2017 Jun Opinions, Interviews
All (100) | Courses, Education (10) | Meetings (11) | News, Features (13) | Opinions, Interviews (26) | Software (6) | Tutorials, Overviews (31) | Webcasts & Webinars (3)
- Optimization in Machine Learning: Robust or global minimum? - Jun 30, 2017.
Here we discuss how convex problems are solved and optimised in machine learning/deep learning.
- Why Artificial Intelligence and Machine Learning? - Jun 30, 2017.
With your goals (i.e., the why) in mind, the next step for any artificial intelligence or machine learning solution is to specify how (e.g., which algorithms or models to use) to achieve a specific goal or set of goals, and finally what the end result will be (e.g., product, report, predictive model).
- Interesting Things Learned as a Student of Machine Learning - Jun 29, 2017.
Did you ever learn something you didn't really want to? The path to machine learning mastery is paved with such collateral knowledge. Here are a few examples of such information I have gleaned while trekking away.
- Who Cares About Evidence? - Jun 29, 2017.
Why bother with evidence? Because it improves the odds that what we believe is actually true. But not always.
- Text Clustering: Get quick insights from Unstructured Data - Jun 28, 2017.
Grouping and clustering free text is an important advance towards making good use of it. We present an algorithm for unsupervised text clustering approach that enables business to programmatically bin this data.
- Pitfalls in pseudo-random number sampling at scale with Apache Spark - Jun 27, 2017.
Large scale simulation of random number generation is possible with today’s high speed & scalable distributed computing frameworks. Let’s understand how it can be achieved using Apache Spark.
- Top 10 Quora Machine Learning Writers and Their Best Advice, Updated - Jun 26, 2017.
Gain some insight on a variety of topics with select answers from Quora's current top machine learning writers. Advice on research, interviews, hot topics in the field, how to best progress in your learning, and more are all covered herein.
- Why Data Science Argues against a Muslim Ban - Jun 24, 2017.
From the perspective of data science, a Muslim ban would weaken security, not strengthen it.
- 3 Key Trends Shaping the 2017 Data Science Hiring Market - Jun 23, 2017.
Interesting finding include: salaries for early career data scientists decrease for the first time in four years, percent of early career data scientists with a PhD drops - read more for details.
- Will Apache Spark Finally Advance Genomic Data Analysis? - Jun 23, 2017.
Spark has been useful in mapping out genetic traits that can be associated with certain diseases and the genetic makeup of microorganisms that live in our bodies.
- The world’s first protein database for Machine Learning and AI - Jun 22, 2017.
dSPP is the world first interactive database of proteins for AI and Machine Learning, and is fully integrated with Keras and Tensorflow. You can access the database at peptone.io/dspp
- Emerging Ecosystem: Data Science and Machine Learning Software, Analyzed - Jun 21, 2017.
We examine which top tools are "friends", their Python vs R bias, and which work well with Spark/Hadoop and Deep Learning, and identify an emerging Big Data Deep Learning ecosystem.
- Why Swarm Intelligence is a Better Way to Read Emotions - Jun 20, 2017.
Swarm Intelligence is using many simple machine learning models good at one small task to solve bigger, more complex problems. We examine how it can improve sentiment analysis and measuring emotions.
- Role of the Data Scientist in the B2B Era - Jun 20, 2017.
In businesses everywhere, the digital transformation is spawning a bunch of new job titles. Among them are Chief Data Officer, Big Data Architect and Data Visualizer. All these sought-after specialist data roles are having a major impact on the workplace.
- The Real “Fear” of AI is Automation Inundation - Jun 16, 2017.
The biggest threat to minimum wage earners (and beyond, quite frankly) is the new tsunami of automation in the workplace.
- Hadoop as a Data Warehouse: Cracking the Code with Kudu - Jun 15, 2017.
Here we discuss problems behind replacing an existing Data Warehouse with Hadoop and available solutions to make this happen. Lets see how.
- Data Scientist: Learn the Skills you need for free - Jun 14, 2017.
Data Scientists are in big demand! We review career pathways, relevant data science skills, and how you can learn them at no cost.
- 7 Ways to Get High-Quality Labeled Training Data at Low Cost - Jun 13, 2017.
Having labeled training data is needed for machine learning, but getting such data is not simple or cheap. We review 7 approaches including repurposing, harvesting free sources, retrain models on progressively higher quality data, and more.
- The Practical Importance of Feature Selection - Jun 12, 2017.
Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing generalizability.
- Autonomous Vehicles Need Superhuman Perception for Success - Jun 12, 2017.
Michael Milford, Associate Professor at Queensland University of Technology (QUT), is a leading robotics researcher working to improve perception and more in autonomous vehicles, conducting his research at the intersection of robotics, neuroscience and computer vision.
- The Unintended Consequences of Machine Learning - Jun 8, 2017.
But with great power comes great responsibility. Let me tell you a story about the unintended consequences of well-meaning machine learning research.
- Your Checklist to Get Data Science Implemented in Production - Jun 7, 2017.
For over a year we surveyed thousands of companies from all types of industries and data science advancement on how they managed to overcome these difficulties and analyzed the results. Here are the key things to keep in mind when you're working on your design-to-production pipeline.
- Women in Tech: Interview with DeepMind’s Silvia Chiappa - Jun 5, 2017.
We interview leading women in STEM to learn more about how we can all work to make science and technology industries more inclusive. How can more women be encouraged to work in these fields?
- Is Regression Analysis Really Machine Learning? - Jun 5, 2017.
What separates "traditional" applied statistics from machine learning? Is statistics the foundation on top of which machine learning is built? Is machine learning a superset of "traditional" statistics? Do these 2 concepts have a third unifying concept in common? So, in that vein... is regression analysis actually a form of machine learning?
- Why Does Deep Learning Not Have a Local Minimum? - Jun 2, 2017.
"As I understand, the chance of having a derivative zero in each of the thousands of direction is low. Is there some other reason besides this?"
- The Artificial ‘Artificial Intelligence’ Bubble and the Future of Cybersecurity - Jun 1, 2017.
What’s going on now in the field of ‘AI’ resembles a soap bubble. And we all know what happens to soap bubbles eventually if they keep getting blown up by the circus clowns (no pun intended!): they burst.