Udemy - Generative Adversarial Networks (GANs) in Practice

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[ DevCourseWeb.com ] Udemy - Generative Adversarial Networks (GANs) in Practice
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 01 - Introduction
    • 001 Introduction.mp4 (84.3 MB)
    • 001 Introduction_en.vtt (6.4 KB)
    • 36659484-Introduction.pdf (2.5 MB)
    02 - Machine Learning
    • 001 Machine Learning.mp4 (84.5 MB)
    • 001 Machine Learning_en.vtt (6.4 KB)
    • 002 Supervised Learning.mp4 (41.0 MB)
    • 002 Supervised Learning_en.vtt (3.7 KB)
    • 003 Unsupervised Learning.mp4 (34.3 MB)
    • 003 Unsupervised Learning_en.vtt (3.2 KB)
    • 004 Semi-Supervised Learning.mp4 (23.0 MB)
    • 004 Semi-Supervised Learning_en.vtt (2.7 KB)
    • 005 Learning Methods Comparison.mp4 (29.9 MB)
    • 005 Learning Methods Comparison_en.vtt (2.0 KB)
    • 006 Reinforcement Learning.mp4 (39.7 MB)
    • 006 Reinforcement Learning_en.vtt (3.0 KB)
    • 007 Learning Example.mp4 (31.9 MB)
    • 007 Learning Example_en.vtt (2.3 KB)
    • 008 Design a Learning System.mp4 (50.0 MB)
    • 008 Design a Learning System_en.vtt (3.8 KB)
    • 009 Leaning Error Types.mp4 (20.1 MB)
    • 009 Leaning Error Types_en.vtt (1.4 KB)
    • 010 Underfitting and Overfitting.mp4 (13.6 MB)
    • 010 Underfitting and Overfitting_en.vtt (1.1 KB)
    • 011 Clustering-kmeans.mp4 (52.7 MB)
    • 011 Clustering-kmeans_en.vtt (7.7 KB)
    • 012 Data Preprocessing.mp4 (127.7 MB)
    • 012 Data Preprocessing_en.vtt (23.3 KB)
    • 013 Coding Example using Scikit-Learn.mp4 (91.4 MB)
    • 013 Coding Example using Scikit-Learn_en.vtt (11.6 KB)
    • 36660332-Lecture-2.pdf (1.0 MB)
    • 38190436-clustering.ipynb (42.9 KB)
    • external-assets-links.txt (1.1 KB)
    03 - Artificial Neural Networks
    • 001 Neural Networks.mp4 (85.6 MB)
    • 001 Neural Networks_en.vtt (6.4 KB)
    • 002 Applications of Artificial Neural Networks.mp4 (32.4 MB)
    • 002 Applications of Artificial Neural Networks_en.vtt (2.0 KB)
    • 003 Single-Layer Neural Networks (Perceptron).mp4 (19.2 MB)
    • 003 Single-Layer Neural Networks (Perceptron)_en.vtt (1.3 KB)
    • 004 Multi-Layer Neural Networks.mp4 (39.3 MB)
    • 004 Multi-Layer Neural Networks_en.vtt (2.6 KB)
    • 005 Activation Functions.mp4 (22.5 MB)
    • 005 Activation Functions_en.vtt (1.9 KB)
    • 006 Neural Network Example with Number.mp4 (23.5 MB)
    • 006 Neural Network Example with Number_en.vtt (1.5 KB)
    • 007 TensorFlow Practicing.mp4 (293.4 MB)
    • 007 TensorFlow Practicing_en.vtt (33.9 KB)
    • 008 TensorFlow Practicing-MLP.mp4 (188.8 MB)
    • 008 TensorFlow Practicing-MLP_en.vtt (23.0 KB)
    • 39379018-lect-21.ipynb (31.5 KB)
    • 39380246-TensorFlow-MLP.ipynb (206.8 KB)
    • external-assets-links.txt (0.5 KB)
    04 - Deep Learning
    • 001 What is Deep Learning.mp4 (40.4 MB)
    • 001 What is Deep Learning_en.vtt (2.4 KB)
    • 002 Deep Learning Applications.mp4 (42.9 MB)
    • 002 Deep Learning Applications_en.vtt (2.3 KB)
    • 003 Deep Learning Algorithms and Architectures.mp4 (12.0 MB)
    • 003 Deep Learning Algorithms and Architectures_en.vtt (1.0 KB)
    • 004 Convolutional Neural Networks (CNNs).mp4 (48.9 MB)
    • 004 Convolutional Neural Networks (CNNs)_en.vtt (4.4 KB)
    • 005 Recurrent Neural Networks(RNNs).mp4 (57.5 MB)
    • 005 Recurrent Neural Networks(RNNs)_en.vtt (5.1 KB)
    • 006 Long Short-Term Memory (LSTM).mp4 (28.5 MB)
    • 006 Long Short-Term Memory (LSTM)_en.vtt (3.6 KB)
    • 007 Residual Neural Network Learning (ResNets).mp4 (24.5 MB)
    • 007 Residual Neural Network Learning (ResNets)_en.vtt (2.2 KB)
    • 008 Classifying Images with Deep Convolutional Neural Networks.mp4 (169.4 MB)
    • 008 Classifying Images with Deep Convolutional Neural Networks_en.vtt (20.2 KB)
    • 009 Modeling Sequential Data Using Recurrent Neural Networks.mp4 (105.4 MB)
    • 009 Modeling Sequential Data Using Recurrent Neural Networks_en.vtt (14.3 KB)
    • 39503732-image-classifying-using-DNN-p1.ipynb (33.2 KB)
    • 39534670-RNN-p1.ipynb (28.3 KB)
    • external-assets-links.txt (1.0 KB)
    • movie_data.csv (62.8 MB)
    05 - Generative Adversarial Networks
    • 001 What is a GAN.mp4 (28.7 MB)
    • 001 What is a GAN_en.vtt (2.3 KB)
    • 002 GAN Applications.mp4 (11.7 MB)
    • 002 GAN Applications_en.vtt (0.8 KB)
    • 003 Type of GANs.mp4 (14.8 MB)
    • 003 Type of GANs_en.vtt (0.7 KB)
    • 004 DCGANs.mp4 (30.2 MB)
    • 004 DCGANs_en.vtt (2.7 KB)
    • 005 SGAN.mp4 (14.7 MB)
    • 005 SGAN_en.vtt (1.3 KB)
    • 006 Conditional GAN.mp4 (19.2 MB)
    • 006 Conditional GAN_en.vtt (2.0 KB)
    • 007 Cycle GAN.mp4 (26.2 MB)
    • 007 Cycle GAN_en.vtt (1.8 KB)
    • 008 Simple Gan.mp4 (96.5 MB)
    • 008 Simple Gan_en.vtt (10.8 KB)
    • 009 DCGAN.mp4 (53.6 MB)
    • 009 DCGAN_en.vtt (5.5 KB)
    • 39534952-First-GAN.ipynb (1.2 MB)
    • 39535112-DCGAN.ipynb (79.3 KB)
    • external-assets-links.txt (0.2 KB)
    06 - GAN for MNIST and FASHION
    • 001 MNIST using GAN.mp4 (14.2 MB)
    • 001 MNIST using GAN_en.vtt (1.5 KB)
    • 002 Initial Setup.mp4 (91.7 MB)
    • 002 Initial Setup_en.vtt (6.7 KB)
    • 003 MMNIST Handwritten Digit Dataset.mp4 (12.3 MB)
    • 003 MMNIST Handwritten Digit Dataset_en.vtt (1.0 KB)
    • 004 Load and Prepare the Dataset.mp4 (64.1 MB)
    • 004 Load and Prepare the Dataset_en.vtt (5.4 KB)
    • 005 Create the Models.mp4 (77.7 MB)
    • 005 Create the Models_en.vtt (6.0 KB)
    • 006 Define and Train the Model.mp4 (14.9 MB)
    • 006 Define and Train the Model_en.vtt (1.3 KB)
    • external-assets-links.txt (0.0 KB)

Description

Generative Adversarial Networks (GANs) in Practice



https://DevCourseWeb.com

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.70 GB | Duration: 7h 16m

With Introductory Review on Artificial Neural Networks and Deep Learning Algorithms and Models

What you'll learn
The fundamentals of Artificial Neural Networks (ANNs) and reviews state-of-the-art DL examples.
The fundamental of Deep learning and the most popular algorithms.
The most popular GAN algorithms features and requirements .
How to implement a GAN model in PRACTICE.
Several examples and applications of GAN.
Requirements
Probability,
Calculus,
Basic of Python, Tensor Flow, Keras, and Numpy.
Description
Deep learning is one of the most recent and advanced topics in machine learning, with several applications in many fields. It shows promising results in many areas, from computer vision to drug discovery and stock market prediction. There are many books and articles on deep learning that discuss its algorithms, theories, and applications. Also, because of its capabilities and potential in solving different problems by deploying different data types, many researchers and people who are not in computer science or related fields are interested in learning and using deep learning architectures in their projects.

This course gives you some fundamentals of artificial neural networks and deep learning and then has focused on Generative Adversarial networks and their applications with some coding examples to understand the concepts better. The course is suitable for people who are new in the machine learning field and deep learning and would like to learn how to implement deep learning algorithms (especially GAN algorithms) using python, TensorFlow, and Keras.



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Udemy - Generative Adversarial Networks (GANs) in Practice

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2.7 GB
seeders:6
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Udemy - Generative Adversarial Networks (GANs) in Practice

Torrent hash: 8C80A720AD714E006510F7B7DA7148C00CC5EC17