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Neural Networks and Deep

Track :

Programming

Lessons no : 31

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What will you learn in this course?
  • Design and implement neural network architectures for deep learning applications using Python and TensorFlow
  • Apply backpropagation and gradient descent algorithms to optimize neural network models effectively
  • Analyze neural network performance metrics to improve accuracy and reduce overfitting in deep learning projects
  • Utilize convolutional and recurrent neural networks for image recognition, natural language processing, and speech recognition
  • Explain the biological inspiration behind neural networks and their relevance to brain-inspired AI models
  • Develop custom neural network models tailored to specific data types and real-world problems
  • Implement data preprocessing, normalization, and augmentation techniques to enhance neural network training
  • Troubleshoot common issues in neural network training such as vanishing gradients and overfitting
  • Evaluate the ethical considerations and potential biases in deep learning models and neural network applications
  • Integrate neural networks into larger AI systems for automation, predictive analytics, and intelligent decision-making

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Lessons | 31
Lecture 1 Introduction to Neural Networks | Deep Learning and Neural Networks
00:08:11

Lecture 1 Introduction to Neural Networks | Deep Learning and Neural Networks
Lecture 2 Supervised Learning | Deep Learning and Neural Networks
00:07:05

Lecture 2 Supervised Learning | Deep Learning and Neural Networks
Lecture 3 Why is Deep Learning getting Popular | Deep Learning and Neural Networks
00:08:09

Lecture 3 Why is Deep Learning getting Popular | Deep Learning and Neural Networks
Lecture 4 Binary Classification | Deep Learning and Neural Networks
00:07:59

Lecture 4 Binary Classification | Deep Learning and Neural Networks
Lecture 5 Logistic Regression | Deep Learning and Neural Networks
00:06:21

Lecture 5 Logistic Regression | Deep Learning and Neural Networks
Lecture 6 Logistic Regression Cost Function | Deep Learning and Neural Networks
00:11:03

Lecture 6 Logistic Regression Cost Function | Deep Learning and Neural Networks
Lecture 7 Gradient Descent | Deep Learning and Neural Networks
00:14:05

Lecture 7 Gradient Descent | Deep Learning and Neural Networks
Lecture 8 Derivatives Part 1 | Derivative from First Principle
00:12:06

Lecture 8 Derivatives Part 1 | Derivative from First Principle
Lecture 9 Derivatives Part 2 | Derivative of x2 sinx cosx ln x polynomials | Power rule
00:22:55

Lecture 9 Derivatives Part 2 | Derivative of x2 sinx cosx ln x polynomials | Power rule
Lecture 10 Derivatives Part 3 | Sum Rule
00:06:07

Lecture 10 Derivatives Part 3 | Sum Rule
Lecture 11 Derivatives Part 4 | Derivative of e x Exponential Function
00:08:12

Lecture 11 Derivatives Part 4 | Derivative of e x Exponential Function
Lecture 12 Derivatives Part 5 | Product Rule of Differentiation
00:10:43

Lecture 12 Derivatives Part 5 | Product Rule of Differentiation
Lecture 13 Derivatives Part 6 | Quotient Rule of Differentiation
00:11:25

Lecture 13 Derivatives Part 6 | Quotient Rule of Differentiation
Lecture 14 Computation Graph | Deep Learning
00:04:16

Lecture 14 Computation Graph | Deep Learning
Lecture 15 Derivatives with Computation Graph | Backpropagation | Deep Learning
00:14:47

Lecture 15 Derivatives with Computation Graph | Backpropagation | Deep Learning
Lecture 16 Logistic Regression Gradient Descent | Deep Learning
00:13:11

Lecture 16 Logistic Regression Gradient Descent | Deep Learning
Lecture 17 Gradient Descent on m Examples | Deep Learning
00:12:14

Lecture 17 Gradient Descent on m Examples | Deep Learning
Lecture 18 Vectorization | Deep Learning
00:08:30

Lecture 18 Vectorization | Deep Learning
Lecture 19 Vectorization examples using NumPy | Deep Learning
00:12:10

Lecture 19 Vectorization examples using NumPy | Deep Learning
Lecture 20 Vectorizing Logistic Regression Forward Propagation | Deep Learning
00:12:21

Lecture 20 Vectorizing Logistic Regression Forward Propagation | Deep Learning
Lecture 21 Vectorizing Logistic Regression Backpropagation | Deep Learning
00:13:45

Lecture 21 Vectorizing Logistic Regression Backpropagation | Deep Learning
Lecture 22 NumPy Overview | Deep Learning
00:08:40

Lecture 22 NumPy Overview | Deep Learning
Lecture 23 NumPy Creating Arrays | Deep Learning
00:10:00

Lecture 23 NumPy Creating Arrays | Deep Learning
Lecture 24 NumPy linspace zeros ones | Deep Learning
00:08:57

Lecture 24 NumPy linspace zeros ones | Deep Learning
Lecture 25 NumPy Indexing Arrays | Deep Learning
00:11:31

Lecture 25 NumPy Indexing Arrays | Deep Learning
Lecture 26 NumPy Slicing NumPy Arrays | Deep Learning
00:12:58

Lecture 26 NumPy Slicing NumPy Arrays | Deep Learning
Lecture 27 NumPy Broadcasting NumPy Arrays | Deep Learning | NumPy Broadcasting |
00:10:33

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

Lecture 28 One hidden layer Neural Network | Deep Learning
00:05:59

Lecture 28 One hidden layer Neural Network | Deep Learning
Lecture 29 Neural Network Representation | Deep Learning
00:06:36

Lecture 29 Neural Network Representation | Deep Learning
Lecture 30 Neural Network Computation | Deep Learning
00:10:16

Lecture 30 Neural Network Computation | Deep Learning
Lecture 32 Activation Functions Neural Networks | Deep Learning
00:15:54

Lecture 32 Activation Functions Neural Networks | Deep Learning


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Bader Mualla

B 2025-05-10

Soumya B.S

wonderful course to learn neural networks 2024-10-08

RENISHA J

its very useful 2024-01-01

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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 .