data Science and Machine Learning related jobs, this advantage is likely to increase in the future.
- Python vs R for Artificial Intelligence, Machine Learning, and Data Science - Sep 11, 2017.
This is a summary (with links) of a three-part article series that's intended to be an in-depth overview of the considerations, tradeoffs, and recommendations associated with selecting between Python and R for programmatic data science tasks.
- Accelerating Your Algorithms in Production with Python and Intel MKL, Sep 21 - Sep 8, 2017.
We will provide tips for data scientists to speed up Python algorithms, including a discussion on algorithm choice, and how effective package tool can make large differences in performance.
- Top KDnuggets tweets, Aug 30 – Sep 5: Python overtakes R, becomes the leader in #DataScience; Humble Book Bundle: #DataScience - Sep 6, 2017.
Also: Pandas tips and tricks #Python #DataScience; How I replicated an $86 million project in 57 lines of code; Future #MachineLearning Class.
- New books on Data Science and Machine Learning from Chapman & Hall/CRC Press – Save 20% - Sep 5, 2017.
New books on Data Science and Analytics with Python, Large-Scale Machine Learning in the Earth Sciences, and Social Networks with Rich Edge Semantics - save 20% with code JWR38.
- Visualizing Cross-validation Code - Sep 5, 2017.
Cross-validation helps to improve your prediction using the K-Fold strategy. What is K-Fold you asked? Check out this post for a visualized explanation.
- Search Millions of Documents for Thousands of Keywords in a Flash - Sep 1, 2017.
We present a python library called FlashText that can search or replace keywords / synonyms in documents in O(n) – linear time.