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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.

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

Table of Contents
YOU, THIS COURSE AND US
INTRODUCTION TO PYTORCH AND NEURAL NETWORKS
PYTORCH TENSORS
GRADIENT DESCENT AND AUTOGRAD
REGRESSION AND CLASSIFICATION
CONVOLUTIONAL NEURAL NETWORKS IN PYTORCH
RECURRENT NEURAL NETWORKS IN PYTORCH
TRANSFER LEARNING AND PRE-TRAINED MODELS


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Tags: Example, PyTorch

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