MP4 | Video: AVC 1280x720 | Audio: AAC 48KHz 2ch | Duration: 3 hours 24 minutes | English | 755 MBVideo DescriptionIn 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 NNsGradient descent, forward and backward passes: The basic steps involved in training a neural networkPyTorch tensors: The building blocks used to store data in PyTorchAutograd: The PyTorch library used to perform gradient descentsRegression and classification models: Build a NN to perform regression and predict air quality and perform classification on salary dataConvolution, pooling, and CNNs: Understand how these layers mimic the visual cortex to identify imagesConvolutional Neural Networks: Classify house numbers using CNNsRecurrent Neural Networks: Predict language from names using RNNsTransfer 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 ApproachThis course will teach you about neurons and neural networks in depth, with practical examples.

Table of ContentsYOU, THIS COURSE AND USINTRODUCTION TO PYTORCH AND NEURAL NETWORKSPYTORCH TENSORSGRADIENT DESCENT AND AUTOGRADREGRESSION AND CLASSIFICATIONCONVOLUTIONAL NEURAL NETWORKS IN PYTORCHRECURRENT NEURAL NETWORKS IN PYTORCHTRANSFER LEARNING AND PRE-TRAINED MODELSScreenshots