2019 Oct Opinions
All (92) | Courses, Education (2) | Meetings (5) | News (4) | Opinions (23) | Top Stories, Tweets (10) | Tutorials, Overviews (47) | Webcasts & Webinars (1)
- How to Make an Agile Team Work for Big Data Analytics - Oct 31, 2019.
Learn how to approach the challenges when merging an agile methodology into a data science team to bring out the best value for your Big Data products.
- How Data Labeling Facilitates AI Models - Oct 31, 2019.
AI-based models are highly dependent on accurate, clean, well-labeled, and prepared data in order to produce the desired output and cognition. These models are fed with bulky datasets covering an array of probabilities and computations to make its functioning as smart and gifted as human intelligence.
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Why is Machine Learning Deployment Hard? - Oct 29, 2019.
Developing an excellent machine learning model is one thing. Deploying it to production is another. Consider these lessons learned and recommendations for approaching this important challenge to help ensure value from your AI work. - About Google’s Self-Proclaimed Quantum Supremacy and its Impact on Artificial Intelligence - Oct 29, 2019.
Google claimed quantum supremacy, IBM challenged it… but the development is really important for the future of AI.
- Seven Myths About the True Costs of AI Systems - Oct 24, 2019.
While there is much excitement today around implementing AI at the enterprise level, the financial costs of this process are often unexpected and underappreciated. These seven myths are crucial lessons learned that executives should know before heading down the road to AI.
- Addressing the Growing Need for Skills in Data Science - Oct 22, 2019.
To address the current difficulties in hiring data scientists due to their short supply, many companies can benefit from retraining existing analytically minded employees.
- Bye Data Scientists, Hello AI? Not Likely! - Oct 22, 2019.
AI is becoming more mainstream. The fact that computers/robots will learn after being built and will surpass a human's intelligence is terrifying.
- 5 Tips for Novice Freelance Data Scientists - Oct 18, 2019.
If you want to launch your data science skills into freelance work, then check out these important tips to help you kick start your next adventure in data.
- Artificial Intelligence: Salaries Heading Skyward - Oct 17, 2019.
While the average salary for a Software Engineer is around $100,000 to $150,000, to make the big bucks you want to be an AI or Machine Learning (Specialist/Scientist/Engineer.)
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How to Become a (Good) Data Scientist – Beginner Guide - Oct 16, 2019.
A guide covering the things you should learn to become a data scientist, including the basics of business intelligence, statistics, programming, and machine learning. - Using DC/OS to Accelerate Data Science in the Enterprise - Oct 15, 2019.
Follow this step-by-step tutorial using Tensorflow to setup a DC/OS Data Science Engine as a PaaS for enabling distributed multi-node, multi-GPU model training.
- Top 7 Things I Learned in my Data Science Masters - Oct 15, 2019.
Even though I’m still in my studies, here’s a list of the most important things I’ve learned (as of yet).
- Choosing a Machine Learning Model - Oct 14, 2019.
Selecting the perfect machine learning model is part art and part science. Learn how to review multiple models and pick the best in both competitive and real-world applications.
- There is No Such Thing as a Free Lunch - Oct 11, 2019.
You have heard the expression “there is no such thing as a free lunch” – well in machine learning the same principle holds. In fact there is even a theorem with the same name.
- The problem with metrics is a big problem for AI - Oct 11, 2019.
The practice of optimizing metrics is not new nor unique to AI, yet AI can be particularly efficient (even too efficient!) at doing so.
- 8 Paths to Getting a Machine Learning Job Interview - Oct 10, 2019.
While you may be focused on your performance during your next job interview, landing that interview can be just as hard. Check out these tips for finding and securing an interview for a machine learning job.
- Lemma, Lemma, Red Pyjama: Or, doing words with AI - Oct 10, 2019.
If we want a machine learning model to be able to generalize these forms together, we need to map them to a shared representation. But when are two different words the same for our purposes? It depends.
- Four questions to help accurately scope analytics engineering project - Oct 9, 2019.
Being really good at scoping analytics projects is crucial for team productivity and profitability. You can consistently deliver on time if you work out the issue first, and these four questions can help you prepare.
- Data Science is Boring (Part 2) - Oct 9, 2019.
Why I love boring ML problems and how I think about them.
- Why the ‘why way’ is the right way to restoring trust in AI - Oct 8, 2019.
As so many more organizations now rely on AI to deliver services and consumer experiences, establishing a public trust in the AI is crucial as these systems begin to make harder decisions that impact customers.
- Overcoming Deep Learning Stumbling Blocks - Oct 4, 2019.
Find out what was presented at the 6th annual Deep Learning Summit in London where industry leaders, academics, researchers, and innovative startups presenting the latest technological advancements and industry application methods in the field of deep learning.
- Training a Machine Learning Engineer - Oct 3, 2019.
There is no clear outline on how to study Machine Learning/Deep Learning due to which many individuals apply all the possible algorithms that they have heard of and hope that one of implemented algorithms work for their problem in hand. Below, I've listed out some of the steps that one should adopt while solving a machine learning problem.
- A European Approach to Master’s Degrees in Data Science - Oct 1, 2019.
Data science education in Europe has been reevaluated and new recommendations are leading the way to the next generation of data science Master's courses to better support and train students.
Why is Machine Learning Deployment Hard?
How to Become a (Good) Data Scientist – Beginner Guide
A European Approach to Master’s Degrees in Data Science
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