Generative Adversarial Networks (GANs) in Practice
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 42 lectures (4h 1m) | Size: 1.41 GB 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 in deep learning that discuss its algorithms, theories, and applications. Also, because of its capabilities and potentials 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 Network and its 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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~Get Your Files Here !/1. Introduction/1. Introduction.mp4
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~Get Your Files Here !/2. Machine Learning/1. Machine Learning.mp4
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~Get Your Files Here !/2. Machine Learning/1.1 Lecture 2.pdf
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~Get Your Files Here !/2. Machine Learning/1.2 Probabilistic Machine Learning Advanced Topics.html
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~Get Your Files Here !/2. Machine Learning/11.1 SuperVize Me What’s the Difference Between Supervised, Unsupervised, Semi-Supervised and Reinforcement Learning.html
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~Get Your Files Here !/2. Machine Learning/22. Underfitting and Overfitting.mp4
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~Get Your Files Here !/2. Machine Learning/5. Supervised Learning.mp4
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~Get Your Files Here !/2. Machine Learning/5.1 Artificial Intelligence A Modern Approach.html
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~Get Your Files Here !/2. Machine Learning/7. Unsupervised Learning.mp4
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~Get Your Files Here !/2. Machine Learning/7.1 Unsupervised Learning Foundations of Neural Computation.html
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~Get Your Files Here !/2. Machine Learning/9. Semi-Supervised Learning.mp4
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~Get Your Files Here !/3. Artificial Neural Networks/1. Neural Networks.mp4
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~Get Your Files Here !/3. Artificial Neural Networks/8. Applications of Artificial Neural Networks.mp4
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~Get Your Files Here !/4. Deep Learning/1. What is Deep Learning.mp4
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~Get Your Files Here !/4. Deep Learning/20. Residual Neural Network Learning (ResNets).mp4