512,81 €
569,79 €
-10% with code: EXTRA
Deep Learning
512,81 € 
- We will send in 10–14 business days.
Deep Learning: From Algorithmic Essence to Industrial Practice introduces the fundamental theories of deep learning, engineering practices, and their deployment and application in the industry. It provides a detailed explanation of classic convolutional neural networks, recurrent neural networks, and transformer networks based on self-attention mechanisms, along with their variants, combining code demonstrations. Additionally, it covers the applications of these models in areas such as image cl…
569.79
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- Author: Shuhao Wang
- Publisher: Elsevier
- ISBN-10: 0443439540
- ISBN-13: 9780443439544
- Format: 15.2 x 22.6 x 2.3 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Deep Learning: From Algorithmic Essence to Industrial Practice introduces the fundamental theories of deep learning, engineering practices, and their deployment and application in the industry. It provides a detailed explanation of classic convolutional neural networks, recurrent neural networks, and transformer networks based on self-attention mechanisms, along with their variants, combining code demonstrations. Additionally, it covers the applications of these models in areas such as image classification, object detection, semantic segmentation, etc. The book also considers the advancements in deep reinforcement learning and generative adversarial networks.EXTRA 10 % discount with code: EXTRA
512,81 €
569,79 €
We will send in 10–14 business days.
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- Author: Shuhao Wang
- Publisher: Elsevier
- ISBN-10: 0443439540
- ISBN-13: 9780443439544
- Format: 15.2 x 22.6 x 2.3 cm, minkšti viršeliai
- Language: English English
Deep Learning: From Algorithmic Essence to Industrial Practice introduces the fundamental theories of deep learning, engineering practices, and their deployment and application in the industry. It provides a detailed explanation of classic convolutional neural networks, recurrent neural networks, and transformer networks based on self-attention mechanisms, along with their variants, combining code demonstrations. Additionally, it covers the applications of these models in areas such as image classification, object detection, semantic segmentation, etc. The book also considers the advancements in deep reinforcement learning and generative adversarial networks.
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