Deeplearning.ai logo

Improving Deep Neural Networks

Jul 7, 2019

Obtained with a grade of 83/100, multiple subject have been treated:

  • Introduction to Neural Networks (Perceptron, MLP...)
  • Theory of the Gradient Descent (Learning Rate Decay...)
  • Theory of ConvNets (convolution, pooling...)
  • Case study (AlexNet, VGG, GoogLeNet, ResNet...)
  • Visualization (Manifold Untangling, t-sne...)
  • Advanced optimization (Momentum, NAG, AdaDelta, Adam...)
  • Unsupervised Learning (GAN, Context Encoder...)
  • RCNN and derivates

See certificate..
AI DL ML
Avatar
Thomas Di Martino
PhD Student in AI & Remote Sensing

My research interests include deep learning technologies, automatic feature extraction and computer vision, all of them applied to Remote Sensing problematics, more precisely to Synthetic Aperture Radar (SAR) acquisitions.

Related