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Lecture 25 NumPy Indexing Arrays | Deep Learning

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Lessons List | 31 Lesson

Lecture 1 Introduction to Neural Networks | Deep Learning and Neural Networks

Lecture 1 Introduction to Neural Networks | Deep Learning and Neural Networks

Lecture 2 Supervised Learning | Deep Learning and Neural Networks

Lecture 2 Supervised Learning | Deep Learning and Neural Networks

Lecture 3 Why is Deep Learning getting Popular | Deep Learning and Neural Networks

Lecture 3 Why is Deep Learning getting Popular | Deep Learning and Neural Networks

Lecture 4 Binary Classification | Deep Learning and Neural Networks

Lecture 4 Binary Classification | Deep Learning and Neural Networks

Lecture 5 Logistic Regression | Deep Learning and Neural Networks

Lecture 5 Logistic Regression | Deep Learning and Neural Networks

Lecture 6 Logistic Regression Cost Function | Deep Learning and Neural Networks

Lecture 6 Logistic Regression Cost Function | Deep Learning and Neural Networks

Lecture 7 Gradient Descent | Deep Learning and Neural Networks

Lecture 7 Gradient Descent | Deep Learning and Neural Networks

Lecture 8 Derivatives Part 1 | Derivative from First Principle

Lecture 8 Derivatives Part 1 | Derivative from First Principle

Lecture 9 Derivatives Part 2 | Derivative of x2 sinx cosx ln x polynomials | Power rule

Lecture 9 Derivatives Part 2 | Derivative of x2 sinx cosx ln x polynomials | Power rule

Lecture 10 Derivatives Part 3 | Sum Rule

Lecture 10 Derivatives Part 3 | Sum Rule

Lecture 11 Derivatives Part 4 | Derivative of e x Exponential Function

Lecture 11 Derivatives Part 4 | Derivative of e x Exponential Function

Lecture 12 Derivatives Part 5 | Product Rule of Differentiation

Lecture 12 Derivatives Part 5 | Product Rule of Differentiation

Lecture 13 Derivatives Part 6 | Quotient Rule of Differentiation

Lecture 13 Derivatives Part 6 | Quotient Rule of Differentiation

Lecture 14 Computation Graph | Deep Learning

Lecture 14 Computation Graph | Deep Learning

Lecture 15 Derivatives with Computation Graph | Backpropagation | Deep Learning

Lecture 15 Derivatives with Computation Graph | Backpropagation | Deep Learning

Lecture 16 Logistic Regression Gradient Descent | Deep Learning

Lecture 16 Logistic Regression Gradient Descent | Deep Learning

Lecture 17 Gradient Descent on m Examples | Deep Learning

Lecture 17 Gradient Descent on m Examples | Deep Learning

Lecture 18 Vectorization | Deep Learning

Lecture 18 Vectorization | Deep Learning

Lecture 19 Vectorization examples using NumPy | Deep Learning

Lecture 19 Vectorization examples using NumPy | Deep Learning

Lecture 20 Vectorizing Logistic Regression Forward Propagation | Deep Learning

Lecture 20 Vectorizing Logistic Regression Forward Propagation | Deep Learning

Lecture 21 Vectorizing Logistic Regression Backpropagation | Deep Learning

Lecture 21 Vectorizing Logistic Regression Backpropagation | Deep Learning

Lecture 22 NumPy Overview | Deep Learning

Lecture 22 NumPy Overview | Deep Learning

Lecture 23 NumPy Creating Arrays | Deep Learning

Lecture 23 NumPy Creating Arrays | Deep Learning

Lecture 24 NumPy linspace zeros ones | Deep Learning

Lecture 24 NumPy linspace zeros ones | Deep Learning

Lecture 25 NumPy Indexing Arrays | Deep Learning

Lecture 25 NumPy Indexing Arrays | Deep Learning

Lecture 26 NumPy Slicing NumPy Arrays | Deep Learning

Lecture 26 NumPy Slicing NumPy Arrays | Deep Learning

Lecture 27 NumPy Broadcasting NumPy Arrays | Deep Learning | NumPy Broadcasting |

Lecture 27 NumPy Broadcasting NumPy Arrays | Deep Learning | NumPy Broadcasting |

Lecture 28 One hidden layer Neural Network | Deep Learning

Lecture 28 One hidden layer Neural Network | Deep Learning

Lecture 29 Neural Network Representation | Deep Learning

Lecture 29 Neural Network Representation | Deep Learning

Lecture 30 Neural Network Computation | Deep Learning

Lecture 30 Neural Network Computation | Deep Learning

Lecture 32 Activation Functions Neural Networks | Deep Learning

Lecture 32 Activation Functions Neural Networks | Deep Learning

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Course Description

Deep Learning is associated with the transformation and extraction of features that attempt to establish a relationship between stimuli and associated neural responses present in the brain, whereas Neural Networks use neurons to transmit data in the form of input to get output with the help of the various connections .

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