baidu comptition net

Baidu is one of chkina's other epicenters of ai

part of its focus n latest annual rport oct 2017:

 Baidu is working to develop AI-based platforms in the voice assist and autonomous driving markets. The company continues to work on DuerOS, as they think that “voice and natural language interface will become the future universal gateway to the digital world”. They also don’t see these projects as something that will bear fruits in many years. Baidu’s management sees a huge opportunity in this market and mentions some forecasts by IDC, which expects that 27% of homes and 51% of cars will be voice-powered by 2020.

The management also gave us an update on the overall state of their projects in autonomous driving, mentioning a significant growth in the number of partners in the ecosystem:

Apollo 1.5 has been very positively received by our partners and developers. Overall, in Q3, the number of Apollo ecosystem partners has grown from 54 to 74 with more OEMs, component and solution providers as well as startups joining the Apollo ecosystem. We reached strategic agreements with OEMs such BAIC Group; King Long, one of China's largest bus manufacturers and Chery and Jianghuai for commercial L3 productions in 2018 for buses and 2019 for cars as well as L4 production by 2021.

At baidu's us hq, andrew ng appears to have left. He moved from co-founding moocs at coursera stanford to head baidu usa back to this

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   Neural Networks and Deep Learning

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   This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization.

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   Sequence Models

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   This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization.

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