This video explains Amazon API Gateway use cases.
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This clip is from the chapter "API Gateway RESTful API Development with Synchronous Lambda Event Sources" of the series "AWS Serverless Microservices with Patterns and Best...

Here, we will learn to implement the POST operation for the “Courses” result.
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This clip is from the chapter "Improving Your Minimal API" of the series "Hands-On .NET Minimal API for Web Developers".This section takes us...

Let's learn to write a classification method that will train our module and help us get predictions.
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This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine Learning: Random Forest with Python from...

NLP is a tool for structuring data in a way that AI systems can process that deals with language. NLP uses AI to 'read' through a document and extract key information. This clip is from the chapter "Recurrent Neural Networks" of the...

This video demonstrates how to build a deeper and wider neural network model. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains artificial neural networks...

This video demonstrates functional API versus sequential API. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains artificial neural networks where you will...

This video demonstrates the architecture and how to carry out the code using TensorFlow in collab and building a convolutional neural network. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in...

This video demonstrates convolutional operations in 1-dimension. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you...

Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks. GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets. This clip is from the chapter "Recurrent Neural Networks"...

This video explains the cross-entropy function, which is designed under the assumption that the variable you are trying to predict is binary. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in...

The Convolution layer (CONV) uses filters that perform convolution operations as it is scanning the input with respect to its dimensions. Its hyperparameters include the filter size and stride. The resulting output is called a feature...

Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks. It can be used to train Elman networks. The algorithm was independently derived by numerous researchers. This clip...

This video explains backward propagation, which is defined by the optimization problem called the gradient descent algorithm. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This...

Functions allow us to write organized sections of resuable code. You'll create your first function in this video.

This video helps in using custom queue construct in the main stack with AWS CDK.
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This clip is from the chapter "Creating AWS SQS Queue Infrastructure with AWS CDK – Polling Checkout Basket" of the series "AWS Serverless...

The author will introduce you to Macro programs.
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This clip is from the chapter "Macro Facility Fundamentals" of the series "Complete SAS Programming Guide - Learn SAS and Become a Data Ninja".This section focuses on the Macro...

In this video, you will learn about the “_call_” method, which can be used to make an object callable.
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This clip is from the chapter "Magic Functions" of the series "Python - Object-Oriented Programming".This section...

In this lesson, we will look at inheritance in further detail and understand accessing object attributes and multi-inheritance.
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This clip is from the chapter "Inheritance and Abstraction" of the series "Python -...

In this video, we will cover output arguments and return statement.
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This clip is from the chapter "Basics for Data Science: Python for Data Science and Data Analysis" of the series "Data Science and Machine Learning (Theory...

In this video, we will cover function practice-output arguments and return statement.
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This clip is from the chapter "Basics for Data Science: Python for Data Science and Data Analysis" of the series "Data Science and Machine...

In this video, we will cover similarity based methods introduction.
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine...

In this video, we will cover DNN PyTorch CIFAR10 example.
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This clip is from the chapter "Deep learning: Artificial Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...

In this video, we will cover definition.
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This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A to...

In this video, we will cover DNN gradient descent.
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This clip is from the chapter "Deep learning: Artificial Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this...