Artificial Intelligence Masterclass

Artificial Intelligence Masterclass

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 89 lectures (12h 0m) | 3.19 GB

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

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
Table of Contents

1 Updates on Udemy Reviews
2 Introduction + Course Structure + Demo
3 BONUS Learning Paths
4 Your Three Best Resources
5 Download the Resources here
6 Meet your instructors!

Step 1 – Artificial Neural Network
7 Welcome to Step 1 – Artificial Neural Network
8 Plan of Attack
9 The Neuron
10 The Activation Function
11 How do Neural Networks work
12 How do Neural Networks learn
13 Gradient Descent
14 Stochastic Gradient Descent
15 Backpropagation

Step 2 – Convolutional Neural Network
16 Welcome to Step 2 – Convolutional Neural Network
17 Plan of Attack
18 What are Convolutional Neural Networks
19 Step 1 – The Convolution Operation
20 Step 1 Bis – The ReLU Layer
21 Step 2 – Pooling
22 Step 3 – Flattening
23 Step 4 – Full Connection
24 Summary
25 Softmax & Cross-Entropy

Step 3 – AutoEncoder
26 Welcome to Step 3 – AutoEncoder
27 Plan of Attack
28 What are AutoEncoders
29 A Note on Biases
30 Training an AutoEncoder
31 Overcomplete Hidden Layers
32 Sparse AutoEncoders
33 Denoising AutoEncoders
34 Contractive AutoEncoders
35 Stacked AutoEncoders
36 Deep AutoEncoders

Step 4 – Variational AutoEncoder
37 Welcome to Step 4 – Variational AutoEncoder
38 Introduction to the VAE
39 Variational AutoEncoders
40 Reparameterization Trick

Step 5 – Implementing the CNN-VAE
41 Welcome to Step 5 – Implementing the CNN-VAE
42 Introduction to Step 5
43 Initializing all the parameters and variables of the CNN-VAE class
44 Building the Encoder part of the VAE
45 Building the V part of the VAE
46 Building the Decoder part of the VAE
47 Implementing the Training operations
48 Full Code Section
49 The Keras Implementation

Step 6 – Recurrent Neural Network
50 Welcome to Step 6 – Recurrent Neural Network
51 Plan of Attack
52 What are Recurrent Neural Networks
53 The Vanishing Gradient Problem
54 LSTMs
55 LSTM Practical Intuition
56 LSTM Variations

Step 7 – Mixture Density Network
57 Welcome to Step 7 – Mixture Density Network
58 Introduction to the MDN-RNN
59 Mixture Density Networks
60 VAE + MDN-RNN Visualization

Step 8 – Implementing the MDN-RNN
61 Welcome to Step 8 – Implementing the MDN-RNN
62 Initializing all the parameters and variables of the MDN-RNN class
63 Building the RNN – Gathering the parameters
64 Building the RNN – Creating an LSTM cell with Dropout
65 Building the RNN – Setting up the Input, Target, and Output of the RNN
66 Building the RNN – Getting the Deterministic Output of the RNN
67 Building the MDN – Getting the Input, Hidden Layer and Output of the MDN
68 Building the MDN – Getting the MDN parameters
69 Implementing the Training operations (Part 1)
70 Implementing the Training operations (Part 2)
71 Full Code Section
72 The Keras Implementation

Step 9 – Reinforcement Learning
73 Welcome to Step 9 – Reinforcement Learning
74 What is Reinforcement Learning
75 A Pseudo Implementation of Reinforcement Learning for the Full World Model
76 Full Code Section

Step 10 – Deep NeuroEvolution
77 Welcome to Step 10 – Deep NeuroEvolution
78 Deep NeuroEvolution
79 Evolution Strategies
80 Genetic Algorithms
81 Covariance-Matrix Adaptation Evolution Strategy (CMA-ES)
82 Parameter-Exploring Policy Gradients (PEPG)
83 OpenAI Evolution Strategy

The Final Run
84 The Whole Implementation
85 Download the whole AI Masterclass folder here
86 Installing the required packages
87 The Final Race Human Intelligence vs. Artificial Intelligence
88 THANK YOU bonus video

Bonus Lectures