-
Save Sarah Connor with Data Science
Data science and data privacy are deeply interwoven, and must be carefully considered by practitioners. In comparing the Safe Harbour and Expert Determination data obfuscation approaches, Safe Harbour has been very popular among data engineers but has fundamental limitations, where Expert Determination offers important advantages.
-
 Avoid These Five Behaviors That Make You Look Like A Data Novice
If you are new to the Data Science industry or a well-versed veteran in all things data and analytics, there are always key pitfalls that each of us can easily slide into if we are not careful. These behaviors not only make us appear like novices, but they can risk our position as a trustworthy, likable data partner with stakeholder.
-
New Computing Paradigm for AI: Processing-in-Memory (PIM) Architecture
As larger deep neural networks are trained on the latest and fastest chip technologies, an important challenge remains that bottlenecks performance -- and it is not compute power. You can try to calculate a DNN as fast as possible, but there is data -- and it has to move. Data pipelines on the chip are expensive and new solutions must be developed to advance capabilities.
-
 The 20 Python Packages You Need For Machine Learning and Data Science
By Matthew T. Dearing on October 14, 2021 in Data Science, Keras, Machine Learning, Matplotlib, numpy, Pandas, Plotly, Python, PyTorch, scikit-learn, TensorFlowDo you do Python? Do you do data science and machine learning? Then, you need to do these crucial Python libraries that enable nearly all you will want to do.
-
How I Built A Perfect Model And Got Into Trouble
Data-driven decisions, actionable insights, business impact—you've seen these buzzwords in data science jobs descriptions. But, just focusing on these terms doesn't automatically lead to the best results. Learn from this real-world scenario that followed data-driven indecisiveness, found misleading insights, and initially created a negative business impact.
-
Scaling human oversight of AI systems for difficult tasks – OpenAI approach
The foundational idea of Artificial Intelligence is that it should demonstrate human-level intelligence. So, unless a model can perform as a human might do, its intended purpose is missed. Here, recent OpenAI research into full-length book summarization focuses on generating results that make sense to humans with state-of-the-art results that leverage scalable AI-enhanced human-in-the-loop feedback.
-
Building and Operationalizing Machine Learning Models: Three tips for success
With more enterprises implementing machine learning to improve revenue and operations, properly operationalizing the ML lifecycle in a holistic way is crucial for data teams to make their projects efficient and effective.
-
38 Free Courses on Coursera for Data Science
There are so many online resources for learning data science, and a great deal of it can be used at no cost. This collection of free courses hosted by Coursera will help you enhance your data science and machine learning skills, no matter your current level of expertise.
-
Important Statistics Data Scientists Need to Know
Several fundamental statistical concepts must be well appreciated by every data scientist -- from the enthusiast to the professional. Here, we provide code snippets in Python to increase understanding to bring you key tools that bring early insight into your data.
-
MLOps and ModelOps: What’s the Difference and Why it Matters
These two terms are often used interchangeably. However, there are key distinctions between the functionality and features each provide, and the AI value and scalability at your organization depend on them.
|
Avoid These Five Behaviors That Make You Look Like A Data Novice
The 20 Python Packages You Need For Machine Learning and Data Science