We had great meetup "Building Trust in the Black Box: An Introduction to AI Explainability". Thank you for your interest. He will talk about "Empathy: The Most Crucial, Least Discussed Data Science Superpower". You can also sign up for our newsletter to be informed about our events, workshops, and articles. He will talk about "Empathy: The Most Crucial, Least Discussed Data Science Superpower".
Most popular @KDnuggets tweets for Feb 12-18 were: Most Retweeted & Favorited: What Does it Mean to Deploy a #MachineLearning Model? It is a major pain point. Most Viewed: True, but not for 69%- maybe 10% of deployed systems? Simple neural networks could probably be replaced by regex, but not Deep Neural Networks. Regex will not do good quality machine translation or speech understanding you see now from Google and other intelligent assistants https://t.co/Er6Awaw4t4
She will talk about "The Data Science Interview". We had great workshop "Separable Convolution for Efficient Implementation of CNNs and Vision Algorithms". Thank you for your interest. She will talk about "The Data Science Interview". You can also sign up for our newsletter to be informed about our events, workshops, and articles.
Its a wrong question to start with which will lead companies to hire the wrong people. We advertise for data-scientist as a role, but then when screen candidates, we don't want to emphasize heavily on those who have had data-scientist job titles previously, simply because data scientist is a recently invented term. We state in the job ad that we're looking for candidates with Masters or PhD in any quantitative field, math, stats, physics, bioinformatics/bio-medical engineering, signal processing, and so forth. We know that candidates from those fields can step up to the mark. They may be not software architects or software engineers, but that area is covered in the engineering team.
AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Learning process ought to be a healthy balance between practical & theoretical. Decide on a domain (finance, HR, etc.), reach out to individuals to comprehend how their job works. Understand ways to maximize your algorithm for effect. Combining technical knowledge together with the business issue is in which a real datascientist measures in. Strategy might work outside in contests, but is bound to fail at an actual job.