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Deep Learning and Neural Networks Course
Description
The «Deep Learning and Neural Networks Course» provides a structured journey from foundational concepts to advanced techniques in modern AI. Starting with the «Foundations of Neural Networks», you’ll master the architecture, underlying mathematics, and core principles — including activation functions, backpropagation, and gradient descent. Next, you’ll explore «Deep Learning Architectures», progressing from multi‑layer perceptrons to state‑of‑the‑art models, understanding how depth enhances learning capacity. The course then dives into «Convolutional Neural Networks and Computer Vision Applications», teaching you to build systems for image classification, object detection, and more. In the «Recurrent Networks, Transformers, and Sequential Data Processing» module, you’ll learn to handle time‑series data, natural language, and sequences using RNNs, LSTMs, and transformer architectures. Finally, «Advanced Deep Learning» covers optimisation algorithms, regularisation techniques (dropout, L1/L2), and best practices for deploying models in real‑world scenarios. With hands‑on projects using TensorFlow/PyTorch, you’ll gain practical skills to tackle complex AI challenges and advance your career in machine learning.
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