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
Instructor Bio:
Eng Amr Nasr
- A computer engineering expert, tech lead, and AI solution architect who brings machine learning and deep learning to life across industries like space, healthcare, and agriculture, specializing in machine learning and computer vision.He has worked with leading avionics and WiFi technology companies in the U.S.
- He has guided students and engineers in Egypt’s space and tech ecosystems, sharing his expertise in neural networks, PyTorch, TensorFlow, and building scalable AI systems from the ground up
