Table of Contents Automatic Machine Learning Common Pitfalls for Studying the Human Side of Machine Learning Statistical Learning Theory:…
Continue Readingmachine
Generating New Ideas for Machine Learning Projects Through Machine Learning
prediction for a mass neural network', 'learning of human activity recognition from analysis of text', "an nba player 's approach…
Continue ReadingWhy yes, I did used to do PHP development, but what does that have to do with TensorFlow Hub not working on my machine?
That’s why you’re here.You’re sad, becauseTensorFlow Hub is a library for reusable machine learning modulesand that sounds awesome, and you…
Continue ReadingCook php
But it is valid for Redhat too.How not to design a database.What I have seen (during this year only), however,…
Continue Reading2018 Year-in-Review: Machine Learning Open Source Projects & Frameworks
While it was released in 2017, TensorFlow Serving has eased the work of developers this year, as far as moving…
Continue ReadingUsing Object Detection for Complex Image Classification Scenarios Part 1:
WikipediaInteractive deep learning with Jupyter, Docker and PyTorch on the Data Science Virtual Machine …Learn to train deep learning models with…
Continue ReadingBuilding a Skin Lesion Classification Web App
After adding a fully connected layer and through only training the last few layers, we obtain a model that can…
Continue ReadingDevelop a NLP Model in Python & Deploy It with Flask, Step by Step
Our ML systems workflow is like this: Train offline -> Make model available as a service -> Predict online.A classifier…
Continue Reading6 uncommon principles for effective data science
Choosing how to clean dirty data or what features to use will affect the accuracy of the model, but the…
Continue ReadingHow not to Use Machine Learning Models
I was very interested in how the presentation team will be using the model though, so I asked the question,…
Continue ReadingData Science Curriculum from Scratch 2018 (Part 1)
The purpose of the curriculum is to serves as a guideline for you to get started on data science even…
Continue ReadingData that illuminates the AI boom
The number of AI startups (top) is shown on the left, compared with total startups on the right..AI investment (below) is…
Continue ReadingTowards Ethical Machine Learning
We must understand this data in order to digress from these biases, rather than perpetuating them.One step to reduce biases…
Continue ReadingSusan Li
Parsing XML, Named Entity Recognition in One-ShotConditional Random Fields, Sequence Prediction…Mobile Ads Click-Through Rate (CTR) PredictionOnline Advertising, Google PPC, AdWords Campaign…Exploring…
Continue ReadingMachine learning for people who know nothing about machine learning
It does a great job of matching the data we do have, but it won’t be able to make sensible…
Continue ReadingSlava Kurilyak
Deep Learning A-Z™: Hands-On Artificial Neural Networks by Kirill Eremenko, Hadelin de Ponteves, and the SuperDataScience Team via UdemyTop Machine…
Continue ReadingMachine Learning & AI Main Developments in 2018 and Key Trends for 2019
This year we posed the question:What were the main developments in Machine Learning and Artificial Intelligence in 2018, and what…
Continue ReadingLearning Machine Learning vs Learning Data Science
If you do want one of these (often excellent) jobs, we still recommend focusing a minority of your education on…
Continue ReadingWhat exactly can you do with Python? Here are Python’s 3 main applications.
Here are Python’s 3 main applications.YK SugiBlockedUnblockFollowFollowingJun 14If you’re thinking of learning Python — or if you recently started learning it — you may…
Continue ReadingBenjamin Lau
I extracted skills recruiters were looking for, went through reviews from class central, combined that with advice from the community,…
Continue ReadingHow Different are Conventional Programming and Machine Learning?
And the practical application of machine learning is where it is not even feasible to articulate a definite mathematical solution…
Continue ReadingThe Hidden Dangers in Algorithmic Decision Making
Math as a construct, along with its properties, exist as a product of human thought, which leaves it vulnerable to…
Continue ReadingAI Tales: Building Machine learning pipeline using Kubeflow and Minio
Phew…!Kubernetes provides consistent way to deploy and run your applications and Kubeflow helps you define your ML pipeline on top…
Continue ReadingHow should we define AI?
These include machine learning, data science, and deep learning.Machine learning can be said to be a subfield of AI, which…
Continue ReadingHow to ace cold calling with Machine Learning
However, with Machine Learning and enough data it might be possible to understand the factors behind successful calls which can…
Continue Reading