In this article, we describe seven key areas to take into account for successful operationalization and lifecycle management (MLOps) of your ML initiatives
I recently was a guest speaker at the Stanford AI Salon on the topic of accessiblity in AI, which included a free-ranging discussion among assembled members of the Stanford AI Lab. There were a number of interesting questions and topics, so I thought I would share a few of my answers here.
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!
KDnuggets poll finds that Machine Learning Engineer, Researcher, and Data Scientist have the highest job satisfaction. Job satisfaction usually starts high, but drops significantly after 4 years on the job. Data professionals in Asia and Latin America are most unsatisfied.
Most people don’t realize, but the actual “fancy” machine learning algorithm is like the last mile of the marathon. There is so much that must be done before you get there!
Traditionally, Data Science would focus on mathematics, computer science and domain expertise. While I will briefly cover some computer science fundamentals, the bulk of this blog will mostly cover the mathematical basics one might either need to brush up on (or even take an entire course).
We bring to you the top 16 open source deep learning libraries and platforms. TensorFlow is out in front as the undisputed number one, with Keras and Caffe completing the top three.
Also: Key Algorithms and Statistical Models for Aspiring Data Scientists; Why Deep Learning is perfect for NLP; Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step; Top 8 Free Must-Read Books on Deep Learning
Deep learning brings multiple benefits in learning multiple levels of representation of natural language. Here we will cover the motivation of using deep learning and distributed representation for NLP, word embeddings and several methods to perform word embeddings, and applications.
Presto enables data scientists to run interactive SQL across multiple data sources. This open source engine supports querying anything, anywhere, and at large scale.
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!
What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article.
First part on a full discussion on how to do Distributed Deep Learning with Apache Spark. This part: What is Spark, basics on Spark+DL and a little more.
We offer a step-by-step guide to technical content and related assets that to help you learn Apache Spark, whether you're getting started with Spark or are an accomplished developer.
It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.
In this article, I will discuss the significance of IoT and gain insights on why this technology is becoming an integral part of the daily learning and teaching methodologies.
This article provides a summary of key algorithms and statistical techniques commonly used in industry, along with a short resource related to these techniques.
Libraries like Keras simplify the construction of neural networks, but are they impeding on practitioners full understanding? Or are they simply useful (and inevitable) abstractions?
In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.
Machine Learning Engineer, with avg. salary of $136K and Data Scientist, with avg. salary $133K are among the top US jobs in 2018, according to job site Indeed.
Machine Learning's popularity is continuing to grow and has engraved itself in pretty much every industry. This article contains lessons from a data scientist on how to unlock it's full potential.
It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.
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.
Deep Learning is the newest trend coming out of Machine Learning, but what exactly is it? And how do I learn more? With that in mind, here's a list of 8 free books on deep learning.
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.
This book covers machine learning techniques from text using both bag-of-words and sequence-centric methods. The scope of coverage is vast, and it includes traditional information retrieval methods and also recent methods from neural networks and deep learning.
No other mean of data description is more comprehensive than Descriptive Statistics and with the ever increasing volumes of data and the era of low latency decision making needs, its relevance will only continue to increase.
The power of charts to assist in accurate interpretation is massive and that's why it is vital to select the correct type when you are trying to visualize data.
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.
Here are the steps you need to obtain your first job in data science, including details on how to create a good portfolio, key networking tips, getting the right education and managing expectations.
Interested in what a data scientist does on a typical day of work? Each data science role may be different, but these contributors have insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.
Blockchain Explained in 7 Python Functions
Building Convolutional Neural Network using NumPy from Scratch
7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning
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