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Packt - Learn By Example Pytorch
English | Size: 755.63 MB
Category: Programming
Build and train neural networks using APIs and libraries from PyTorch

In this course you'll learn about PyTorch APIs; these are closely integrated with native-Python, which makes its APIs intuitive and easy to follow for Python developers.

Here is what this course covers:

Neurons and neural networks: The basic functionality of a neuron and how neurons come together to build NNs
Gradient descent, forward and backward passes: The basic steps involved in training a neural network
PyTorch tensors: The building blocks used to store data in PyTorch
Autograd: The PyTorch library used to perform gradient descents
Regression and classification models: Build a NN to perform regression and predict air quality and perform classification on salary data
Convolution, pooling, and CNNs: Understand how these layers mimic the visual cortex to identify images
Convolutional Neural Networks: Classify house numbers using CNNs
Recurrent Neural Networks: Predict language from names using RNNs
Transfer learning: Use the Resnet-18 pre-trained model to classify images.
This course is built around hands-on demos using datasets from the real world. You'll be analyzing air quality data, salary data, images of house numbers, and name data in order to build your machine learning models.

This course will teach you about neurons and neural networks in depth, with practical examples.

What You Will Learn

Understand how neurons and neural networks work.
Understand gradient descent and forward and backward passes in a NN
Work with PyTorch tensors to store and manipulate data
Build and train regression and classification neural network models using PyTorch
Use pre-trained models to harness the power of transfer learning
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