Deep Learning & Neural Networks: Master CNNs, RNNs, Transformers, and prepare for industry certification using PyTorch
Sub Category
- Other IT & Software
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Objectives
- Design and implement Feedforward Neural Networks from scratch using Python and core computational libraries.
- Master optimization techniques, including gradient descent variations, regularization (dropout), and hyperparameter tuning.
- Build, train, and evaluate robust Convolutional Neural Networks (CNNs) for complex image classification tasks.
- Develop practical Recurrent Neural Networks (RNNs), LSTMs, and GRUs for sequence data modeling and prediction.
- Understand the underlying mechanism of attention and implement modern Transformer architecture fundamentals.
- Efficiently utilize major deep learning frameworks (PyTorch and TensorFlow) for large-scale model development.
Pre Requisites
- Strong foundation in Python programming (including NumPy and Pandas).
- Working knowledge of basic linear algebra (vectors, matrices) and differential calculus concepts.
- Familiarity with core machine learning concepts (e.g., supervised learning, regression, classification).
- Access to a personal computer capable of running Jupyter Notebooks and modern deep learning environments.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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