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Udemy | Artificial Intelligence Masterclass [FTU]

  • Category Other
  • Type Tutorials
  • Language English
  • Total size 6.1 GB
  • Uploaded By SunRiseZone
  • Downloads 935
  • Last checked 3 months ago
  • Date uploaded 4 months ago
  • Seeders 38
  • Leechers 13

Infohash : 0CBFBE4BDD220E48976639FF1B095C571A8A26C6

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Enter the new era of Hybrid AI Models optimized by Deep NeuroEvolution, with a complete toolkit of ML, DL & AI models

Created by : Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team
Last updated : 7/2019
Language : English
Course Source : //www.udemy.com/artificial-intelligence-masterclass/

What you'll learn

• How to Build an AI
• How to Build a Hybrid Intelligent System
• Fully-Connected Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• AutoEncoders
• Variational AutoEncoders
• Mixture Density Network
• Deep Reinforcement Learning
• Policy Gradient
• Genetic Algorithms
• Evolution Strategies
• Covariance-Matrix Adaptation Evolution Strategies (CMA-ES)
• Controllers
• Meta Learning
• Deep NeuroEvolution

Course content
all 89 lectures 12:01:58

Requirements

• High school mathematics
• A bit of coding experience

Description

Today, we are bringing you the king of our AI courses...:

The Artificial Intelligence MASTERCLASS

Are you keen on Artificial Intelligence? Do want to learn to build the most powerful AI model developed so far and even play against it? Sounds tempting right...

Then Artificial Intelligence Masterclass course is the right choice for you. This ultimate AI toolbox is all you need to nail it down with ease. You will get 10 hours step by step guide and the full roadmap which will help you build your own Hybrid AI Model from scratch.

In this course, we will teach you how to develop the most powerful Artificial intelligence model based on the most robust Hybrid Intelligent System. So far this model proves to be the best state of the art AI ever created beating its predecessors at all the AI competitions with incredibly high scores.

This Hybrid Model is aptly named the Full World Model, and it combines all the state of the art models of the different AI branches, including Deep Learning, Deep Reinforcement Learning, Policy Gradient, and even, Deep NeuroEvolution.

By enrolling in this course you will have the opportunity to learn how to combine the below models in order to achieve best performing artificial intelligence system:


• Fully-Connected Neural Networks

• Convolutional Neural Networks

• Recurrent Neural Networks

• Variational AutoEncoders

• Mixed Density Networks

• Genetic Algorithms

• Evolution Strategies

• Covariance Matrix Adaptation Evolution Strategy (CMA-ES)

• Parameter-Exploring Policy Gradients

• Plus many others

Therefore, you are not getting just another simple artificial intelligence course but all in one package combining a course and a master toolkit, of the most powerful AI models. You will be able to download this toolkit and use it to build hybrid intelligent systems. Hybrid Models are becoming the winners in the AI race, so you must learn how to handle them already.

In addition to all this, we will also give you the full implementations in the two AI frameworks: TensorFlow and Keras. So anytime you want to build an AI for a specific application, you can just grab those model you need in the toolkit, and reuse them for different projects!

Don’t wait to join us on this EPIC journey in mastering the future of the AI - the hybrid AI Models.

Who this course is for :

• Anyone interested in Artificial Intelligence, Deep Learning, or Machine Learning.



Files:

[FreeTutorials.Us] Udemy - Artificial Intelligence Masterclass 0. Websites you may like
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  • How you can help Team-FTU.txt (0.2 KB)
1. Introduction
  • 1. Updates on Udemy Reviews.mp4 (22.0 MB)
  • 1. Updates on Udemy Reviews.srt (3.5 KB)
  • 1. Updates on Udemy Reviews.vtt (3.0 KB)
  • 2. Introduction + Course Structure + Demo.mp4 (195.3 MB)
  • 2. Introduction + Course Structure + Demo.srt (21.9 KB)
  • 2. Introduction + Course Structure + Demo.vtt (19.2 KB)
  • 3. BONUS Learning Paths.html (2.4 KB)
  • 4. Your Three Best Resources.mp4 (134.5 MB)
  • 4. Your Three Best Resources.srt (13.3 KB)
  • 4. Your Three Best Resources.vtt (11.8 KB)
  • 5. Download the Resources here.html (0.8 KB)
  • 6. Meet your instructors!.html (0.7 KB)
10. Step 9 - Reinforcement Learning
  • 1. Welcome to Step 9 - Reinforcement Learning.html (0.4 KB)
  • 2. What is Reinforcement Learning.mp4 (68.6 MB)
  • 2. What is Reinforcement Learning.srt (18.1 KB)
  • 2. What is Reinforcement Learning.vtt (16.0 KB)
  • 3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.mp4 (154.3 MB)
  • 3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.srt (27.0 KB)
  • 3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.vtt (23.5 KB)
  • 4. Full Code Section.html (0.4 KB)
11. Step 10 - Deep NeuroEvolution
  • 1. Welcome to Step 10 - Deep NeuroEvolution.html (1.2 KB)
  • 2. Deep NeuroEvolution.mp4 (108.8 MB)
  • 2. Deep NeuroEvolution.srt (15.1 KB)
  • 2. Deep NeuroEvolution.vtt (13.3 KB)
  • 3. Evolution Strategies.mp4 (119.4 MB)
  • 3. Evolution Strategies.srt (13.0 KB)
  • 3. Evolution Strategies.vtt (11.4 KB)
  • 4. Genetic Algorithms.mp4 (149.1 MB)
  • 4. Genetic Algorithms.srt (17.7 KB)
  • 4. Genetic Algorithms.vtt (15.5 KB)
  • 5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).mp4 (144.1 MB)
  • 5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).srt (17.2 KB)
  • 5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).vtt (15.2 KB)
  • 6. Parameter-Exploring Policy Gradients (PEPG).mp4 (143.9 MB)
  • 6. Parameter-Exploring Policy Gradients (PEPG).srt (16.4 KB)
  • 6. Parameter-Exploring Policy Gradients (PEPG).vtt (14.5 KB)
  • 7. OpenAI Evolution Strategy.mp4 (108.1 MB)
  • 7. OpenAI Evolution Strategy.srt (10.4 KB)
  • 7. OpenAI Evolution Strategy.vtt (9.2 KB)
12. The Final Run
  • 1. The Whole Implementation.mp4 (273.7 MB)
  • 1. The Whole Implementation.srt (28.3 KB)
  • 1. The Whole Implementation.vtt (24.9 KB)
  • 2. Download the whole AI Masterclass folder here.html (1.0 KB)
  • 2.1 AI Masterclass.zip.zip (17.1 MB)
  • 3. Installing the required packages.mp4 (158.7 MB)
  • 3. Installing the required packages.srt (17.5 KB)
  • 3. Installing the required packages.vtt (15.0 KB)
  • 4. The Final Race Human Intelligence vs. Artificial Intelligence.mp4 (125.1 MB)
  • 4. The Final Race Human Intelligence vs. Artificial Intelligence.srt (15.8 KB)
  • 4. The Final Race Human Intelligence vs. Artificial Intelligence.vtt (13.5 KB)
  • 5. THANK YOU bonus video.mp4 (29.2 MB)
  • 5. THANK YOU bonus video.srt (2.3 KB)
  • 5. THANK YOU bonus video.vtt (2.0 KB)
13. Bonus Lectures
  • 1. YOUR SPECIAL BONUS.html (1.1 KB)
2. Step 1 - Artificial Neural Network
  • 1. Welcome to Step 1 - Artificial Neural Network.html (0.6 KB)
  • 2. Plan of Attack.mp4 (15.8 MB)
  • 2. Plan of Attack.srt (3.9 KB)
  • 2. Plan of Attack.vtt (3.5 KB)
  • 3. The Neuron.mp4 (98.8 MB)
  • 3. The Neuron.srt (24.7 KB)
  • 3. The Neuron.vtt (21.6 KB)
  • 4. The Activation Function.mp4 (45.4 MB)
  • 4. The Activation Function.srt (11.8 KB)
  • 4. The Activation Function.vtt (10.4 KB)
  • 5. How do Neural Networks work.mp4 (81.9 MB)
  • 5. How do Neural Networks work.srt (19.1 KB)
  • 5. How do Neural Networks work.vtt (16.8 KB)
  • 6. How do Neural Networks learn.mp4 (112.1 MB)
  • 6. How do Neural Networks learn.srt (19.0 KB)
  • 6. How do Neural Networks learn.vtt (16.5 KB)
  • 7. Gradient Descent.mp4 (60.6 MB)
  • 7. Gradient Descent.srt (14.2 KB)
  • 7. Gradient Descent.vtt (12.3 KB)
  • 8. Stochastic Gradient Descent.mp4 (67.3 MB)
  • 8. Stochastic Gradient Descent.srt (12.2 KB)
  • 8. Stochastic Gradient Descent.vtt (10.8 KB)
  • 9. Backpropagation.mp4 (43.1 MB)
  • 9. Backpropagation.srt (7.3 KB)
  • 9. Backpropagation.vtt (6.4 KB)
3. Step 2 - Convolutional Neural Network
  • 1. Welcome to Step 2 - Convolutional Neural Network.html (0.4 KB)
  • 10. Softmax & Cross-Entropy.mp4 (118.0 MB)
  • 10. Softmax & Cross-Entropy.srt (25.3 KB)
  • 10. Softmax & Cross-Entropy.vtt (22.1 KB)
  • 2. Plan of Attack.mp4 (21.8 MB)
  • 2. Plan of Attack.srt (5.3 KB)
  • 2. Plan of Attack.vtt (4.7 KB)
  • 3. What are Convolutional Neural Networks.mp4 (108.0 MB)
  • 3. What are Convolutional Neural Networks.srt (22.2 KB)
  • 3. What are Convolutional Neural Networks.vtt (19.4 KB)
  • 4. Step 1 - The Convolution Operation.mp4 (97.9 MB)
  • 4. Step 1 - The Convolution Operation.srt (23.3 KB)
  • 4. Step 1 - The Convolution Operation.vtt (20.4 KB)
  • 5. Step 1 Bis - The ReLU Layer.mp4 (53.4 MB)
  • 5. Step 1 Bis - The ReLU Layer.srt (9.3 KB)
  • 5. Step 1 Bis - The ReLU Layer.vtt (8.2 KB)
  • 6. Step 2 - Pooling.mp4 (140.2 MB)
  • 6. Step 2 - Pooling.srt (21.0 KB)
  • 6. Step 2 - Pooling.vtt (18.4 KB)
  • 7. Step 3 - Flattening.mp4 (7.9 MB)
  • 7. Step 3 - Flattening.srt (2.6 KB)
  • 7. Step 3 - Flattening.vtt (2.3 KB)
  • 8. Step 4 - Full Connection.mp4 (194.3 MB)
  • 8. Step 4 - Full Connection.srt (28.5 KB)
  • 8. Step 4 - Full Connection.vtt (25.0 KB)
  • 9. Summary.mp4 (30.3 MB)
  • 9. Summary.srt (6.1 KB)
  • 9. Summary.vtt (5.4 KB)
4. Step 3 - AutoEncoder
  • 1. Welcome to Step 3 - AutoEncoder.html (0.4 KB)
  • 10. Stacked AutoEncoders.mp4 (16.4 MB)
  • 10. Stacked AutoEncoders.srt (2.4 KB)
  • 10. Stacked AutoEncoders.vtt (2.1 KB)
  • 11. Deep AutoEncoders.mp4 (12.0 MB)
  • 11. Deep AutoEncoders.srt (2.7 KB)
  • 11. Deep AutoEncoders.vtt (2.4 KB)
  • 2. Plan of Attack.mp4 (15.8 MB)
  • 2. Plan of Attack.srt (3.2 KB)
  • 2. Plan of Attack.vtt (2.9 KB)
  • 3. What are AutoEncoders.mp4 (94.6 MB)
  • 3. What are AutoEncoders.srt (16.3 KB)
  • 3. What are AutoEncoders.vtt (14.3 KB)
  • 4. A Note on Biases.mp4 (8.6 MB)
  • 4. A Note on Biases.srt (2.1 KB)
  • 4. A Note on Biases.vtt (1.8 KB)
  • 5. Training an AutoEncoder.mp4 (50.3 MB)
  • 5. Training an AutoEncoder.srt (9.5 KB)
  • 5. Training an AutoEncoder.vtt (8.4 KB)
  • 6. Overcomplete Hidden Layers.mp4 (28.1 MB)
  • 6. Overcomplete Hidden Layers.srt (5.7 KB)
  • 6. Overcomplete Hidden Layers.vtt (5.0 KB)
  • 7. Sparse AutoEncoders.mp4 (57.5 MB)
  • 7. Sparse AutoEncoders.srt (8.8 KB)
  • 7. Sparse AutoEncoders.vtt (7.8 KB)
  • 8. Denoising AutoEncoders.mp4 (24.1 MB)
  • 8. Denoising AutoEncoders.srt (3.6 KB)
  • 8. Denoising AutoEncoders.vtt (3.2 KB)
  • 9. Contractive AutoEncoders.mp4 (20.5 MB)
  • 9. Contractive AutoEncoders.srt (3.6 KB)
  • 9. Contractive AutoEncoders.vtt (3.1 KB)
5. Step 4 - Variational AutoEncoder
  • 1. Welcome to Step 4 - Variational AutoEncoder.html (0.4 KB)
  • 2. Introduction to the VAE.mp4 (72.8 MB)
  • 2. Introduction to the VAE.srt (11.0 KB)
  • 2. Introduction to the VAE.vtt (9.7 KB)
  • 3. Variational AutoEncoders.mp4 (26.3 MB)
  • 3. Variational AutoEncoders.srt (6.1 KB)
  • 3. Variational AutoEncoders.vtt (5.4 KB)
  • 4. Reparameterization Trick.mp4 (26.4 MB)
  • 4. Reparameterization Trick.srt (6.6 KB)
  • 4. Reparameterization Trick.vtt (5.8 KB)
6. Step 5 - Implementing the CNN-VAE
  • 1. Welcome to Step 5 - Implementing the CNN-VAE.html (2.3 KB)
  • 2. Introduction to Step 5.mp4 (58.8 MB)
  • 2. Introduction to Step 5.srt (10.8 KB)
  • 2. Introduction to Step 5.vtt (9.4 KB)
  • 3. Initializing all the parameters and variables of the CNN-VAE class.mp4 (71.7 MB)
  • 3. Initializing all the parameters and variables of the CNN-VAE class.srt (17.0 KB)
  • 3. Initializing all the parameters and variables of the CNN-VAE class.vtt (14.9 KB)
  • 4. Building the Encoder part of the VAE.mp4 (133.6 MB)
  • 4. Building the Encoder part of the VAE.srt (26.2 KB)
  • 4. Building the Encoder part of the VAE.vtt (22.8 KB)
  • 5. Building the V part of the VAE.mp4 (80.3 MB)
  • 5. Building the V part of the VAE.srt (13.5 KB)
  • 5. Building the V part of the VAE.vtt (11.8 KB)
  • 6. Building the Decoder part of the VAE.mp4 (92.9 MB)
  • 6. Building the Decoder part of the VAE.srt (13.0 KB)
  • 6. Building the Decoder part of the VAE.vtt (11.4 KB)
  • 7. Implementing the Training operations.mp4 (187.0 MB)
  • 7. Implementing the Training operations.srt (23.4 KB)
  • 7. Implementing the Training operations.vtt (20.4 KB)
  • 8. Full Code Section.html (4.0 KB)
  • 9. The Keras Implementation.html (7.7 KB)
7. Step 6 - Recurrent Neural Network
  • 1. Welcome to Step 6 - Recurrent Neural Network.html (0.5 KB)
  • 2. Plan of Attack.mp4 (10.5 MB)
  • 2. Plan of Attack.srt (3.4 KB)
  • 2. Plan of Attack.vtt (3.1 KB)
  • 3. What are Recurrent Neural Networks.mp4 (121.1 MB)
  • 3. What are Recurrent Neural Networks.srt (23.8 KB)
  • 3. What are Recurrent Neural Networks.vtt (20.8 KB)
  • 4. The Vanishing Gradient Problem.mp4 (111.2 MB)
  • 4. The Vanishing Gradient Problem.srt (20.8 KB)
  • 4. The Vanishing Gradient Problem.vtt (18.3 KB)
  • 5. LSTMs.mp4 (136.5 MB)
  • 5. LSTMs.srt (28.2 KB)
  • 5. LSTMs.vtt (24.6 KB)
  • 6. LSTM Practical Intuition.mp4 (187.4 MB)
  • 6. LSTM Practical Intuition.srt (21.0 KB)
  • 6. LSTM Practical Intuition.vtt (18.4 KB)
  • 7. LSTM Variations.mp4 (20.1 MB)
  • 7. LSTM Variations.srt (4.9 KB)
  • 7. LSTM Variations.vtt (4.3 KB)
8. Step 7 - Mixture Density Network
  • 1. Welcome to Step 7 - Mixture Density Network.html (0.5 KB)
  • 2. Introduction to the MDN-RNN.mp4 (83.4 MB)
  • 2. Introduction to the MDN-RNN.srt (12.7 KB)
  • 2. Introduction to the MDN-RNN.vtt (11.2 KB)
  • 3. Mixture Density Networks.mp4 (65.4 MB)
  • 3. Mixture Density Networks.srt (13.5 KB)
  • 3. Mixture Density Networks.vtt (12.0 KB)
  • 4. VAE + MDN-RNN Visualization.mp4 (45.3 MB)
  • 4. VAE + MDN-RNN Visualization.srt (7.5 KB)
  • 4. VAE + MDN-RNN Visualization.vtt (6.6 KB)
9. Step 8 - Implementing the MDN-RNN
  • 1. Welcome to Step 8 - Implementing the MDN-RNN.html (2.8 KB)
  • 10. Implementing the Training operations (Part 2).mp4 (162.9 MB)
  • 10. Implementing the Training operations (Part 2).srt (18.9 KB)
  • 10. Implementing the Training operations (Part 2).vtt (16.4 KB)
  • 11. Full Code Section.html (10.8 KB)
  • 12. The Keras Implementation.html (5.3 KB)
  • 2. Initializing all the parameters and variables of the MDN-RNN class.mp4 (99.5 MB)
  • 2. Initializing all the parameters and variables of the MDN-RNN class.srt (18.0 KB)
  • 2. Initializing all the parameters and variables of the MDN-RNN class.vtt (15.8 KB)
  • 3. Building the RNN - Gathering the parameters.mp4 (76.6 MB)
  • 3. Building the RNN - Gathering the parameters.srt (12.9 KB)
  • 3. Building the RNN - Gathering the parameters.vtt (11.3 KB)
  • 4. Building the RNN - Creating an LSTM cell with Dropout.mp4 (127.2 MB)
  • 4. Building the RNN - Creating an LSTM cell with Dropout.srt (21.8 KB)
  • 4. Building the RNN - Creating an LSTM cell with Dropout.vtt (19.2 KB)
  • 5. Building the RNN - Setting up the Input, Target, and Output of the RNN.mp4 (131.1 MB)
  • 5. Building the RNN - Setting up the Input, Target, and Output of the RNN.srt (20.0 KB)
  • 5. Building the RNN - Setting up the Input, Target, and Output of the RNN.vtt (17.7 KB)
  • 6. Building the RNN - Getting the Deterministic Output of the RNN.mp4 (125.5 MB)
  • 6. Building the RNN - Getting the Deterministic Output of the RNN.srt (16.4 KB)
  • 6. Building the RNN - Getting the Deterministic Output of the RNN.vtt (14.4 KB)
  • 7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.mp4 (147.0 MB)
  • 7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.srt (16.6 KB)
  • 7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.vtt (14.7 KB)
  • 8. Building the MDN - Getting the MDN parameters.mp4 (109.4 MB)
  • 8. Building the MDN - Getting the MDN parameters.srt (14.5 KB)
  • 8. Building the MDN - Getting the MDN parameters.vtt (12.8 KB)
  • 9. Implementing the Training operations (Part 1).mp4 (177.4 MB)
  • 9. Implementing the Training operations (Part 1).srt (20.5 KB)
  • 9. Implementing the Training operations (Part 1).vtt (17.8 KB)

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