Deep Learning

Go Zero-One by learning the much-needed fundamentals of “Deep Learning". In this course, you will learn " Neural Network, Deep Neural Networks, Optimization Algorithms, Foundation of CNN".

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Flexible Learning

Learn at your own pace and reach your personal goals on the schedule that works best for you.

Real-world Projects

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Live Mentor Workshops

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Verifiable Certificate

Upon successful completion of the Course, You will receive a Verifiable certificate with QR code.

Quiz & Mock Tests

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Assured Internship

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Deep Learning

Go Zero-One by learning the much-needed fundamentals of “Deep Learning". In this course, you will learn " Neural Network, Deep Neural Networks, Optimization Algorithms, Foundation of CNN".

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Includes:
  • Verifiable certificate
  • Quiz & mock tests
  • Live mentor workshops
  • 2 devices access*
Course Description

Welcome to " Deep Learning "

In this course, you will be learning “Neural Network, Deep Neural Networks, Optimization Algorithms, Foundation of CNN, Deep Convolutional Model, RNN”. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know to succeed in the industry. This course has a tremendous amount of content and resources so that you can learn everything you need to know - whatever is appropriate for your ability level. You will be able to learn at your own pace. You will always be able to come back to the content to review it or learn additional concepts when you are ready for them. You will get great value from this course and, more importantly, you will have a great time learning.

 

Happy Learning

What you will learn?
  • What is Neural Networks
  • Gradient Descent
  • Computation Graph and Its Derivative
  • Gradient Descent for Logistic Regression
  • Gradient Descent on m Training Examples
  • Vectorization
  • Vectorization of Logistic Regression
  • Neural Networks Representation
  • Compute Neural Network's Output
  • Activation Function
  • Derivative of Activation Functions
  • Deep L-Layered Neural Network
  • Forward Propagation in Deep Networks
  • Dropout Regularization
  • Normalizing Input
  • RMSProp
  • Learning Rate Decay
  • Why Convolution
  • Edge Detection Example
  • Padding
  • Strided Convolutions
  • Pooling Layers
  • Classic Networks
  • ResNet
  • Transfer Learning
  • Data Augmentation
  • Neural Style Transfer
  • Introduction to RNNs
  • Different Types of RNNs
  • Brief Introduction of GRU and LSTM
Requirements
  • No Prerequisites Required
Course Curriculum
Introduction to Neural Network
12 Lessons
  • Neural Network Representation
  • Computing a Neural Network's Output
  • Activation functions
  • Derivatives of activation functions
  • What is Neural Networks.
  • Revisiting Binary Classification And Logistic Regression
  • Gradient Descent
  • Computation Graph and It's Derivative
  • Gradient descent for Logistic Regression
  • Gradient Descent Over m Training Examples
  • Vectorization
  • Vectorization Of Logistic Regression
  • Deep L-layer neural network
  • Forward Propagation in Deep Networks
  • Dropout Regularization
  • Normalizing Input
  • Vanishing_Exploding Gradients
  • RMS Convolution
  • Learning Rate Decay
  • Why Convolution
  • Edge Detection Example
  • Padding
  • Strided Convolutions
  • Convolution Over Volume
  • One Layer Of Convolutional Network
  • Simple Convolutional Network Example
  • Pooling Layers
  • Classic Networks
  • ResNet
  • Transfer Learning
  • Data Augmentation
  • Neural Style Transfer
  • Introduction to RNN's
  • Different Types of RNN's
  • Brief Introduction of GRU and LSTM

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