Deep Convolutional Generative Adversarial Networks (DCGAN)
Deep Convolutional Generative Adversarial Networks (DCGAN), available at $39.99, has an average rating of 4.05, with 17 lectures, based on 26 reviews, and has 3058 subscribers.
You will learn about Learn the basic principles of Generative Adversarial Networks (GAN) Learn the basic principles of Deep Convolutional Generative Adversarial Networks (DCGAN) Build a Deep Convolutional Generative Adversarial Networks (DCGAN) with step by step guidance Setup the code for Deep Convolutional Generative Adversarial Networks (DCGAN) This course is ideal for individuals who are Anyone who wish to improve the deep learning knowledge or students who wish to learn the new trends Deep Convolutional Generative Adversarial Networks (DCGAN) It is particularly useful for Anyone who wish to improve the deep learning knowledge or students who wish to learn the new trends Deep Convolutional Generative Adversarial Networks (DCGAN).
Enroll now: Deep Convolutional Generative Adversarial Networks (DCGAN)
Summary
Title: Deep Convolutional Generative Adversarial Networks (DCGAN)
Price: $39.99
Average Rating: 4.05
Number of Lectures: 17
Number of Published Lectures: 17
Number of Curriculum Items: 17
Number of Published Curriculum Objects: 17
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Learn the basic principles of Generative Adversarial Networks (GAN)
- Learn the basic principles of Deep Convolutional Generative Adversarial Networks (DCGAN)
- Build a Deep Convolutional Generative Adversarial Networks (DCGAN) with step by step guidance
- Setup the code for Deep Convolutional Generative Adversarial Networks (DCGAN)
Who Should Attend
- Anyone who wish to improve the deep learning knowledge
- students who wish to learn the new trends Deep Convolutional Generative Adversarial Networks (DCGAN)
Target Audiences
- Anyone who wish to improve the deep learning knowledge
- students who wish to learn the new trends Deep Convolutional Generative Adversarial Networks (DCGAN)
Generative Adversarial Networks (GANs) & Deep Convolutional Generative Adversarial Networks (DCGAN)are one of the most interesting and trending ideas in computer science today.
Two models are trained simultaneously by an adversarial process. A generator , learns to create images that look real, while a discriminator learns to tell real images apart from fakes.
At the end of the Course you will understand the basics of Python Programming and the basics ofGenerative Adversarial Networks (GANs) & Deep Convolutional Generative Adversarial Networks (DCGAN) .
The course will have step by step guidance
Import TensorFlow and other libraries
Load and prepare the dataset
Create the models (Generator & Discriminator)
Define the loss and optimizers (Generator loss , Discriminator loss)
Define the training loop
Train the model
Analyze the output
Suggested Prerequisites:
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Python coding: some revision is provided during this course
-
Gradient descent
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Basic knowledge of neural networks
Course Curriculum
Chapter 1: Introduction
Lecture 1: What are GANs ? Generative Adversarial Networks (GANs)
Lecture 2: Import TensorFlow and other libraries
Lecture 3: Load and prepare the dataset
Lecture 4: Create the models – The Generator
Lecture 5: Create the models – The Discriminator
Lecture 6: Define the loss and optimizers
Lecture 7: Define the training loop
Lecture 8: Train the model – Part
Lecture 9: Create a GIF
Lecture 10: GAN vs DCGAN difference
Lecture 11: Source code – for the course
Lecture 12: Download the Source code
Lecture 13: Output
Chapter 2: Extra Reading
Lecture 1: Generative Adversarial Networks
Lecture 2: Deep Convolutional Generative Adversarial Network – Research paper
Chapter 3: Revision – Neural Networks
Lecture 1: Setting up the Environment : Anaconda
Lecture 2: KERAS Tutorial : Developing an Artificial Neural Network in Python [Step by Step
Instructors
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Academy of Computing & Artificial Intelligence
Senior Lecturer / Project Supervisor / Consultant
Rating Distribution
- 1 stars: 3 votes
- 2 stars: 0 votes
- 3 stars: 4 votes
- 4 stars: 4 votes
- 5 stars: 15 votes
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