Artificial Intelligence Masterclass

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Artificial Intelligence Masterclass Artificial Intelligence Masterclass 3. Step 2 - Convolutional Neural Network
  • 8. Step 4 - Full Connection.mp4 (194.3 MB)
  • 1. Welcome to Step 2 - Convolutional Neural Network.html (0.4 KB)
  • 2. Plan of Attack.mp4 (15.8 MB)
  • 2. Plan of Attack.vtt (4.8 KB)
  • 3. What are Convolutional Neural Networks.mp4 (108.0 MB)
  • 3. What are Convolutional Neural Networks.vtt (19.9 KB)
  • 4. Step 1 - The Convolution Operation.mp4 (97.9 MB)
  • 4. Step 1 - The Convolution Operation.vtt (21.5 KB)
  • 5. Step 1 Bis - The ReLU Layer.mp4 (53.4 MB)
  • 5. Step 1 Bis - The ReLU Layer.vtt (8.5 KB)
  • 6. Step 2 - Pooling.mp4 (140.2 MB)
  • 6. Step 2 - Pooling.vtt (19.1 KB)
  • 7. Step 3 - Flattening.mp4 (7.9 MB)
  • 7. Step 3 - Flattening.vtt (2.3 KB)
  • 8. Step 4 - Full Connection.vtt (26.1 KB)
  • 9. Summary.mp4 (30.3 MB)
  • 9. Summary.vtt (5.7 KB)
  • 10. Softmax & Cross-Entropy.mp4 (118.0 MB)
  • 10. Softmax & Cross-Entropy.vtt (23.0 KB)
  • READ_ME.txt (0.4 KB)
    • 1. Introduction
    • 1. Updates on Udemy Reviews.mp4 (46.0 MB)
    • 1. Updates on Udemy Reviews.vtt (3.1 KB)
    • 2. Introduction + Course Structure + Demo.mp4 (156.8 MB)
    • 2. Introduction + Course Structure + Demo.vtt (19.9 KB)
    • 3. Your Three Best Resources.mp4 (143.3 MB)
    • 3. Your Three Best Resources.vtt (12.2 KB)
    • 4. Download the Resources here.html (3.0 KB)
    • 5. Meet your instructors!.html (0.7 KB)
    • READ_ME.txt (0.4 KB)
    2. Step 1 - Artificial Neural Network
    • 1. Welcome to Step 1 - Artificial Neural Network.html (0.6 KB)
    • 2. Plan of Attack.mp4 (11.9 MB)
    • 2. Plan of Attack.vtt (3.5 KB)
    • 3. The Neuron.mp4 (98.8 MB)
    • 3. The Neuron.vtt (22.0 KB)
    • 4. The Activation Function.mp4 (45.4 MB)
    • 4. The Activation Function.vtt (10.5 KB)
    • 5. How do Neural Networks work.mp4 (81.9 MB)
    • 5. How do Neural Networks work.vtt (17.3 KB)
    • 6. How do Neural Networks learn.mp4 (112.1 MB)
    • 6. How do Neural Networks learn.vtt (17.0 KB)
    • 7. Gradient Descent.mp4 (60.6 MB)
    • 7. Gradient Descent.vtt (12.7 KB)
    • 8. Stochastic Gradient Descent.mp4 (67.3 MB)
    • 8. Stochastic Gradient Descent.vtt (11.2 KB)
    • 9. Backpropagation.mp4 (43.1 MB)
    • 9. Backpropagation.vtt (6.7 KB)
    4. Step 3 - AutoEncoder
    • 1. Welcome to Step 3 - AutoEncoder.html (0.4 KB)
    • 2. Plan of Attack.mp4 (11.8 MB)
    • 2. Plan of Attack.vtt (2.8 KB)
    • 3. What are AutoEncoders.mp4 (94.6 MB)
    • 3. What are AutoEncoders.vtt (14.6 KB)
    • 4. A Note on Biases.mp4 (8.6 MB)
    • 4. A Note on Biases.vtt (1.9 KB)
    • 5. Training an AutoEncoder.mp4 (50.3 MB)
    • 5. Training an AutoEncoder.vtt (8.6 KB)
    • 6. Overcomplete Hidden Layers.mp4 (28.1 MB)
    • 6. Overcomplete Hidden Layers.vtt (5.0 KB)
    • 7. Sparse AutoEncoders.mp4 (57.5 MB)
    • 7. Sparse AutoEncoders.vtt (8.2 KB)
    • 8. Denoising AutoEncoders.mp4 (24.1 MB)
    • 8. Denoising AutoEncoders.vtt (3.2 KB)
    • 9. Contractive AutoEncoders.mp4 (20.6 MB)
    • 9. Contractive AutoEncoders.vtt (3.3 KB)
    • 10. Stacked AutoEncoders.mp4 (16.4 MB)
    • 10. Stacked AutoEncoders.vtt (14.4 MB)
    • 11. Deep AutoEncoders.mp4 (12.0 MB)
    • 11. Deep AutoEncoders.vtt (2.5 KB)
    5. Step 4 - Variational AutoEncoder
    • 1. Welcome to Step 4 - Variational AutoEncoder.html (0.4 KB)
    • 2. Introduction to the VAE.mp4 (103.7 MB)
    • 2. Introduction to the VAE.vtt (10.0 KB)
    • 3. Variational AutoEncoders.mp4 (26.3 MB)
    • 3. Variational AutoEncoders.vtt (5.7 KB)
    • 4. Reparameterization Trick.mp4 (26.4 MB)
    • 4. Reparameterization Trick.vtt (6.2 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 (58.9 MB)
    • 2. Introduction to Step 5.vtt (9.8 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.vtt (15.5 KB)
    • 4. Building the Encoder part of the VAE.mp4 (133.7 MB)
    • 4. Building the Encoder part of the VAE.vtt (23.6 KB)
    • 5. Building the V part of the VAE.mp4 (80.3 MB)
    • 5. Building the V part of the VAE.vtt (12.4 KB)
    • 6. Building the Decoder part of the VAE.mp4 (92.9 MB)
    • 6. Building the Decoder part of the VAE.vtt (11.9 KB)
    • 7. Implementing the Training operations.mp4 (187.0 MB)
    • 7. Implementing the Training operations.vtt (21.2 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.vtt (3.1 KB)
    • 3. What are Recurrent Neural Networks.mp4 (121.1 MB)
    • 3. What are Recurrent Neural Networks.vtt (21.4 KB)
    • 4. The Vanishing Gradient Problem.mp4 (111.2 MB)
    • 4. The Vanishing Gradient Problem.vtt (18.6 KB)
    • 5. LSTMs.mp4 (136.5 MB)
    • 5. LSTMs.vtt (25.1 KB)
    • 6. LSTM Practical Intuition.mp4 (187.4 MB)
    • 6. LSTM Practical Intuition.vtt (18.6 KB)
    • 7. LSTM Variations.mp4 (20.1 MB)
    • 7. LSTM Variations.vtt (4.4 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.vtt (83.4 MB)
    • 3

Description

Artificial Intelligence Masterclass



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



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Artificial Intelligence Masterclass

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Download torrent
6.2 GB
seeders:6
leechers:3
Artificial Intelligence Masterclass

Torrent hash: 53EF47A12B7CCA042612A1C29AF46FD5017EAE39