AIai in mediaai moviesmoviesAccelerate AIposted by Ava Burcham, ODSC Aug 23, 2019
Why Should You Do a Data Science Mini Bootcamp?
data science bootcampdata science mini bootcampjob trainingmini bootcampCareer Insightsposted by ODSC Team Aug 19, 2019
What Are a Few AI Research Labs on the West Coast?
ResearchWest 2019Academic Researchposted by Alex Landa, ODSC Aug 13, 2019
7 Reasons Your Data Science Resume is Suboptimal
ResumesWest 2019Career Insightsposted by ODSC Team Aug 12, 2019
Is AI the Future of Digital Influencers?
Accelerate AIposted by Ava Burcham, ODSC Aug 9, 2019
You Can’t Buy Your Way Out of Data Science Problems
data science problemsAccelerate AIposted by Elizabeth Wallace, ODSC Aug 8, 2019
If Your Business Can’t Compete With AI, Then Quit
AIBusinessAccelerate AIposted by Elizabeth Wallace, ODSC Aug 6, 2019
EthicsMachine Learningmachine learning fairnessConferencesposted by Sray Agarwal Aug 23, 2019
Optuna: An Automatic Hyperparameter Optimization Framework
hyperparameterMachine LearningoptunaWest 2019Conferencesposted by Crissman Loomis Aug 22, 2019
IBM Research Launches Explainable AI Toolkit
AIai toolkitexplainable AIinterpretable aiMachine Learningposted by Daniel Gutierrez, ODSC Aug 22, 2019
Data Visualization for Academics
AcademiadataData VisualizationData Visualizationposted by Allen Downey Aug 22, 2019
What is Augmented Programming?
augmented machine learningaugmented programmingConferencesposted by Gideon Mann Aug 21, 2019
Best Deep Reinforcement Learning Research of 2019 So Far
deep reinforcement learningReinforcement LearningAcademic Researchposted by Daniel Gutierrez, ODSC Aug 21, 2019
What is Implicit Deep Learning?
Deep Learningimplicit deep learningimplicit rulesConferencesposted by Laurent El Ghaoui Aug 19, 2019
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Cracking the Box: Interpreting Black Box Machine Learning Models -
7 Reasons Your Data Science Resume is Suboptimal -
Causal Inference: An Indispensable Set of Techniques for Your Data Science Toolkit -
Bears Need to Learn as well – Practical Reinforcement Learning with TensorFlow 2.0 & TF-Agents -
Reviewing Amazon’s Machine Learning University – Is it Worth All of the Hype? -
Not Always a Black Box: Machine Learning Approaches For Model Explainability
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Mission Analytics: Common pitfalls and how to avoid them – Delin Shen | ODSC East 2018
The promise of data and data analytics is immense, but its actual implementation needs more than just data science PhDs and Hadoop clusters – it requires a mindset shift. What is the right mix of talent to make that happen? What kind of projects need to be undertaken and how to phase them? How to separate the hype of advanced techniques like machine learning from what will work for a business in the now and here? Why is scaling important and how does it usually get undermined? As you already have realized while solving this for your organization, the approach requires a mix of EQ and IQ. While there is no silver bullet, in this video, we discuss how we can be proactively aware of the common pitfalls, and avoid being blindsided by them on our journey.
EthicsMachine Learningmachine learning fairnessConferencesposted by Sray Agarwal Aug 23, 2019
Optuna: An Automatic Hyperparameter Optimization Framework
hyperparameterMachine LearningoptunaWest 2019Conferencesposted by Crissman Loomis Aug 22, 2019
What is Augmented Programming?
augmented machine learningaugmented programmingConferencesposted by Gideon Mann Aug 21, 2019
What is Implicit Deep Learning?
Deep Learningimplicit deep learningimplicit rulesConferencesposted by Laurent El Ghaoui Aug 19, 2019
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data science bootcampdata science mini bootcampjob trainingmini bootcampCareer Insightsposted by ODSC Team Aug 19, 2019
7 Reasons Your Data Science Resume is Suboptimal
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Why the Government of Tomorrow is Also a Data Organisation
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AI and Small Business: It’s Not Just for Enterprises
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