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Deep Learning (DEEP-0001)

Deep Learning (DEEP-0001)

Publicado 3/13/2024, 10:15:27 AM, última modificação 3/13/2024, 10:21:05 AM

Ementa: Conceitos básicos de aprendizado de máquina; redes neurais profundas; otimização para aprendizado profundo; redes neurais convolucionais; métodos práticos e regularização; modelagem sequencial; redes recorrentes; redes recursivas; redes adversas generativas; detecção de objetos usando aprendizado profundo; processamento de linguagem natural; redes siamesas; e autoencoders.

Syllabus: Machine Learning Basics; Deep Feedforward Networks; Optimization for Deep Learning; Convolutional Networks; Practical Methodology and Regularization; Sequence Modeling; Recurrent Networks; Recursive Networks; Generative Adversarial Networks; Object detection with Deep Learning; Natural Language Processing; Siamese Network; and Autoencoders.

Bibliografia/Bibliography:

[1] Ian Goodfellow, Yoshua Bengio and Aaron Courville. 2016. Deep Learning. MIT Press.

[2] François Chollet. 2017. Deep Learning with Python. Manning Publications.

[3] Ovidiu Calin. 2020. Deep Learning Architectures: A Mathematical Approach. Springer Series in the Data Sciences.

[4] Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag.

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