Dec
2019 Jan
All (103) | Courses, Education (8) | Meetings (12) | News (10) | Opinions (28) | Top Stories, Tweets (10) | Tutorials, Overviews (28) | Webcasts & Webinars (7)
- Unlock and Extract Data from Your PDF Documents
- Jan 31, 2019.
Automate and accurately extract data and information locked within PDF documents using PDF Alchemist, increasing productivity and data throughput while reducing costs.
- Exploring Python Basics
- Jan 31, 2019.
This free eBook is a great resource for any beginner, providing a good introduction into Python, a look at the basics of learning a programming language and explores modelling and predictions.
- What Is Dimension Reduction In Data Science?
- Jan 31, 2019.
An extensive introduction into Dimension Reduction, including a look at some of the different techniques, linear discriminant analysis, principal component analysis, kernel principal component analysis, and more.
- ELMo: Contextual Language Embedding
- Jan 31, 2019.
Create a semantic search engine using deep contextualised language representations from ELMo and why context is everything in NLP.
- Data Science in the Real World – Meet Netflix, Google and Amazon at DATAx Singapore
- Jan 30, 2019.
Machine learning, predictive analytics, IoT, smart cities and fintech are some of the hot topics where you need to know the latest. Get a sneak peak of upcoming DATAx with the DATAx New York festival post event report.
- Top KDnuggets tweets, Jan 23-29: What were the most significant machine learning/AI advances in 2018?
- Jan 30, 2019.
Also: Logistic Regression: A Concise Technical Overview; 7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition; Data Science Project Flow for Startups; Papers with Code: A Fantastic GitHub Resource for Machine Learning
- Get the latest analyst research on data science platforms
- Jan 30, 2019.
Access a complimentary copy of the Gartner 2019 Magic Quadrant for Data Science and Machine-Learning Platforms to discover the latest trends and see why Dataiku was named a "Challenger" in the industry.
- Random forests explained intuitively
- Jan 30, 2019.
A detailed explanation of random forests, with real life use cases, a discussion into when a random forest is a poor choice relative to other algorithms, and looking at some of the advantages of using random forest.
- Building an image search service from scratch
- Jan 30, 2019.
By the end of this post, you should be able to build a quick semantic search model from scratch, no matter the size of your dataset.
- [Webinar] Simple Steps to Distributed Deep Learning
- Jan 29, 2019.
In this webinar, Feb 12, find out how distributed deep learning works, get an an overview of the different frameworks, and learn how Databricks is making it easy for data scientists to migrate their single-machine workloads to distributed workloads, at all stages of a deep learning project.
- The Algorithms Aren’t Biased, We Are
- Jan 29, 2019.
We explain the concept of bias and how it can appear in your projects, share some illustrative examples, and translate the latest academic research on “algorithmic bias.”
- Cracking the Data Scientist Interview
- Jan 29, 2019.
After interviewing with over 50 companies for Data Scientist/Machine Learning Engineer, I am going to frame my experiences in the Q&A format and try to debunk any myths that beginners may have in their quest for becoming a Data Scientist.
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7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition
- Jan 29, 2019.
With a new year upon us, I thought it would be a good time to revisit the concept and put together a new learning path for mastering machine learning with Python. With these 7 steps you can master basic machine learning with Python! - Top Stories, Jan 21-27: 2018’s Top 7 Python Libraries for Data Science and AI; Your AI skills are worth less than you think
- Jan 29, 2019.
Also: 2018's Top 7 R Packages for Data Science and AI; Data Science Project Flow for Startups; What were the most significant machine learning/AI advances in 2018?; How to go from Zero to Employment in Data Science; Logistic Regression: A Concise Technical Overview
- Robotic Process Automation in the Nordics
- Jan 29, 2019.
Read this white paper to discover what the future has in store for robotic automation, as well as the current limitations of RPA, how to move past these limitations, why an intelligent future is the natural progression for RPA, and more.
- Monetizing the Math – are you ready?
- Jan 28, 2019.
We outline an extensive list of things to do or plan for to help fully realize the ROI of your AI and Machine Learning projects in 2019.
- Airbnb Rental Listings Dataset Mining
- Jan 28, 2019.
An Exploratory Analysis of Airbnb’s Data to understand the rental landscape in New York City.
- AI is a Big Fat Lie
- Jan 26, 2019.
Is AI legit? This treatise by Eric Siegel, which ridicules the widespread myth of artificial intelligence, is enlightening and actually pretty funny. It's time for the term AI to be “terminated.”
- Beginner to Advanced. Improve your skills with training, workshops and more.
- Jan 25, 2019.
ODSC East in Boston will be the top global event in 2019 that gets you ahead. We offer an unparalleled 350 hours of talks, workshops and training sessions across 14 tracks. Register now with code KDN55 to save 55%!
- Machine Learning Security
- Jan 25, 2019.
We take a look at how malicious actors can break machine learning models and what some of the best practices are when it comes to stopping them.
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Your AI skills are worth less than you think
- Jan 25, 2019.
We are in the middle of an AI boom. That doesn’t mean that making your AI startup succeed is easy. I think there are some important pitfalls ahead of anyone trying to build their business around AI. - Advance Your Career in Analytics
- Jan 24, 2019.
The new Global Master of Management Analytics* from Smith School of Business at Queen’s University (Canada) will put you at the forefront of this rapidly growing field. You can join our next class from anywhere in the world. On January 31st, attend an online information session.
- 2019 CDO Virtual Event: Empowering Business Through Data
- Jan 24, 2019.
This webinar will discuss the evolution of BI, how new technologies have paved the way for modern data platforms and how to use this data to serve the needs of companies across all departments.
- The Data Science Gold Rush: Top Jobs in Data Science and How to Secure Them
- Jan 24, 2019.
Because big data touches almost every industry across the board, those who aren’t already working in data and analytics will soon be utilizing the technology for its undeniable business benefits. Whichever way you slice it, the future of work is through data.
- Data Science Project Flow for Startups
- Jan 24, 2019.
The aim of this post, then, is to present the characteristic project flow that I have identified in the working process of both my colleagues and myself in recent years. Hopefully, this can help both data scientists and the people working with them to structure data science projects in a way that reflects their uniqueness.
- Top KDnuggets tweets, Jan 16-22: How to build an API for a machine learning model in 5 minutes using Flask; Great Introduction to Deep #ReinforcementLearning
- Jan 23, 2019.
Also: Why Ice Cream Is Linked to Shark Attacks; Data Scientist’s Dilemma: The Cold Start Problem; 10 Exciting Ideas of 2018 in #NLP; What were the most significant machine learning/AI advances in 2018?
- How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy
- Jan 23, 2019.
We explain how to retrieve estimates of a model's performance using scoring metrics, before taking a look at finding and diagnosing the potential problems of a machine learning algorithm.
- Building AI to Build AI: The Project That Won the NeurIPS AutoML Challenge
- Jan 23, 2019.
This is an overview of designing a computer program capable of developing predictive models without any manual intervention that are trained & evaluated in a lifelong machine learning setting in NeurIPS 2018 AutoML3 Challenge.
- Logistic Regression: A Concise Technical Overview
- Jan 23, 2019.
Logistic Regression is a Regression technique that is used when we have a categorical outcome (2 or more categories). Logistic Regression is one of the most easily interpretable classification techniques in a Data Scientist’s portfolio.
- Top Stories, Jan 14-20: How to go from Zero to Employment in Data Science; End To End Guide For Machine Learning Projects
- Jan 22, 2019.
Also: How to build an API for a machine learning model in 5 minutes using Flask; How to solve 90% of NLP problems: a step-by-step guide; Data Scientists Dilemma: The Cold Start Problem - Ten Machine Learning Examples; Ontology and Data Science; 9 Must-have skills you need to become a Data Scientist, updated
- Five Major AI Predictions to Watch in 2019
- Jan 22, 2019.
Will 2019 be the year your organization embraces AI? If not, it might be too late. In this live webinar, we will unveil five major predictions companies should pay close attention to in 2019. Register now!
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What were the most significant machine learning/AI advances in 2018?
- Jan 22, 2019.
2018 was an exciting year for Machine Learning and AI. We saw “smarter” AI, real-world applications, improvements in underlying algorithms and a greater discussion on the impact of AI on human civilization. In this post, we discuss some of the highlights. - How AI and Data Science is Changing the Utilities Industry
- Jan 22, 2019.
Together, artificial intelligence (AI) and data science are causing positive developments for the utilities providers that choose to investigate these things. Here are some examples of technology at work.
- 2018’s Top 7 R Packages for Data Science and AI
- Jan 22, 2019.
This is a list of the best packages that changed our lives this year, compiled from my weekly digests.
- Data Science and Ethics – Why Companies Need a new CEO (Chief Ethics Officer)
- Jan 21, 2019.
We explain why data science companies need to have a Chief Ethics Officer and discuss their importance in tackling algorithm bias.
- PAW celebrates its 10-year anniversary – here’s what’s happened so far
- Jan 21, 2019.
Happy 10th Anniversary to Predictive Analytics World! Join Mega-PAW, the premier predictive analytics conference, Jun 16-20 in Las Vegas. Register now!
- KDD 2019 Call for Research, Applied Data Science Papers
- Jan 21, 2019.
KDD-2019 invites submission of papers describing innovative research on all aspects of data science, and of applied papers describing designs and implementations for practical tasks in data science. Submissions due Feb 3.
- 2018’s Top 7 Python Libraries for Data Science and AI
- Jan 21, 2019.
This is a list of the best libraries that changed our lives this year, compiled from my weekly digests.
- Cartoon: Is this how you do the blockchain thing?
- Jan 19, 2019.
Despite the increasing popularity of Blockchain, the concept remains hard to understand. Here is one attempt to explain it.
- Why Ice Cream Is Linked to Shark Attacks – Correlation/Causation Smackdown
- Jan 19, 2019.
Why are soda and ice cream each linked to violence? This article delivers the final word on what people mean by "correlation does not imply causation."
- Webinar: 2019 AI Trends: Filtering the Noise
- Jan 18, 2019.
Check out Dataiku's exclusive webinar on Feb 7, 11am EST, "2019 AI Trends: Filtering the Noise," featuring insights from Léo Drefus-Schmidt, Lead Data Scientist at Dataiku.
- How to Monitor Machine Learning Models in Real-Time
- Jan 18, 2019.
We present practical methods for near real-time monitoring of machine learning systems which detect system-level or model-level faults and can see when the world changes.
- Automated Machine Learning in Python
- Jan 18, 2019.
An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.
- Comparing Machine Learning Models: Statistical vs. Practical Significance
- Jan 18, 2019.
Is model A or B more accurate? Hmm… In this blog post, I’d love to share my recent findings on model comparison.
- Why Applied MSc in Data Engineering? Data Engineers are in greater demand than Data Scientists
- Jan 17, 2019.
2 graduate programmes now available at Data ScienceTech Institute in France: Applied MSc in Data Engineering Applied MSc in Data Science & Artificial Intelligence, with enterprise level certifications included in each. There is a 100% conversion to an internship and 90% to a job contract.
- The Hundred-Page Machine Learning Book
- Jan 17, 2019.
This book covers supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning.
- Data Scientist’s Dilemma: The Cold Start Problem – Ten Machine Learning Examples
- Jan 17, 2019.
We present an array of examples showcasing the cold-start problems in data science where the algorithms and techniques of machine learning produce the good judgment in model progression toward the optimal solution.
- How to build an API for a machine learning model in 5 minutes using Flask
- Jan 17, 2019.
Flask is a micro web framework written in Python. It can create a REST API that allows you to send data, and receive a prediction as a response.
- Top KDnuggets tweets, Jan 9-15: Never mind killer robots—here are six real #AI dangers to watch out for in 2019
- Jan 16, 2019.
Also: Jupyter Notebooks Advanced Tutorial; Math for Programmers; NLP Overview: Modern Deep Learning Techniques Applied to Natural Language Processing; The Five Best Data Visualization Libraries
- 10 Exciting Ideas of 2018 in NLP
- Jan 16, 2019.
We outline a selection of exciting developments in NLP from the last year, and include useful recent papers and images to help further assist with your learning.
- Webinar – QBE, Allstate and Arch MI discuss how AI is successfully winning customers hearts
- Jan 16, 2019.
Whilst there are many barriers to implementing AI effectively to drive results across underwriting, customer service and claims, it is essential for winning and retaining customers in the future. Join this webinar and learn how.
- Word Embeddings & Self-Supervised Learning, Explained
- Jan 16, 2019.
There are many algorithms to learn word embeddings. Here, we consider only one of them: word2vec, and only one version of word2vec called skip-gram, which works well in practice.
- Ontology and Data Science
- Jan 16, 2019.
In simple words, one can say that ontology is the study of what there is. But there is another part to that definition that will help us in the following sections, and that is ontology is usually also taken to encompass problems about the most general features and relations of the entities which do exist.
- Jump-start your data science career
- Jan 15, 2019.
Build statistical and analytical expertise as well as the management and leadership skills necessary to implement high-level, data-driven decisions in Northwestern’s online Master of Science in Data Science program. Apply now!
- Math for Programmers
- Jan 15, 2019.
Math for Programmers teaches you the math you need to know for a career in programming, concentrating on what you need to know as a developer.
- How to go from Zero to Employment in Data Science
- Jan 15, 2019.
We propose the quickest and surest way to go from zero experience to landing a job, either in data science generally, or specifically in a new programming language or a new technology.
- The 6 Most Useful Machine Learning Projects of 2018
- Jan 15, 2019.
Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.
- Top Stories, Jan 7-13: The Five Best Data Visualization Libraries; Modern Deep Learning Techniques Applied to NLP
- Jan 15, 2019.
Also: Top 10 Books on NLP and Text Analysis; Practical Apache Spark in 10 Minutes; The cold start problem: how to build your machine learning portfolio; Core Principles of Sustainable Data Science, Machine Learning and AI Product Development; 4 Myths of Big Data and 4 Ways to Improve with Deep Data
- The AI Conference returns to New York, Apr 15-18 – Special offer from KDnuggets
- Jan 14, 2019.
Join the brightest minds in AI in NYC to learn best practices, hear intriguing case studies, and dive deep into the latest technologies. Best rate till Jan 25 and save extra 20% with code KDNUGGETS.
- Data Analytics Certification or a Degree?
- Jan 14, 2019.
The classic IT question: Which is better for your career, certs or a degree? The answer: Both. For your best shot at a successful data career, you need a degree and certs. Get the credentials you need and turn your career up a degree. Find out what WGU can offer you!
- First sessions confirmed for PAW Industry 4.0 and DLW Munich 2019 – Super Early Bird rates available until Feb 1st
- Jan 14, 2019.
Get your ticket now for PAW Industry 4.0 and DLW Munich, 6-7 May 2019, and enter a world full of Predictive Maintenance, Anomaly Detection, Risk Management, Internet of Things, Deep Learning, Machine Learning & many more related topics!
- Top Active Blogs on AI, Analytics, Big Data, Data Science, Machine Learning – updated
- Jan 14, 2019.
Stay up-to-date with the latest technological advancements using our extensive list of active blogs; this is a list of 100 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.
- How to solve 90% of NLP problems: a step-by-step guide
- Jan 14, 2019.
Read this insightful, step-by-step article on how to use machine learning to understand and leverage text.
- End To End Guide For Machine Learning Projects
- Jan 14, 2019.
Let’s imagine you are attempting to work on a machine learning project. This article will provide you with the step to step guide on the process that you can follow to implement a successful project.
- Why Vegetarians Miss Fewer Flights – Five Bizarre Insights from Data
- Jan 12, 2019.
A frenzy of number-crunching is churning out a heap of insights that are colorful, sometimes surprising, and often valuable. We explain how this works, and investigate five bizarre discoveries found in data.
- How to Remove Unfair Bias From Your AI
- Jan 11, 2019.
In this live webinar, Jan 17 @ 11:00 am ET, Colin Priest, Senior Director of Product Marketing at DataRobot will discuss how to identify and correct bias in AI.
- The SIAM Book Series on Data Science
- Jan 11, 2019.
SIAM is soliciting manuscripts for its new book series on the mathematical and computational foundations of data science.
- The year in AI/Machine Learning advances: Xavier Amatriain 2018 Roundup
- Jan 11, 2019.
A summary of the main machine learning advances from 2018, including AI hype cooling down, interpretability, deep learning, NLP, and more.
- Practical Apache Spark in 10 Minutes
- Jan 11, 2019.
Check out this series of articles on Apache Spark. Each part is a 10 minute tutorial on a particular Apache Spark topic. Read on to get up to speed using Spark.
- MS in Applied Data Science Online – which track is right for you?
- Jan 10, 2019.
At Bay Path University, we'll provide you with a framework for working together regardless of your background and experience. That is why we created two tracks to complete the MS in Applied Data Science degree, which is right for you?
- Biggest Deep Learning Summit – Special KDnuggets Offer
- Jan 10, 2019.
At RE•WORK, the team are dedicating 2019 to keep up the high-quality events and to bring you the latest innovations & breakthroughs in AI. RE•WORK are offering a huge saving on all summit passes when you register with the discount code NEWYEAR.
- Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data
- Jan 10, 2019.
This comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more.
- Explainable Artificial Intelligence
- Jan 10, 2019.
We outline the necessity of explainable AI, discuss some of the methods in academia, take a look at explainability vs accuracy, investigate use cases, and more.
- The Role of the Data Engineer is Changing
- Jan 10, 2019.
The role of the data engineer in a startup data team is changing rapidly. Are you thinking about it the right way?
- Python Patterns: max Instead of if
- Jan 10, 2019.
I often have to loop over a set of objects to find the one with the greatest score. You can use an if statement and a placeholder, but there are more elegant ways!
- Top KDnuggets tweets, Jan 02-08: 10 Free Must-Read Books for Machine Learning and Data Science
- Jan 9, 2019.
Also: Papers with Code: A Fantastic GitHub Resource; Most Recommended #DataScience and #MachineLearning Books by Top MS programs;10 More Free Must-Read Books for ML and DS
- [Webinar] Accelerating Machine Learning on Databricks
- Jan 9, 2019.
In this webinar, we will cover some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine Learning.
- Top December Stories: Why You Shouldn’t be a Data Science Generalist
- Jan 9, 2019.
Also: Common mistakes when carrying out machine learning and data science; Learning Machine Learning vs Learning Data Science; Here are the most popular Python IDEs / Editors; 10 More Must-See Free Courses for Machine Learning and Data Science.
- New Online MS in Business Analytics from Drexel University
- Jan 9, 2019.
With Drexel University’s online MS in Business Analytics program, you’ll be able to effectively analyze this data to give your company and yourself a competitive edge.
- Industry leaders to speak at Mega-PAW, Las Vegas – June 16-20
- Jan 9, 2019.
Meet the stellar industry leaders speaking at Mega-PAW at Caesars Palace, Las Vegas, Jun 16-20, 2019. Don't forget to register!
- 4 Myths of Big Data and 4 Ways to Improve with Deep Data
- Jan 9, 2019.
There is a fundamental misconception that bigger data produces better machine learning results. However bigger data lakes / warehouses won’t necessarily help to discover more profound insights. It is better to focus on data quality, value and diversity not just size. "Deep Data" is better than Big Data.
- Top 10 Books on NLP and Text Analysis
- Jan 9, 2019.
When it comes to choosing the right book, you become immediately overwhelmed with the abundance of possibilities. In this review, we have collected our Top 10 NLP and Text Analysis Books of all time, ranging from beginners to experts.
- Core Principles of Sustainable Data Science, Machine Learning and AI Product Development: Research as a core driver
- Jan 9, 2019.
Regardless of the size of your organisation, if you are developing machine learning or AI products, the core asset you have is a research professional, data scientist or AI scientist, regardless of their academic background.
- Apply to NYU Stern’s MS in Business Analytics
- Jan 8, 2019.
Do you want to achieve new, exciting educational and career goals? Consider NYU Stern MS in Business Analytics, a one-year, part-time graduate degree for experienced professionals. Apply by Feb 15 to start in May.
- Do something for yourself in 2019
- Jan 8, 2019.
With Penn State's Master of Professional Studies in Data Analytics offered online through Penn State World Campus, you can evolve as a primary influencer who drives key business decisions. We are currently reviewing applications for summer and fall 2019!
- 5 things that happened in Data Science in 2018
- Jan 8, 2019.
We review 5 things that happened in Data Science in 2018 and offer 20% discount on Reinforce AI Conference, Mar 20-22 in Budapest.
- A Non-Compromising Approach to Privacy-Preserving Personalized Services
- Jan 8, 2019.
Could one even achieve both high privacy and high utility? Yes, and we explain how.
- NLP Overview: Modern Deep Learning Techniques Applied to Natural Language Processing
- Jan 8, 2019.
Trying to keep up with advancements at the overlap of neural networks and natural language processing can be troublesome. That's where the today's spotlighted resource comes in.
- Top Stories, Dec 24 – Jan 6: The Essence of Machine Learning; Papers with Code: A Fantastic GitHub Resource for Machine Learning
- Jan 8, 2019.
Also: A Guide to Decision Trees for Machine Learning and Data Science; The cold start problem: how to build your machine learning portfolio; Comparison of the Top Speech Processing APIs; Synthetic Data Generation: A must-have skill for new data scientists; Approaches to Text Summarization: An Overview
- The Data Science Event You Need in 2019
- Jan 7, 2019.
MADS Can Help You Achieve Your 2019 Goals! Marketing Analytics and Data Science is coming to San Francisco, Apr 8-10. KDnuggets readers save 20% with VIP Code MADS19KDN. Register Today and Save!
- Rev Summit for Data Science Leaders featuring Daniel Kahneman
- Jan 7, 2019.
Rev features interactive sessions, Q&A with industry luminaries, poster sessions for interesting modeling techniques and accomplishments, and stimulating conversations about how to make data science an enterprise-grade capability.
- The Five Best Data Visualization Libraries
- Jan 7, 2019.
There are plenty of library options out there to make great visualizations. We outline five of the best, complete with code examples and explanations, that will enable you to create and build interactive visualizations.
- Comparison of the Text Distance Metrics
- Jan 7, 2019.
There are many different approaches of how to compare two texts (strings of characters). Each has its own advantages and disadvantages and is good only for a range of specific use cases.
- Math for Machine Learning
- Jan 4, 2019.
This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.
- Strata Data SF 2019 KDnuggets Offer
- Jan 4, 2019.
Strata Data Conference is in San Francisco Mar 25-28. Best price for Strata San Francisco expires on Friday, Jan 11. KDnuggets readers can save an additional 20% on Gold, Silver, and Bronze passes with code KDNU (up to $849 on a Gold pass).
- The cold start problem: how to build your machine learning portfolio
- Jan 4, 2019.
This post outlines what makes a good machine leaning portfolio, with useful examples to help you begin to understand the type of project that gets noticed by big companies.
- What to do when your training and testing data come from different distributions
- Jan 4, 2019.
However, sometimes only a limited amount of data from the target distribution can be collected. It may not be sufficient to build the needed train/dev/test sets. What to do in such a case? Let us discuss some ideas!
- Improve your AI and Machine Learning skills at AI NEXTCon in Seattle, Jan 23-27
- Jan 3, 2019.
If you are a developer looking to hone your skills, a tech lead and manager to learn latest AI tech that apply to your engineering teams to innovate products and services, or someone who just wants to learn more about the AI industry that's re-shaping the tech world, the AI NEXTCon is right for you.
- Ensemble Learning: 5 Main Approaches
- Jan 3, 2019.
We outline the most popular Ensemble methods including bagging, boosting, stacking, and more.
- Approaches to Text Summarization: An Overview
- Jan 3, 2019.
This article will present the main approaches to text summarization currently employed, as well as discuss some of their characteristics.
- The Backpropagation Algorithm Demystified
- Jan 2, 2019.
A crucial aspect of machine learning is its ability to recognize error margins and to interpret data more precisely as rising numbers of datasets are fed through its neural network. Commonly referred to as backpropagation, it is a process that isn’t as complex as you might think.
- Top KDnuggets tweets, Dec 19 – Jan 1: Deep Learning Cheat Sheets
- Jan 2, 2019.
Also: Machine Learning Cheat Sheets; Papers with Code: A Fantastic GitHub Resource for Machine Learning; Neural network AI is simple. So… Stop pretending you are a genius; Top Python Libraries in 2018 in Data Science, Deep Learning and Machine Learning
- 3 More Google Colab Environment Management Tips
- Jan 2, 2019.
This is a short collection of lessons learned using Colab as my main coding learning environment for the past few months. Some tricks are Colab specific, others as general Jupyter tips, and still more are filesystem related, but all have proven useful for me.