Julia is an easy, fast, open source language that if written well performs nearly as well as low-level languages such as C and FORTRAN. Its design is a dance between specialization and abstraction, providing high machine...

The SymPy package computes symbolic solutions to simplify, expand, factor, differentiate, integrate, and solve equations. These problems cover a range of SymPy functions.

Two blocks oscillating via springs is a 2 DOF system. The final solution will have the form of a



a modal vector times cos(w1t - phi1) + another modal vector times cos(w

2t -phi2).

The system is hence said to be...

In this video, we will understand the learning objectives of this domain.
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This clip is from the chapter "Fundamental Principles of Machine Learning on Azure (30- 35%)" of the series "AI-900: Microsoft Azure AI Fundamentals...

Exploring additional usage of the NumPy Python library

Julia is an easy, fast, open source language that if written well performs nearly as well as low-level languages such as C and FORTRAN. Its design is a dance between specialization and abstraction, providing high machine...

Matrices are an important class of mathematical object used in many branches of mathematics, science and engineering. This lecture also introduces augmented matrices, a compact easy-to-manipulate representation of systems of linear...

How to perform a change of basis on a system of equations.

Performing diagonalization.

The NumPy library accelerates Python's number-crunching powers, while keeping Python's ease of use and flexibility. Learn in this video the basics of NumPy's use, where it shines, and where it's less effective.

Summarize the class and explain the class project

A system of linear equations represented as an augmented matrix can be simplified through the process of Gaussian elimination to row echelon form. At that point the matrix can be converted back into equations which are simpler and easy...

This video explains about the 2D arrays.
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This clip is from the chapter "Methods" of the series "The Complete Java Developer Course: From Beginner to Master".This section explains Methods that are a set of blocks of code...

Torque as the cross product is introduced. How to actually perform the cross product using matrices is reviewed and 4.5 examples are walked through. This is an AP Physics C: Mechanics topic.
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In this video, we will cover DNN implementation gradient step.
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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...

This video explains CloudWatch live dashboards and widgets.
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This clip is from the chapter "Create and Deploy Serverless Application Resources" of the series "AWS Cloud Development Kit - From Beginner to Professional".In this...

In this video, we will cover slicing-part 2.
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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 and Projects) A to Z".In...

This is third of the five-part video on implementation in NumPy BackwardPass.
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This clip is from the chapter "Gradient Descent in CNNs" of the series "Deep Learning CNN: Convolutional Neural Networks with Python".This section...

This video explains why activation function is required in DNN.
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This clip is from the chapter "DNN Foundation for Deep RL" of the series "Reinforcement Learning and Deep RL Python (Theory and Projects)".This section focuses...

This video explains about the implementation of the gradient step.
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This clip is from the chapter "DNN Foundation for Deep RL" of the series "Reinforcement Learning and Deep RL Python (Theory and Projects)".This section...

In this video, we will understand why activation function is required.
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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...

In this video, we will cover language modelling next word prediction Python 3.
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This clip is from the chapter "Deep learning: Recurrent Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and...

In this video, we will cover NumPy Pandas and Matplotlib (part 2).

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