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Tag: behaviorcloning

  • Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques

    Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques


    Price: $14.95
    (as of Dec 28,2024 07:24:53 UTC – Details)




    ASIN ‏ : ‎ B08GFQ7YH6
    Publisher ‏ : ‎ Packt Publishing; 1st edition (August 14, 2020)
    Publication date ‏ : ‎ August 14, 2020
    Language ‏ : ‎ English
    File size ‏ : ‎ 35577 KB
    Text-to-Speech ‏ : ‎ Enabled
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 334 pages
    Page numbers source ISBN ‏ : ‎ 1838646302


    Self-driving cars have been a hot topic in the tech world for years now, and with advancements in deep learning and computer vision technology, we are getting closer to making autonomous vehicles a reality. In this post, we will explore how deep neural networks and behavior-cloning techniques are being applied to build self-driving cars.

    Deep learning, a subset of artificial intelligence, has revolutionized the field of computer vision by enabling machines to learn from large amounts of data and make decisions without being explicitly programmed. This technology is crucial for self-driving cars as they need to accurately perceive and interpret their surroundings in real-time to navigate safely.

    Behavior-cloning, on the other hand, involves training a model to mimic the actions of a human driver. By collecting data from sensors such as cameras, lidar, and radar, the model learns to predict steering angles, throttle, and brake commands based on the input it receives. This approach is useful for teaching self-driving cars to navigate complex scenarios and handle unexpected situations on the road.

    By combining deep learning with behavior-cloning techniques, researchers and engineers are making significant strides in building self-driving cars that can safely and efficiently navigate through traffic. Companies like Tesla, Waymo, and Uber are already testing autonomous vehicles on public roads, and the technology is continuously improving.

    If you are interested in learning more about how deep learning and computer vision are being applied to build self-driving cars, there are plenty of resources available online, including research papers, tutorials, and open-source projects. With the rapid advancements in technology, we can expect to see more autonomous vehicles on the roads in the near future, bringing us closer to a safer and more efficient transportation system.
    #Applied #Deep #Learning #Computer #Vision #SelfDriving #Cars #Build #autonomous #vehicles #deep #neural #networks #behaviorcloning #techniques

  • Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques

    Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques


    Price: $48.99 – $46.57
    (as of Nov 21,2024 16:37:56 UTC – Details)




    Publisher ‏ : ‎ Packt Publishing (August 14, 2020)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 332 pages
    ISBN-10 ‏ : ‎ 1838646302
    ISBN-13 ‏ : ‎ 978-1838646301
    Item Weight ‏ : ‎ 1.28 pounds
    Dimensions ‏ : ‎ 9.25 x 7.52 x 0.7 inches


    In this post, we will explore the fascinating world of self-driving cars and how deep learning and computer vision are revolutionizing the automotive industry. By applying cutting-edge techniques such as deep neural networks and behavior-cloning, developers can now build autonomous vehicles that can navigate complex environments with ease.

    Deep learning is a subset of artificial intelligence that mimics the way the human brain works, allowing machines to learn from data and make decisions without being explicitly programmed. In the context of self-driving cars, deep learning algorithms can analyze sensor data such as images, lidar, and radar to recognize objects, pedestrians, and road signs, enabling the vehicle to understand its surroundings and make intelligent decisions.

    Computer vision is another crucial technology for self-driving cars, as it enables the vehicle to “see” and interpret its environment in real-time. By using cameras and other sensors, computer vision algorithms can detect lane markings, traffic lights, and obstacles on the road, allowing the autonomous vehicle to navigate safely and efficiently.

    Behavior cloning is a technique that involves training a neural network to mimic the behavior of a human driver. By collecting data from human drivers and using it to train the neural network, developers can teach the autonomous vehicle how to drive like a human, making it more intuitive and adaptive in real-world scenarios.

    By combining deep learning and computer vision with behavior-cloning techniques, developers can create self-driving cars that are capable of navigating complex environments, making split-second decisions, and ensuring the safety of passengers and pedestrians. The future of transportation is bright, and with the power of deep learning and computer vision, we are one step closer to achieving fully autonomous vehicles on our roads.
    #Applied #Deep #Learning #Computer #Vision #SelfDriving #Cars #Build #autonomous #vehicles #deep #neural #networks #behaviorcloning #techniques