Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in RNN: Why Gradients
In this video, we will understand why gradients. This clip is from the chapter "Deep learning: Recurrent Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we will...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in RNN: Chain Rule in Action
In this video, we will cover chain rules in action. This clip is from the chapter "Deep learning: Recurrent Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in RNN: Chain Rule
In this video, we will cover chain rules. This clip is from the chapter "Deep learning: Recurrent Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we will cover...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in CNNs: Why Derivatives
In this video, we will understand why derivatives. 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,...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in CNNs: Implementation in NumPy BackwardPass 4
In this video, we will cover implementation in NumPy BackwardPass 4. 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...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in CNNs: Implementation in NumPy BackwardPass 3
In this video, we will cover implementation in NumPy BackwardPass 3. 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...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in CNNs: Implementation in NumPy BackwardPass 2
In this video, we will cover implementation in NumPy BackwardPass 2. 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...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in CNNs: Implementation in NumPy BackwardPass 1
In this video, we will cover implementation in NumPy BackwardPass 1. 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...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in CNNs: Gradients of Convolutional Layer
In this video, we will cover gradients of convolutional layer. 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...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in CNNs: Extending to Multiple Filters
In this video, we will cover extending to multiple filters. 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...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Linear Algebra Module Python
In this video, we will cover linear algebra module Python. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and...
Brian McLogan
Determine the derivative expanding a binomial to use the power rule
👉 Learn how to find the derivative of a function using the power rule. The derivative of a function, y = f(x), is the measure of the rate of change of the function, y, with respect to the variable x. The process of finding the derivative...
Institute for New Economic Thinking
Janine Wedel - Behind the Scenes of International Banking Regulation
Five years into the Great Recession, discussion and political fights continue about the right approach to international banking supervision. How to avert the next financial crisis or at the very least lessen its damage? Given the topic's...
Brian McLogan
Learn how to find the derivative of tangent using the quotient rule
👉 Learn how to find the derivative of a function using the quotient rule. The derivative of a function, y = f(x), is the measure of the rate of change of the function, y, with respect to the variable x. The process of finding the...
Brian McLogan
Solving a falling ladder problem using related rates
👉 Learn how to take the derivative of a function. Learn how to find the derivative of a function using the chain rule. The derivative of a function, y = f(x), is the measure of the rate of change of the function, y, with respect to the...
Brian McLogan
Implicit differentiation using the product rule
👉 Learn how to find the derivative of an implicit function. The derivative of a function, y = f(x), is the measure of the rate of change of the function, y, with respect to the variable x. The process of finding the derivative of a...
Math Fortress
Calculus I: Derivatives of Polynomials and Natural Exponential Functions (Level 1 of 3)
This video will teach you the basics of calculating the derivative of simple polynomials and exponential functions.
Tarver Academy
One Piece of Advice for Starting School
In This Episode, Tyler Teaches Us About One Piece of Advice for Starting School
Brian McLogan
How to use u substition to evaluate the integral with e
👉 Learn how to evaluate the integral of a function. The integral, also called antiderivative, of a function, is the reverse process of differentiation. Integral of a function can be evaluated as indefinite integral or as a definite...
Brian McLogan
Determine the points of inflections with trig
👉 Learn how to find the points of inflection of a function given the equation or the graph of the function. The points of inflection of a function are the points where the graph of the function changes its concavity. The points of...
Brian McLogan
Take the derivative from the difference quotient without simplifying
👉 Learn how to evaluate the limit of a function using the difference quotient formula. The difference quotient is a measure of the average rate of change of the function over an interval, h. The limit of the difference quotient gives the...
Math Fortress
Calculus II: Integration By Parts (Level 2 of 6)
This video goes over 3 examples, covering the proper way to use the integration by parts formula. This video includes an example covering the two forms of the integration by parts formula, an example where rewriting of the integrand is...
Math Fortress
Calculus II: Integration By Parts (Level 4 of 6)
This video goes over an example, covering the proper way to find integrals that require the repeated application of the integration by parts formula specifically an integral that generates a constant multiple of the original integral. In...
Curated Video
Understanding Stationary Points through Second Derivative
The video is a presentation on stationary points, specifically using the second derivative. The presenter summarizes a previous lecture on finding maximum and minimum points and looking at the gradient function. They then introduce an...