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Deepnote Comes Out of Beta to Make Data Science and Analytics Collaborative

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Deepnote, an early-stage startup backed by Accel and Index Ventures, launched version 1.0 today, opening up to the general availability of collaborative data science notebooks to data teams worldwide. This press release features multimedia. Deepnote comes out of Beta to make data science and analytics collaborative. Data team efficacy relies on the process of access to, exploration of, and collaboration around data--for example, when an organization needs to make a data-informed decision, it will rely on data teams to explore datasets and share insights that lead to action. Too often, this process is siloed within a single department, findings are inconsistent, and insights quickly become out of date.


Gradient Descent: Design Your First Machine Learning Model

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Gradient descent is an optimization algorithm that is used to train machine learning models and is now used in a neural network. Training data helps the model learn over time as gradient descent act as an automatic system that tunes parameters to achieve better results. These parameters are updated after each iteration until the function achieves the smallest possible error. The red arrow in the figure below is a gradient and by updating our parameters after each iteration we can reduce loss which is our primary goal. According to Arthur Samuel, gradient descent is the automatic processing of altering weights to maximize performance Fast AI.


Deepnote Comes Out of Beta to Make Data Science and Analytics Collaborative

#artificialintelligence

Deepnote, an early-stage startup backed by Accel and Index Ventures, launched version 1.0, opening up to the general availability of collaborative data science notebooks to data teams worldwide. Data team efficacy relies on the process of access to, exploration of, and collaboration around data--for example, when an organization needs to make a data-informed decision, it will rely on data teams to explore datasets and share insights that lead to action. Too often, this process is siloed within a single department, findings are inconsistent, and insights quickly become out of date. The people analytics team at Gusto is one of hundreds of teams already using Deepnote. "Deepnote has been instrumental in centralizing much of our People Analytics work. With Deepnote, we can collaborate on our data pipelines, data science, and analytics efforts all from a single platform. Deepnote's publishing capabilities also provide our internal stakeholders a rich user experience to easily view insights and see where they should take action."


SQL Just Got Machine Learning

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The coalescence of machine learning tools into the Python ecosystem makes sense when you consider all steps that are required to train and test models: cleaning, transformation, visualization, and so on. There's so much iteration involved in machine learning that using a data science programming language seems necessary. However, the marriage of Python and machine learning, while sensible, does have a trade off: Database professionals are more likely to speak SQL than Python. The 2020 Stack Overflow survey bears this out nicely, showing an ML cluster centered around Python, and a separate cluster linking SQL with database technologies. Based on this, if we assume that data/analytics engineers are "closest" to their company's data, then why not put tools in their hands that unlock the full potential of their domain expertise? This is where MindsDB comes in.


Global Big Data Conference

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Deepnote, a startup that offers data scientists an IDE-like collaborative online experience for building their machine learning models, today announced that it has raised a $3.8 million seed round led by Index Ventures and Accel, with participation from YC and Credo Ventures, as well as a number of angel investors, including OpenAI's Greg Brockman, Figma's Dylan Field, Elad Gil, Naval Ravikant, Daniel Gross and Lachy Groom. Built around standard Jupyter notebooks, Deepnote wants to provide data scientists with a cloud-based platform that allows them to focus on their work by abstracting away all of the infrastructure. So instead of having to spend a few hours setting up their environment, a student in a data science class, for example, can simply come to Deepnote and get started. In its current form, Deepnote doesn't charge for its service, despite the fact that it allows its users to work with large data sets and train their models on cloud-based machines with attached GPUs. As Deepnote co-founder and CEO (and ex-Mozilla engineer) Jakub Jurových told me, though, he believes that the most important feature of the service is its ability to allow users to collaborate.