pierre-lavieille/deep-neural-networks-with-pytorch

Deep Neural Networks with PyTorch IBM

Deep-Neural-Networks-with-PyTorch

This repository presents my implementation of the different labs of the Deep Neural Networks with PyTorch IBM certificate.

The course teach how to develop deep learning models using Pytorch. The course start with Pytorch's tensors and Automatic differentiation package. Then each section cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning is covered. Finally, several other Deep learning methods is covered.

Learning Outcomes:
Able to:

• explain and apply their knowledge of Deep Neural Networks and related machine learning methods
• know how to use Python libraries such as PyTorch for Deep Learning applications
• build Deep Neural Networks using PyTorch

Certificate