Course Description:
- You will dive into neural networks and modern deep learning architectures. Study forward and backward propagation, activation functions, CNNs, RNNs, LSTMs, autoencoders, and GANs.
- Practical labs and assignments focus on real-world applications like digit recognition, image classification, and generative models
Course main points:
- Neural Networks Fundamentals:
- – Neural networks intuition
- – Neural network model
- Training & Advanced Architectures:
- – Training dynamics (backprop)
- – Activation functions (ReLU, Softmax)
- Deep Learning Architectures:
- – CNNs (Image Processing)
- – RNNs/LSTMs (Sequential Data)
- Unsupervised Deep Learning:
- – Autoencoders
- – GANs (Generative Models)
Course Duration:
36 Hrs
