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AI & Deep Learning Applications

30 hours
All levels
11 lessons
0 quiz
50 students

About FDP on Artificial Intelligence & Deep Learning

The one-week FDP on Artificial Intelligence & Deep learning aims to introduce hands-on training deep learning. As well as one-week FDP on Artificial Intelligence introduces AI concepts like convolutional neural networks (CNN), perceptron in CNN, TensorFlow, TensorFlow code, transfer learning, graph visualization, recurrent neural networks (RNN), Deep Learning libraries, GPU in Deep Learning, Keras and TFLearn APIs, backpropagation, and hyperparameters via hands-on projects.

One week AI Course Outlines

Introduction  Artificial Intelligence

  • AI Applications, Types of Learning, Supervised,
  • Unsupervised and Reinforcement learning.
  • Artificial Neural Networks (ANNs) Concept,
  • Feed Forward Neural Networks and Back Propagation

Getting Started with python

  • Environment Setup
  • Setting up Anaconda, Spyder/VS Code
  • Installing Libraries
  • Working with Python Framework for Data Science
  • Python for Data Analysis-NumPy

Artificial Neural Networks

  • The Neuron Diagram Neuron Models & Neural Network
  • The functioning of Neurons Activation functions Gradient Descent,
  • Stochastic Descent, ramp function, sigmoid function, Gaussian function
  • single-layer feed-forward, Multi-layer feed-forward,

CNN’s (Convolutional Neural Networks)

  • What is a convolutional neural network?
  • understanding the architecture and use-cases of CNN,
  • how to visualize using CNN,
  • how to fine-tune a convolutional neural network,
  • understanding recurrent neural networks
  • deploying convolutional neural networks in TensorFlow.

RNNs (Recurrent Neural Networks)

  • Introduction to the RNN model
  • Use cases of RNN,
  • training RNNs with back propagation,
  • long short-term memory (LSTM),
  • Recursive Neural Tensor Network theory,
  • the basic RNN cell, unfolded RNN, RNN training, dynamic RNN, and time-series predictions.

Artificial Intelligence Projects

  • For recognizing handwritten digits (MNIST dataset)
  • Image Recognition with TensorFlow
  • Ecommerce Product Recommendation
  • Modern Face Recognition with Deep Learning
  • Case Study: Cancer Detection, Character Recognition, Iris Clustering
  • Case Study: Intelligent Washing Machine Design
  • Develop a predictive analytics model for a complex dataset

Requirements for FDP

  • FDP training center can only be arranged for a minimum of 50 Attendees.
  • Seminar hall/Computer lab having enough capacity to conduct hands-on-session for all participants.
  • 5-Days Accommodation for a  training expert.

Participants Eligibility

The program is open to the Faculty/ Research Scholars/ Students of science & Engineering institutes and other working professionals are also, eligible.

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