Artificial Intelligence Masterclass
- Descripción
- Currículum
- Reseñas
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.
-
6Welcome to Step 1 - Artificial Neural NetworkText lesson
-
7Plan of AttackVideo lesson
-
8The NeuronVideo lesson
-
9The Activation FunctionVideo lesson
-
10How do Neural Networks work?Video lesson
-
11How do Neural Networks learn?Video lesson
-
12Gradient DescentVideo lesson
-
13Stochastic Gradient DescentVideo lesson
-
14BackpropagationVideo lesson
-
15Welcome to Step 2 - Convolutional Neural NetworkText lesson
-
16Plan of AttackVideo lesson
-
17What are Convolutional Neural Networks?Video lesson
-
18Step 1 - The Convolution OperationVideo lesson
-
19Step 1 Bis - The ReLU LayerVideo lesson
-
20Step 2 - PoolingVideo lesson
-
21Step 3 - FlatteningVideo lesson
-
22Step 4 - Full ConnectionVideo lesson
-
23SummaryVideo lesson
-
24Softmax & Cross-EntropyVideo lesson
-
25Welcome to Step 3 - AutoEncoderText lesson
-
26Plan of AttackVideo lesson
-
27What are AutoEncoders?Video lesson
-
28A Note on BiasesVideo lesson
-
29Training an AutoEncoderVideo lesson
-
30Overcomplete Hidden LayersVideo lesson
-
31Sparse AutoEncodersVideo lesson
-
32Denoising AutoEncodersVideo lesson
-
33Contractive AutoEncodersVideo lesson
-
34Stacked AutoEncodersVideo lesson
-
35Deep AutoEncodersVideo lesson
-
40Welcome to Step 5 - Implementing the CNN-VAEText lesson
-
41Introduction to Step 5Video lesson
-
42Initializing all the parameters and variables of the CNN-VAE classVideo lesson
-
43Building the Encoder part of the VAEVideo lesson
-
44Building the "V" part of the VAEVideo lesson
-
45Building the Decoder part of the VAEVideo lesson
-
46Implementing the Training operationsVideo lesson
-
47Full Code SectionText lesson
-
48The Keras ImplementationText lesson
-
60Welcome to Step 8 - Implementing the MDN-RNNText lesson
-
61Initializing all the parameters and variables of the MDN-RNN classVideo lesson
-
62Building the RNN - Gathering the parametersVideo lesson
-
63Building the RNN - Creating an LSTM cell with DropoutVideo lesson
-
64Building the RNN - Setting up the Input, Target, and Output of the RNNVideo lesson
-
65Building the RNN - Getting the Deterministic Output of the RNNVideo lesson
-
66Building the MDN - Getting the Input, Hidden Layer and Output of the MDNVideo lesson
-
67Building the MDN - Getting the MDN parametersVideo lesson
-
68Implementing the Training operations (Part 1)Video lesson
-
69Implementing the Training operations (Part 2)Video lesson
-
70Full Code SectionText lesson
-
71The Keras ImplementationText lesson
-
76Welcome to Step 10 - Deep NeuroEvolutionText lesson
-
77Deep NeuroEvolutionVideo lesson
-
78Evolution StrategiesVideo lesson
-
79Genetic AlgorithmsVideo lesson
-
80Covariance-Matrix Adaptation Evolution Strategy (CMA-ES)Video lesson
-
81Parameter-Exploring Policy Gradients (PEPG)Video lesson
-
82OpenAI Evolution StrategyVideo lesson