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.
I mastered the mechanics of deep model optimization by configuring advanced solvers like Adam and SGD, while strategically applying regularization methods such as Dropout and weight decay to ensure robust generalization and prevent overfitting during the backpropagation process.
Focused on sequential data architectures, I utilized PyTorch to design and implement Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), effectively addressing the vanishing gradient problem to maintain temporal dependencies in complex time-series and natural language tasks.
Advanced implementation of computer vision models. Focused on CNN architecture design, including convolutional layers, pooling, and dropout strategies for feature extraction and image classification tasks.
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.
Awarded for performance in one of the most prestigious algorithmic programming competitions in the world. Demonstrates ability to solve complex problems under tight time constraints.
Certified C2 level of proficiency in English, enabling seamless collaboration in international environments and consumption of advanced technical literature.
