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Artificial Intelligence (AI)

Artificial intelligence (AI) is a research field that studies how to comprehend the intelligent human behaviours on a computer. The final goal of Artificial intelligence is to make a computer that can learn, plan, and solve problems unconventionally. We still cannot make a computer that is as intelligent as a human in all aspects even though the studies have gone beyond half century on AI. However, we have many successful applications. In a few cases, the computer’s that are equipped with AI technology can be even more intelligent than us. 


A few research topics in AI are problem solving, reasoning, planning, natural language understanding, computer vision, automatic programming, machine learning, and so on. However, these topics are closely related with each other. For example, the knowledge acquired through learning can be used both for reasoning and also for problem solving. The methods for problem solving are useful for planning and reasoning.

5 Days

  • Strong grip on Mathematics
  • Strong knowledge of programming languages
  • Writing algorithm for finding patterns and learning
  • Strong data analytics skills
  • Good knowledge of Discrete mathematics
  • Strong determination to learn machine learning languages
  • Deep Learning: A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • What is Deep Learning?
  • Advantage of Deep Learning over Machine learning
  • 3 Reasons to go for Deep Learning
  • Real-Life use cases of Deep Learning
  • Review of Machine Learning
  • Core language fundamentals
  • Functions
  • Arrays
  • Slices
  • Maps
  • Defer, Panic and Recover
  • Error handling
  • Activation Functions
  • Illustrate Perceptron
  • Training a Perceptron
  • Important Parameters of Perceptron
  • What is Tensor Flow?
  • Tensor Flow code-basics
  • Graph Visualization
  • Constants, Placeholders, Variables
  • Creating a Model
  • Step by Step – Use-Case Implementation
  • Understand limitations of A Single Perceptron
  • Understand Neural Networks in Detail
  • Illustrate Multi-Layer Perceptron
  • Backpropagation – Learning Algorithm
  • Understand Backpropagation – Using Neural Network Example
  • MLP Digit-Classifier using TensorFlow
  • Tensor Board
  • Why Deep Learning?
  • SONAR Dataset Classification
  • What is Deep Learning?
  • Feature Extraction
  • Working of Deep Network
  • Training using Backpropagation
  • Variants of Gradient Descent
  • Types of Deep Networks
  • Introduction to Convolutional Neural Networks
  • CNN Applications
  • Architecture of a Convolutional Neural Network
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN
  • Transfer Learning and Fine-tuning Convolutional Neural Networks
  • Intro to RNN Model
  • Application use cases of RNN
  • Modelling sequences
  • Training RNNs with Backpropagation
  • Long Short-Term Memory (LSTM)
  • Recursive Neural Tensor Network Theory
  • Recurrent Neural Network Model
  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Define Keras
  • How to compose Models in Keras
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with TFlearn
  • Customizing the Training Process
  • Using Tensor Board with Keras
  • Use-Case Implementation with Keras
  • Define TFlearn
  • Composing Models in TFlearn
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with TFlearn
  • Customizing the Training Process
  • Using Tensor Board with TFlearn
  • Use-Case with TFlearn
  • AWS ML Offerings 
  • Google mL Offerings
  • On- Premised Deployments
  • Grid search
  • Random search
  • Bayesian optimization
  • Kubeflow
  • AWS sage maker
  • Data connectors to consume big data