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  2. IdeaPad - Wikipedia

    en.wikipedia.org/wiki/IdeaPad

    IdeaPad (stylized as IDEAPΛD and formerly ideapad) is a line of consumer-oriented laptop computers designed, developed and marketed by Lenovo. The IdeaPad mainly competes against computers such as Acer 's Aspire , Dell 's Inspiron and XPS , HP 's Pavilion , Envy , Stream , and Spectre , Samsung 's Sens and Toshiba 's Satellite .

  3. IdeaPad S series - Wikipedia

    en.wikipedia.org/wiki/IdeaPad_S_Series

    The IdeaPad S10 was Lenovo's first netbook. [4] While Engadget found the design unremarkable, the low starting price was well-received. [4] The S10 featured a 10.2-inch (260 mm) TFT active matrix 1024×576 or 1024×600 display with an 80 or 160 GB hard disk drive and 512 MB or 1 GB DDR2 Random Access Memory, both of which could be upgraded via a trap door on the bottom of the netbook.

  4. Lenovo IdeaPad Yoga 13 - Wikipedia

    en.wikipedia.org/wiki/Lenovo_IdeaPad_Yoga_13

    Best Buy released an alternative version of the Yoga 13 with an Intel Core i5 processor (vs. Lenovo's base model's i3 processor) and no Microsoft Office (whereas Lenovo's base model includes Microsoft Office). Its smaller cousin, Yoga 11, which runs Windows RT (as opposed to the Yoga 13, running Windows 8), was released in December 2012. [1] [2]

  5. ThinkPad X1 series - Wikipedia

    en.wikipedia.org/wiki/ThinkPad_X1_series

    X1 Nano was the lightest ThinkPad ever at the time at just 1.99 pounds (907 g) and also Lenovo's first ThinkPad based on Intel Evo platform, powered by 11th Gen Intel Core processors. [ citation needed ] The machine has a 13-inch 16:10 screen with four speakers with Dolby Atmos and four 360-degree microphones.

  6. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...

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