Deep Learning with Python and PyTorch
Learn how to use Python and its popular libraries such as NumPy and Pandas, as well as the PyTorch Deep Learning library. You'll then apply them to build Neural Networks and Deep Learning models.
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Description
The course will teach you how to develop Deep Learning models using Pytorch while providing the necessary deep-learning background.
We'll start off with PyTorch's tensors and its Automatic Differentiation package. Then we'll cover different Deep Learning models in each section, beginning with fundamentals such as Linear Regression and logistic/softmax regression.
We'll then move on to Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers.
In the final part of the course, we'll focus on Convolutional Neural Networks and Transfer Learning (pre-trained models). Several other Deep Learning methods will also be covered.
We'll start off with PyTorch's tensors and its Automatic Differentiation package. Then we'll cover different Deep Learning models in each section, beginning with fundamentals such as Linear Regression and logistic/softmax regression.
We'll then move on to Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers.
In the final part of the course, we'll focus on Convolutional Neural Networks and Transfer Learning (pre-trained models). Several other Deep Learning methods will also be covered.
Pricing:
Free
Free
Level:
Intermediate
Intermediate
Duration:
6 weeks, 2h-4h/week
6 weeks, 2h-4h/week
Educator:
Joseph Santarcangelo
Joseph Santarcangelo
Organization:
IBM
IBM
Submitted by:
Coursearena
Coursearena
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