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Generate Images Using DC-GAN | Generative Adversarial Networks | Machine Learning Projects 6|Edureka

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This Edureka video on ‘Generate Images Using DC-Gan’s’ will give you an overview of Generate Images Using DC-Gan’s using Machine Learning and will help you understand various important concepts that concern Generate Images Using DC-Gan’s with ML.

Following pointers are covered in this Generate Images Using DC-Gan’s:

Agenda 00:00
Problem Statement 00:45
Workflow of the Project 01:25
Tools and Frameworks 01:55
Hands-on: Project 02:30

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———Edureka Machine Learning Projects———

๐Ÿ”ต Plant Leaf Disease Detection with GUI: https://bit.ly/36Y6l8g
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๐Ÿ”ต Emoji Prediction using LSTM: https://bit.ly/2TDuPjR
๐Ÿ”ต Color old photographs using Autoencoders: https://bit.ly/3BQg7r9
๐Ÿ”ต Handwritten Digit Recognition on MNIST dataset: https://bit.ly/3zTCxGf

———๐„๐๐ฎ๐ซ๐ž๐ค๐š ๐Ž๐ง๐ฅ๐ข๐ง๐ž ๐“๐ซ๐š๐ข๐ง๐ข๐ง๐  ๐š๐ง๐ ๐‚๐ž๐ซ๐ญ๐ข๐Ÿ๐ข๐œ๐š๐ญ๐ข๐จ๐ง———

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#edureka #edurekamachinelearning #machinelearning # GenerateImagesUsingDC-Gan’s #machinelearningproject #machinelearningtutorial #edurekatraining

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About the Course :

Edurekaโ€™s Machine Learning Course using Python is designed to make you grasp the concepts of Machine Learning. The Machine Learning training will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in the python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.
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Why Learn Machine Learning with Python?

Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
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