Deep Convolutional Generative Adversarial Networks (DCGAN)
- 3.6
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Brief Introduction
Learn to create Generative Adversarial Networks (GAN) & Deep Convolutional Generative Adversarial Networks (DCGAN)Description
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:
Python coding: some revision is provided during this course
Gradient descent
Basic knowledge of neural networks
Requirements
- Requirements
- Basic neural networks knowledge would be helpful, however all the basic concepts will be covered in this course
Academy of Computing & Artificial Intelligence
3.6 Raiting
Tensorflow 2.0: Deep Learning and Artificial Intelligence
- 4.6
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Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More! Welcome to Tensorflow 2. 0! What an exciting time....
Deep Convolutional Generative Adversarial Networks (DCGAN)
- 3.6
-
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Learn to create Generative Adversarial Networks (GAN) & Deep Convolutional Generative Adversarial Networks (DCGAN) Two models are trained simultaneously by an adversarial process....
Introduction to Deep Learning
- 4.5
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The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding....
人工智慧：機器學習與理論基礎 (Artificial Intelligence - Learning & Theory)
- 4.6
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本課程第二部分著重在和人工智慧密不可分的機器學習。課程內容包含了機器學習基礎理論（包含 1990 年代發展的VC理論）、分類器（包含決策樹及支援向量機）、神經網路（包含深度學習）及增強式學習（包含深度增強式學習。 此部份技術包含最早追溯至 1950 年代直到最近 2016 年附近的最新發展。此課程從基礎理論開始，簡介了各機器學習主流技法以及從淺層學習架構演變到最近深度架構的轉換。 本課程之核心目標為： （一）使同學對人工智慧相關的機器學習技術有基礎概念 （二）同學能夠理解機器學習基礎理論、分類器、神經網路、增強式學習 （三）同學能將相關技術應用到自己的問題上 修課前，基礎背景知識： 需要的先備知識：計算機概論 建議的先備知識：資料結構與演算法 Concept learning 1-1 Brief Introduction to Machine Learning, Learning from Example...
