Deep Learning With Python
Vitti offers certified project based training program in Deep Learning

This program is focused on building industry- ready professionals who can work on Deep Learning Platforms leveraging machine learning, data mining, and statistical modeling for predictive and prescriptive enterprise analytics. This program will enable you to develop understanding and practical experience with neural network models, deep learning & data analytics using Tensor-flow and Python.
Specially developed in collaboration with academia & AI Industry experts to reskill and retool working professionals towards Artificial Intelligence space, this program offers the following benefits: Experienced Faculty-Led Sessions: Live-Interactive Online classes. Program content & structure designed in collaboration with faculty and Industry experts.

Duration: 40 Hours Session: 2 hours/day - 2 days a week (weekend) Batch Size: 50 Students

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Vitti offers certified project based training program in advanced Python API for Deep Learning with deep-dive in Neural Networks Fast-forward your Career with market-relevant “Vitti Certified AI program” from the best in the Industry

Syllabus Structure

What you will learn from this course


Module 1 : Introduction to deep learning
  • Fundamental concepts
  • Feature learning
  • Deep Learning algorithms
  • Object Recognition and Classification
  • Open source package for deep learning
  • TensorFlow, Keras, H2O
Module 2 : Unsupervised Feature Learning
  • Autoencoders
  • Network Design
  • Restricted Boltzmann machines
  • Hopfield network and Boltzmann machines
  • Implementation in TensorFlow
  • Deep belief networks
Module 3 : Convolutional Neural Network
  • Architecture of CNN
  • Types of layers in CNN
  • Building an image classifier using CNN
  • Deep Learning with CNN

Module 4 : Recurrent Neural Networks
  • Recurrent Neural Networks
  • Language modeling
  • Word-based models
  • N-grams
  • Character-based model
  • LSTM network
  • Speech Recognition
  • Speech as input data
  • Acoustic model
  • Image Recognition
Module 5 : Deep Learning for Board Games
  • Early game playing AI
  • Using min-max algorithm to value game states
  • Implementing tic-tac-toe games
  • Deep learning in Monte Carlo Tree Search
Module 6 : Deep Learning for Computer Games
  • A supervised learning approach to computer games
  • Applying genetic algorithm to playing games
  • Q-learning
  • Dynamic Games
Module 7 : Anomaly Detection
  • What is anomaly and outlier detection
  • Real-world application of anomaly detection
  • Anomaly detection using deep encoders
  • MNIST digit anomaly recognition using H2O library
  • Electrocardiogram pulse detection

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