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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.
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 Curriculum

1.1 Introduction + Course Structure + Demo
1.1 Introduction + Course Structure + Demo 00:00:00
1.2 Your Three Best Resources
1.2 Your Three Best Resources 00:00:00
1.3 Download the Resources here
1.3 Download the Resources here 00:00:00
2.1 Welcome to Step 1 - Artificial Neural Network
2.1 Welcome to Step 1 – Artificial Neural Network 00:00:00
2.2 Plan of Attack
2.2 Plan of Attack 00:00:00
2.3 The Neuron
2.3 The Neuron 00:00:00
2.4 The Activation Function
2.4 The Activation Function 00:00:00
2.5 How do Neural Networks work
2.5 How do Neural Networks work 00:00:00
2.6 How do Neural Networks learn
2.6 How do Neural Networks learn 00:00:00
2.7 Gradient Descent
2.7 Gradient Descent 00:00:00
2.8 Stochastic Gradient Descent
2.8 Stochastic Gradient Descent 00:00:00
2.9 Backpropagation
2.9 Backpropagation 00:00:00
3.1 Welcome to Step 2 - Convolutional Neural Network
3.1 Welcome to Step 2 – Convolutional Neural Network 00:00:00
3.2 Backpropagation
3.2 Backpropagation 00:00:00
3.3 What are convolutional neural networks
3.3 What are convolutional neural networks 00:00:00
3.4 Step 1 - Convolution Operation
3.4 Step 1 – Convolution Operation 00:00:00
3.5 Step 1(b) - ReLU Layer
3.5 Step 1(b) – ReLU Layer 00:00:00
3.6 Step 2 - Pooling
3.6 Step 2 – Pooling 00:00:00
3.7 Step 3 - Flattening
3.7 Step 3 – Flattening 00:00:00
3.8 Step 4 - Full Connection
3.8 Step 4 – Full Connection 00:00:00
3.9 Step 4 - Full Connection
3.9 Step 4 – Full Connection 00:00:00
3.10 Softmax & Cross-Entropy
3.10 Softmax & Cross-Entropy 00:00:00
4.1 Welcome to Step 3 - AutoEncoder
4.1 Welcome to Step 3 – AutoEncoder 00:00:00
4.2 Plan of attack
4.2 Plan of attack 00:00:00
4.3 What are AutoEncoders
4.3 What are AutoEncoders 00:00:00
4.4 A Note on Biases
4.4 A Note on Biases 00:00:00
4.5 Training an Auto Encoder
4.5 Training an Auto Encoder 00:00:00
4.6 Overcomplete hidden layers
4.6 Overcomplete hidden layers 00:00:00
4.7 Sparse Autoencoders
4.7 Sparse Autoencoders 00:00:00
4.8 Denoising Autoencoders
4.8 Denoising Autoencoders 00:00:00
4.9 Denoising Autoencoders
4.9 Denoising Autoencoders 00:00:00
4.10 Stacked Autoencoders
4.10 Stacked Autoencoders 00:00:00
4.11 Deep Autoencoders
4.11 Deep Autoencoders 00:00:00
5.1 Welcome to Step 4 - Variational AutoEncoder
5.1 Welcome to Step 4 – Variational AutoEncoder 00:00:00
5.2 Introduction to the VAE
5.2 Introduction to the VAE 00:00:00
5.3 Variational AutoEncoders
5.3 Variational AutoEncoders 00:00:00
5.4 Reparameterization Trick
5.4 Reparameterization Trick 00:00:00
6.1 Welcome to Step 5 - Implementing the CNN-VAE
6.1 Welcome to Step 5 – Implementing the CNN-VAE 00:00:00
6.2 Introduction to Step 5
6.2 Introduction to Step 5 00:00:00
6.3 Initializing all the parameters and variables of the CNN-VAE class
6.3 Initializing all the parameters and variables of the CNN-VAE class 00:00:00
6.4 Building the Encoder part of the VAE
6.4 Building the Encoder part of the VAE 00:00:00
6.5 Building the V part of the VAE
6.5 Building the V part of the VAE 00:00:00
6.6 Building the Decoder part of the VAE
6.6 Building the Decoder part of the VAE 00:00:00
6.7 Implementing the Training operations
6.7 Implementing the Training operations 00:00:00
6.8 Full Code Section
6.8 Full Code Section 00:00:00
6.9 The Keras Implementation
6.9 The Keras Implementation 00:00:00
7.1 Welcome to Step 6 - Recurrent Neural Network
7.1 Welcome to Step 6 – Recurrent Neural Network 00:00:00
7.2 Plan of Attack
7.2 Plan of Attack 00:00:00
7.3 What are Recurrent Neural Networks
7.3 What are Recurrent Neural Networks 00:00:00
7.4 The Vanishing Gradient Problem
7.4 The Vanishing Gradient Problem 00:00:00
7.5 LSTMs
7.5 LSTMs 00:00:00
7.6 LSTM Practical Intuition
7.6 LSTM Practical Intuition 00:00:00
7.7 LSTM Variations
7.7 LSTM Variations 00:00:00
8.1 Welcome to Step 7 - Mixture Density Network
8.1 Welcome to Step 7 – Mixture Density Network 00:00:00
8.2 Introduction to the MDN-RNN
8.2 Introduction to the MDN-RNN 00:00:00
8.3 Mixture Density Networks
8.3 Mixture Density Networks 00:00:00
8.4 VAE + MDN-RNN Visualization
8.4 VAE + MDN-RNN Visualization 00:00:00
9.1 Welcome to Step 8 - Implementing the MDN-RNN
9.1 Welcome to Step 8 – Implementing the MDN-RNN 00:00:00
9.2 Initializing all the parameters and variables of the MDN-RNN class
9.2 Initializing all the parameters and variables of the MDN-RNN class 00:00:00
9.3 Building the RNN - Gathering the parameters
9.3 Building the RNN – Gathering the parameters 00:00:00
9.4 Building the RNN - Creating an LSTM cell with Dropout
9.4 Building the RNN – Creating an LSTM cell with Dropout 00:00:00
9.5 Building the RNN - Setting up the Input, Target, and Output of the RNN
9.5 Building the RNN – Setting up the Input, Target, and Output of the RNN 00:00:00
9.6 Building the RNN - Getting the Deterministic Output of the RNN
9.6 Building the RNN – Getting the Deterministic Output of the RNN 00:00:00
9.7 Building the MDN - Getting the Input, Hidden Layer and Output of the MDN
9.7 Building the MDN – Getting the Input, Hidden Layer and Output of the MDN 00:00:00
9.8 Building the MDN - Getting the MDN parameters
9.8 Building the MDN – Getting the MDN parameters 00:00:00
9.9 Implementing the Training operations (Part 1)
9.9 Implementing the Training operations (Part 1) 00:00:00
9.10 Implementing the Training operations (Part 2)
9.10 Implementing the Training operations (Part 2) 00:00:00
9.11 Full Code Section
9.11 Full Code Section 00:00:00
9.12 The Keras Implementation
9.12 The Keras Implementation 00:00:00
10.1 Welcome to Step 9 - Reinforcement Learning
10.1 Welcome to Step 9 – Reinforcement Learning 00:00:00
10.2 What is Reinforcement Learning
10.2 What is Reinforcement Learning 00:00:00
10.3 A Pseudo Implementation of Reinforcement Learning for the Full World Model
10.3 A Pseudo Implementation of Reinforcement Learning for the Full World Model 00:00:00
10.4 Full Code Section
10.4 Full Code Section 00:00:00
11.1 Welcome to Step 10 - Deep NeuroEvolution
11.1 Welcome to Step 10 – Deep NeuroEvolution 00:00:00
11.2 Deep NeuroEvolution
11.2 Deep NeuroEvolution 00:00:00
11.3 Evolution Strategies
11.3 Evolution Strategies 00:00:00
11.4 Genetic Algorithms
11.4 Genetic Algorithms 00:00:00
11.5 Covariance-Matrix Adaptation Evolution Strategy (CMA-ES)
11.5 Covariance-Matrix Adaptation Evolution Strategy (CMA-ES) 00:00:00
11.6 Parameter-Exploring Policy Gradients (PEPG)
11.6 Parameter-Exploring Policy Gradients (PEPG) 00:00:00
11.7 OpenAI Evolution Strategy
11.7 OpenAI Evolution Strategy 00:00:00
12.1 The Whole Implementation
12.1 The Whole Implementation 00:00:00
12.2 Download the whole AI Masterclass folder here
12.2 Download the whole AI Masterclass folder here 00:00:00
12.3 Installing the required packages
12.3 Installing the required packages 00:00:00
12.4 The Final Race Human Intelligence vs. Artificial Intelligence
12.4 The Final Race Human Intelligence vs. Artificial Intelligence 00:00:00

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