Deep Neural Networks with PyTorch (IBM)

AI colleagues, the Deep Neural Networks with PyTorch from IBM equips you  to develop deep learning models using  Pytorch. The course will start with Pytorch's  tensors and Automatic differentiation package. Then each section will 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 will be covered. Finally, several other Deep learning methods will be covered. After completing this program you will be 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 and build Deep Neural Networks using PyTorch. Skill-based training modules cover: 1) Tensor and Datasets, 2) Differentiation in PyTorch, 3) Simple Datasets, 4) Linear Regression, 2) Gradient Descent, 3) Prediction in One Dimension, 4) PyTorch Linear Regression Training Slope and Bia, 5) Training Parameters in PyTorch, 6) Multiple Input Output Linear Regression, 7) Multiple Output Linear Regression, 8) Linear Classifier, 9) Logistic Regression: Prediction, 10) Bernoulli Distribution and Maximum Likelihood Estimation, 11) Softmax Regression, 12) Deep Neural Networks, 13) Convolutional Neural Network, and 14) TorchVision Models.

Enroll today (eams & execs welcome): https://tinyurl.com/4uj75b2z 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy