Deep Learning & Neural Networks: Test your knowledge on Architectures, Optimization, Regularization, and Framework Conce
Sub Category
- IT Certifications
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Objectives
- Identify core neural network architectures, including MLPs, CNNs, RNNs, and modern Transformer models.
- Differentiate between various activation functions (ReLU, Sigmoid, Tanh, Softmax) and analyze their specific use cases and limitations.
- Explain the process of backpropagation and precisely define terminology related to modern gradient descent optimization methods (e.g., Adam, RMSprop).
- Master the implementation and theoretical necessity of regularization techniques such as Dropout, L1/L2 weight decay, and early stopping.
- Understand and correctly apply concepts related to the bias-variance tradeoff, overfitting, underfitting, and model capacity.
- Analyze initialization techniques (e.g., Xavier, He) and their role in preventing gradient vanishing or exploding during training.
- Accurately evaluate knowledge of modern DL framework components and conceptual differences between graph computation models.
- Apply knowledge of appropriate loss functions suitable for various classification, segmentation, and regression tasks.
- Successfully answer intermediate and advanced deep learning theory questions common in technical interviews and certifications.
- Pinpoint specific areas of weakness in core Deep Learning concepts that require focused review and subsequent study.
- Define the purpose and mechanism of Batch Normalization layers and evaluate their strategic placement within NN architectures.
- Critically assess hyperparameter tuning strategies based on model performance metrics like precision, recall, and F1 score.
Pre Requisites
- Basic understanding of Python programming.
- Familiarity with foundational Machine Learning concepts (e.g., supervised vs. unsupervised learning).
- Prior exposure to implementing basic neural networks using a DL framework like Keras or PyTorch.
- Knowledge of fundamental linear algebra and calculus concepts relevant to gradients and vector operations.
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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