Artificial Intelligence Course

About Course

Artificial Intelligence Course

Artificial intelligence (AI) is truly a revolutionary feat of computer science, set to become an essential element of all modern software over the approaching years and decades. This offers a threat but also an opportunity. AI will be organized to augment both defensive and offensive cyber operations. Furthermore, new means of cyber-attack will be developed to take advantage of the particular weaknesses of AI technology. Finally, the significance of data will be improved by AI’s appetite for large amounts of training data, restating how we must think about data protection. Prudent governance at the global level will be important to ensure that this era-defining technology will bring about largely shared safety and prosperity.

Artificial Intelligence Course In common terms, AI denotes to computational tools that are able to substitute for human intelligence in the performance of certain tasks. This technology is presently advancing at a breakneck pace, much like the exponential growth experienced by database technology in the late twentieth century. Databases have grown to become the central infrastructure that drives enterprise-level software. Likewise, most of the new value added from software over the coming decades is expected to be driven, at least in part, by AI.

One of the important purposes of AI is to mechanize tasks that previously would have required human intelligence. to analyze diseases based on patients’ symptoms. 

In an easy model of how AI could be applied to cyber defense, log lines of recorded activity from servers and network components can be considered as “hostile” or “non-hostile,” and an AI system can be trained using this data set to categorize future observations into one of those two classes. The system can then act as an automated sentinel, singling out unusual observations from the huge background noise of normal activity.

Our training is divided into two formats => Basic and Advanced

Mode of Training => Online Live Classes

Certificate In Artificial Intelligence Course

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What Will You Learn?

  • How to construct an AI
  • How to construct a Hybrid Intelligent System
  • Fully-Linked Neural Networks
  • Convolutional and 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

About the instructor

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Course Curriculum

Artificial Neural Network

  • The Neuron
  • The Activation Function
  • How does it work?
  • How do Neural Networks learn?
  • Stochastic Gradient Descent
  • Backpropagation

Convolutional Neural Network

  • What are Convolutional Neural Networks?
  • The Convolution Operation
  • Bis – The ReLU Layer
  • Pooling
  • Flattening
  • Full Connection
  • Softmax & Cross-Entropy

AutoEncoder

  • What are AutoEncoders?
  • Training an AutoEncoder
  • Overcomplete Hidden Layers
  • Sparse AutoEncoders
  • Denoising AutoEncoders
  • Contractive AutoEncoders
  • Stacked AutoEncoders
  • Deep AutoEncoders

Variational AutoEncoder

  • Introduction
  • Variational AutoEncoders
  • Reparameterization Trick

Implementing the CNN-VAE

  • Initializing all the parameters and variables of the CNN-VAE class
  • Build the Encoder part of the VAE
  • Build the “V” part of the VAE
  • Build the Decoder part of the VAE
  • Implementing the Training operations
  • Full Code Section
  • The Keras Implementation

Recurrent Neural Network

  • What are Recurrent Neural Networks?
  • The Vanishing Gradient Problem
  • LSTMs
  • LSTM Variations

Mixture Density Network

  • Introduction to the MDN-RNN
  • Mixture Density Networks
  • VAE + MDN-RNN Visualization

Implementing the MDN-RNN

  • Initializing all the parameters and variables of the MDN-RNN class
  • Building the RNN
  • Building the MDN
  • Implementing the Training operations
  • The Keras Implementation

Reinforcement Learning

  • What is Reinforcement Learning?
  • A Pseudo Implementation of Reinforcement

Deep NeuroEvolution

  • Deep NeuroEvolution
  • Evolution Strategies
  • Genetic Algorithms
  • Covariance-Matrix Adaptation Evolution Strategy (CMA-ES)
  • Parameter-Exploring Policy Gradients (PEPG)
  • OpenAI Evolution Strategy

The Final Run

  • The Whole Implementation
  • Installing the required packages
  • Human Intelligence vs. Artificial Intelligence
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