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30-Day Money-Back Guarantee

This course includes:

  • 12 hours on-demand video
  • 20 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Artificial Intelligence

Artificial Intelligence Masterclass

Enter the new era of Hybrid AI Models optimized by Deep NeuroEvolution, with a complete toolkit of ML, DL & AI models
Rating: 4.4 out of 54.4 (1,002 ratings)
9,627 students
Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team
Last updated 1/2021
English
English, Italian [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

13 sections • 89 lectures • 12h 0m total length

  • Preview01:09
  • Preview16:44
  • BONUS: Learning Paths
    00:51
  • Your Three Best Resources
    10:42
  • Download the Resources here
    00:08
  • Meet your instructors!
    00:12

  • Welcome to Step 1 - Artificial Neural Network
    00:23
  • Preview02:51
  • The Neuron
    16:15
  • The Activation Function
    08:29
  • How do Neural Networks work?
    12:47
  • How do Neural Networks learn?
    12:58
  • Gradient Descent
    10:12
  • Stochastic Gradient Descent
    08:44
  • Backpropagation
    05:21

  • Welcome to Step 2 - Convolutional Neural Network
    00:17
  • Plan of Attack
    03:31
  • What are Convolutional Neural Networks?
    15:49
  • Step 1 - The Convolution Operation
    16:38
  • Step 1 Bis - The ReLU Layer
    06:41
  • Step 2 - Pooling
    14:13
  • Step 3 - Flattening
    01:52
  • Step 4 - Full Connection
    19:24
  • Summary
    04:19
  • Softmax & Cross-Entropy
    18:20

  • Welcome to Step 3 - AutoEncoder
    00:16
  • Plan of Attack
    02:12
  • What are AutoEncoders?
    10:50
  • A Note on Biases
    01:15
  • Training an AutoEncoder
    06:10
  • Overcomplete Hidden Layers
    03:52
  • Sparse AutoEncoders
    06:15
  • Denoising AutoEncoders
    02:32
  • Contractive AutoEncoders
    02:23
  • Stacked AutoEncoders
    01:54
  • Deep AutoEncoders
    01:50

  • Welcome to Step 4 - Variational AutoEncoder
    00:15
  • Introduction to the VAE
    08:15
  • Variational AutoEncoders
    04:29
  • Reparameterization Trick
    04:55

  • Welcome to Step 5 - Implementing the CNN-VAE
    00:27
  • Introduction to Step 5
    08:11
  • Initializing all the parameters and variables of the CNN-VAE class
    13:54
  • Building the Encoder part of the VAE
    19:34
  • Building the "V" part of the VAE
    10:40
  • Building the Decoder part of the VAE
    10:40
  • Implementing the Training operations
    18:34
  • Full Code Section
    01:10
  • The Keras Implementation
    02:43

  • Welcome to Step 6 - Recurrent Neural Network
    00:20
  • Plan of Attack
    02:32
  • What are Recurrent Neural Networks?
    16:01
  • The Vanishing Gradient Problem
    14:27
  • LSTMs
    19:47
  • LSTM Practical Intuition
    15:11
  • LSTM Variations
    03:36

  • Welcome to Step 7 - Mixture Density Network
    00:21
  • Preview09:28
  • Mixture Density Networks
    09:33
  • VAE + MDN-RNN Visualization
    05:45

  • Welcome to Step 8 - Implementing the MDN-RNN
    00:41
  • Initializing all the parameters and variables of the MDN-RNN class
    13:42
  • Building the RNN - Gathering the parameters
    09:54
  • Building the RNN - Creating an LSTM cell with Dropout
    16:15
  • Building the RNN - Setting up the Input, Target, and Output of the RNN
    14:54
  • Building the RNN - Getting the Deterministic Output of the RNN
    11:56
  • Building the MDN - Getting the Input, Hidden Layer and Output of the MDN
    13:22
  • Building the MDN - Getting the MDN parameters
    10:57
  • Implementing the Training operations (Part 1)
    15:31
  • Implementing the Training operations (Part 2)
    13:34
  • Full Code Section
    02:48
  • The Keras Implementation
    02:05

  • Welcome to Step 9 - Reinforcement Learning
    00:15
  • What is Reinforcement Learning?
    11:26
  • A Pseudo Implementation of Reinforcement Learning for the Full World Model
    20:00
  • Full Code Section
    00:09

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

Featured review

Rich Parker
Rich Parker
107 courses
35 reviews
Rating: 5.0 out of 5a year ago
The course started off with review of basic networks, and then combined them to create a road-game player. Learning here was almost a log curve. Great course!!! Only note would be you have to mess with the 2d game library to get it to work with newer Python versions. Definitely worth the time!

Instructors

Hadelin de Ponteves
AI Entrepreneur
Hadelin de Ponteves
  • 4.5 Instructor Rating
  • 264,133 Reviews
  • 1,285,843 Students
  • 84 Courses

Hadelin is the co-founder and CEO at BlueLife AI, which leverages the power of cutting edge Artificial Intelligence to empower businesses to make massive profits by innovating, automating processes and maximizing efficiency. Hadelin is also an online entrepreneur who has created 70+ top-rated educational e-courses to the world on topics such as Machine Learning, Deep Learning, Artificial Intelligence and Blockchain, which have reached 1M+ students in 210 countries.

Kirill Eremenko
Data Scientist
Kirill Eremenko
  • 4.5 Instructor Rating
  • 493,858 Reviews
  • 1,733,928 Students
  • 121 Courses

My name is Kirill Eremenko and I am super-psyched that you are reading this!

Professionally, I am a Data Science management consultant with over five years of experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and today I leverage Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.

From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. I am also passionate about public speaking, and regularly present on Big Data at leading Australian universities and industry events.

To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you!

SuperDataScience Team
Helping Data Scientists Succeed
SuperDataScience Team
  • 4.5 Instructor Rating
  • 455,821 Reviews
  • 1,644,584 Students
  • 107 Courses

Hi there,

We are the SuperDataScience Social team. You will be hearing from us when new SDS courses are released, when we publish new podcasts, blogs, share cheatsheets and more!

We are here to help you stay on the cutting edge of Data Science and Technology. 

See you in class,

Sincerely,

The Real People at SuperDataScience

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