neural networks
Background
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Provider: Alura
Deep dive into the mathematical mechanics of training. Explored the logic behind backpropagation, gradient descent, and the optimization of loss functions to adjust internal weights and ensure model convergence.
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Provider: Alura
Practical implementation of neural networks using the Keras high-level API. Focused on data preparation, building Sequential models, and executing training cycles for supervised learning tasks.
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Provider: Alura
Foundational implementation of neural networks. Covered data preprocessing for deep learning, building sequential models, and understanding activation functions in a supervised learning context.
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Provider: Alura
Comprehensive training covering the end-to-end lifecycle of deep learning models. Focused on tensor operations, building custom neural architectures, and implementing training loops using PyTorch for complex non-linear problems.
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Provider: Alura
This certification focused on the low-level implementation of neural architectures using Tensors and computational graphs, where I leveraged the torch.nn module to construct Multi-Layer Perceptrons and define custom weight initialization strategies for non-linear classification challenges.