| 1. The Whole Implementation.mp4 | 191.6 MB | ||
| 1. The Whole Implementation.vtt | 25.7 KB | ||
| 1. Updates on Udemy Reviews.mp4 | 46 MB | ||
| 1. Updates on Udemy Reviews.vtt | 3.1 KB | ||
| 1. Welcome to Step 1 - Artificial Neural Network.html | 614.4 B | ||
| 1. Welcome to Step 10 - Deep NeuroEvolution.html | 1.2 KB | ||
| 1. Welcome to Step 2 - Convolutional Neural Network.html | 409.6 B | ||
| 1. Welcome to Step 3 - AutoEncoder.html | 409.6 B | ||
| 1. Welcome to Step 4 - Variational AutoEncoder.html | 409.6 B | ||
| 1. Welcome to Step 5 - Implementing the CNN-VAE.html | 2.3 KB | ||
| 1. Welcome to Step 6 - Recurrent Neural Network.html | 512 B | ||
| 1. Welcome to Step 7 - Mixture Density Network.html | 512 B | ||
| 1. Welcome to Step 8 - Implementing the MDN-RNN.html | 2.8 KB | ||
| 1. Welcome to Step 9 - Reinforcement Learning.html | 409.6 B | ||
| 10. Implementing the Training operations (Part 2).mp4 | 162.9 MB | ||
| 10. Implementing the Training operations (Part 2).vtt | 17.2 KB | ||
| 10. Softmax & Cross-Entropy.mp4 | 118 MB | ||
| 10. Softmax & Cross-Entropy.vtt | 23 KB | ||
| 10. Stacked AutoEncoders.mp4 | 16.4 MB | ||
| 10. Stacked AutoEncoders.vtt | 14.4 MB | ||
| 11. Deep AutoEncoders.mp4 | 12 MB | ||
| 11. Deep AutoEncoders.vtt | 2.5 KB | ||
| 11. Full Code Section.html | 10.8 KB | ||
| 12. The Keras Implementation.html | 5.3 KB | ||
| 2. Deep NeuroEvolution.mp4 | 108.8 MB | ||
| 2. Deep NeuroEvolution.vtt | 13.8 KB | ||
| 2. Download the whole AI Masterclass folder here.html | 1 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.vtt | 16.4 KB | ||
| 2. Introduction + Course Structure + Demo.mp4 | 156.8 MB | ||
| 2. Introduction + Course Structure + Demo.vtt | 19.9 KB | ||
| 2. Introduction to Step 5.mp4 | 58.9 MB | ||
| 2. Introduction to Step 5.vtt | 9.8 KB | ||
| 2. Introduction to the MDN-RNN.mp4 | 83.4 MB | ||
| 2. Introduction to the MDN-RNN.vtt | 83.4 MB | ||
| 2. Introduction to the VAE.mp4 | 103.7 MB | ||
| 2. Introduction to the VAE.vtt | 10 KB | ||
| 2. Plan of Attack.mp4 | 10.5 MB | ||
| 2. Plan of Attack.vtt | 3.1 KB | ||
| 2. What is Reinforcement Learning.mp4 | 68.6 MB | ||
| 2. What is Reinforcement Learning.vtt | 16.1 KB | ||
| 2.1 AI_Masterclass.zip.zip | 17.1 MB | ||
| 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.vtt | 24.5 KB | ||
| 3. Building the RNN - Gathering the parameters.mp4 | 76.6 MB | ||
| 3. Building the RNN - Gathering the parameters.vtt | 55.3 MB | ||
| 3. Evolution Strategies.mp4 | 119.4 MB | ||
| 3. Evolution Strategies.vtt | 11.9 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 | ||
| 3. Installing the required packages.mp4 | 158.7 MB | ||
| 3. Installing the required packages.vtt | 15.1 KB | ||
| 3. Mixture Density Networks.mp4 | 65.4 MB | ||
| 3. Mixture Density Networks.vtt | 12.4 KB | ||
| 3. The Neuron.mp4 | 98.8 MB | ||
| 3. The Neuron.vtt | 22 KB | ||
| 3. Variational AutoEncoders.mp4 | 26.3 MB | ||
| 3. Variational AutoEncoders.vtt | 5.7 KB | ||
| 3. What are AutoEncoders.mp4 | 94.6 MB | ||
| 3. What are AutoEncoders.vtt | 14.6 KB | ||
| 3. What are Convolutional Neural Networks.mp4 | 108 MB | ||
| 3. What are Convolutional Neural Networks.vtt | 19.9 KB | ||
| 3. What are Recurrent Neural Networks.mp4 | 121.1 MB | ||
| 3. What are Recurrent Neural Networks.vtt | 21.4 KB | ||
| 3. Your Three Best Resources.mp4 | 143.3 MB | ||
| 3. Your Three Best Resources.vtt | 12.2 KB | ||
| 4. A Note on Biases.mp4 | 8.6 MB | ||
| 4. A Note on Biases.vtt | 1.9 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 | ||
| 4. Building the RNN - Creating an LSTM cell with Dropout.mp4 | 127.2 MB | ||
| 4. Building the RNN - Creating an LSTM cell with Dropout.vtt | 19.8 KB | ||
| 4. Download the Resources here.html | 3 KB | ||
| 4. Full Code Section.html | 409.6 B | ||
| 4. Genetic Algorithms.mp4 | 149.1 MB | ||
| 4. Genetic Algorithms.vtt | 15.4 KB | ||
| 4. Reparameterization Trick.mp4 | 26.4 MB | ||
| 4. Reparameterization Trick.vtt | 6.2 KB | ||
| 4. Step 1 - The Convolution Operation.mp4 | 97.9 MB | ||
| 4. Step 1 - The Convolution Operation.vtt | 21.5 KB | ||
| 4. The Activation Function.mp4 | 45.4 MB | ||
| 4. The Activation Function.vtt | 10.5 KB | ||
| 4. The Final Race Human Intelligence vs. Artificial Intelligence.mp4 | 125.1 MB | ||
| 4. The Final Race Human Intelligence vs. Artificial Intelligence.vtt | 14 KB | ||
| 4. The Vanishing Gradient Problem.mp4 | 111.2 MB | ||
| 4. The Vanishing Gradient Problem.vtt | 18.6 KB | ||
| 4. VAE + MDN-RNN Visualization.mp4 | 45.3 MB | ||
| 4. VAE + MDN-RNN Visualization.vtt | 6.9 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.vtt | 17.9 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 | ||
| 5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).mp4 | 144.1 MB | ||
| 5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).vtt | 15.8 KB | ||
| 5. How do Neural Networks work.mp4 | 81.9 MB | ||
| 5. How do Neural Networks work.vtt | 17.3 KB | ||
| 5. LSTMs.mp4 | 136.5 MB | ||
| 5. LSTMs.vtt | 25.1 KB | ||
| 5. Meet your instructors!.html | 716.8 B | ||
| 5. Step 1 Bis - The ReLU Layer.mp4 | 53.4 MB | ||
| 5. Step 1 Bis - The ReLU Layer.vtt | 8.5 KB | ||
| 5. Training an AutoEncoder.mp4 | 50.3 MB | ||
| 5. Training an AutoEncoder.vtt | 8.6 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 | ||
| 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.vtt | 15 KB | ||
| 6. How do Neural Networks learn.mp4 | 112.1 MB | ||
| 6. How do Neural Networks learn.vtt | 17 KB | ||
| 6. LSTM Practical Intuition.mp4 | 187.4 MB | ||
| 6. LSTM Practical Intuition.vtt | 18.6 KB | ||
| 6. Overcomplete Hidden Layers.mp4 | 28.1 MB | ||
| 6. Overcomplete Hidden Layers.vtt | 5 KB | ||
| 6. Parameter-Exploring Policy Gradients (PEPG).mp4 | 143.9 MB | ||
| 6. Parameter-Exploring Policy Gradients (PEPG).vtt | 15.1 KB | ||
| 6. Step 2 - Pooling.mp4 | 140.2 MB | ||
| 6. Step 2 - Pooling.vtt | 19.1 KB | ||
| 7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.mp4 | 147 MB | ||
| 7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.vtt | 15 KB | ||
| 7. Gradient Descent.mp4 | 60.6 MB | ||
| 7. Gradient Descent.vtt | 12.7 KB | ||
| 7. Implementing the Training operations.mp4 | 187 MB | ||
| 7. Implementing the Training operations.vtt | 21.2 KB | ||
| 7. LSTM Variations.mp4 | 20.1 MB | ||
| 7. LSTM Variations.vtt | 4.4 KB | ||
| 7. OpenAI Evolution Strategy.mp4 | 108.1 MB | ||
| 7. OpenAI Evolution Strategy.vtt | 9.7 KB | ||
| 7. Sparse AutoEncoders.mp4 | 57.5 MB | ||
| 7. Sparse AutoEncoders.vtt | 8.2 KB | ||
| 7. Step 3 - Flattening.mp4 | 7.9 MB | ||
| 7. Step 3 - Flattening.vtt | 2.3 KB | ||
| 8. Building the MDN - Getting the MDN parameters.mp4 | 109.5 MB | ||
| 8. Building the MDN - Getting the MDN parameters.vtt | 13.2 KB | ||
| 8. Denoising AutoEncoders.mp4 | 24.1 MB | ||
| 8. Denoising AutoEncoders.vtt | 3.2 KB | ||
| 8. Full Code Section.html | 4 KB | ||
| 8. Step 4 - Full Connection.mp4 | 194.3 MB | ||
| 8. Step 4 - Full Connection.vtt | 26.1 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 | ||
| 9. Contractive AutoEncoders.mp4 | 20.6 MB | ||
| 9. Contractive AutoEncoders.vtt | 3.3 KB | ||
| 9. Implementing the Training operations (Part 1).mp4 | 177.5 MB | ||
| 9. Implementing the Training operations (Part 1).vtt | 18.3 KB | ||
| 9. Summary.mp4 | 30.3 MB | ||
| 9. Summary.vtt | 5.7 KB | ||
| 9. The Keras Implementation.html | 7.7 KB | ||
| READ_ME.txt | 409.6 B | ||
| ▲ 158 total files | |||
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
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 1.8 GB | freecoursewb | 4 weeks | 11 | 5 | |
| 1.8 GB | freecoursewb | 2 months | 13 | 11 | |
| 3.9 GB | freecoursewb | 2 months | 15 | 4 | |
| 2 GB | freecoursewb | 2 months | 27 | 34 | |
| 1.9 GB | freecoursewb | 3 months | 5 | 3 |
All Comments