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Deep Learning for Data Architects: Unleash the power of Python's deep learning algorithms
Focuses on deep learning concepts and their practical applications within the field of data architecture and data science. It introduces foundational elements like Artificial Neural Networks, Python libraries for data handling (such as Pandas and NumPy), and tools for data analysis and visualization (like Sweetviz, AutoViz, and Lux). The text explores specific deep learning architectures including Convolutional Neural Networks (CNNs) for image tasks like classification, object detection, and segmentation, as well as Recurrent Neural Networks (RNNs), including LSTM and GRU variants, for sequential data. Finally, it discusses Generative Adversarial Networks (GANs) for data generation and the groundbreaking Transformer models used in Natural Language Processing (NLP). Overall, the sources provide both theoretical background and Python implementation examples for these advanced machine learning techniques.
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Published on 1 month ago