The course will teach you how 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.
Acerca de este Curso
- 5 stars64,34 %
- 4 stars22,96 %
- 3 stars5,72 %
- 2 stars3,94 %
- 1 star3,01 %
Principales reseñas sobre DEEP NEURAL NETWORKS WITH PYTORCH
While there are some minor technical issues loading out of date libraries, the material and subjects are incredibly useful. This course is very difficult and welcome
Wonderful course!!! Best among all the courses under AI Engineer Certificate by IBM. Deep learning always haunted me with the maths involved but now I get a very good start with this.
Good pacing, great examples and the assignments are doable within the time allocated for them. Combines both technical information and applied code.
Great introduction to deep learning with pytorch. It would help if the notebooks in the labs take shorter to run so that the students can experiment with the code and the models.
¿Cuándo podré acceder a las lecciones y tareas?
¿Qué recibiré si me suscribo a este Certificado?
¿Tienes más preguntas? Visita el Centro de Ayuda al Estudiante.