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DEEP LEARNING

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DEEP LEARNING

Duration : 6 MONTHS
Eligibility : Degree/Diploma

OVERVIEW

Deep Learning has proved itself to be a possible solution to such Computer Vision tasks. Not only in Computer Vision, Deep Learning techniques are also widely applied in Natural Language Processing tasks. In this course we will start with traditional Machine Learning approaches, e.g. Bayesian Classification, Multilayer Perceptron etc. and then move to modern Deep Learning architectures like Convolutional Neural Networks, Autoencoders etc. On completion of the course students will acquire the knowledge of applying Deep Learning techniques to solve various real life problems.

Course Modules

  • Introduction to Deep Learning, Bayesian Learning, Decision Surfaces
  • Linear Classifiers, Linear Machines with Hinge Loss
  • Optimization Techniques, Gradient Descent, Batch Optimization
  • Introduction to Neural Network, Multilayer Perceptron, Back Propagation Learning
  • Unsupervised Learning with Deep Network, Auto encoders
  • Convolutional Neural Network, Building blocks of CNN, Transfer Learning
  • Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam
  • Effective training in Deep Net- early stopping, Dropout, Batch Normalization, Instance Normalization, Group Normalization
  • Recent Trends in Deep Learning Architectures, Residual Network, Skip Connection Network, Fully Connected CNN etc.
  • Classical Supervised Tasks with Deep Learning, Image Denoising, Semanticd Segmentation, Object Detection etc.
  • LSTM Networks
  • Generative Modelling with DL, Variational Autoencoder, Generative Adversarial Network Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam

Learning Outcome

The student should be able to

  • Explain the basic principles behind neural networks and deep learning
  • Compare modelling aspects of various neural network architectures
  • Implement simple neural network algorithms
  • Apply and evaluate deep learning on real data sets
  • Provide successful examples how deep learning can be used in different contexts in the society
  • Read and critically assess papers on artificial neural networks and their applications

Placement opportunities

Deep learning has extended an extended spectrum of employment opportunities across different industries, including healthcare, finance, transportation, retail, and more. Here are some of the employment possibilities in deep learning:

  •  Deep Learning Engineer: Deep Learning Engineers are accountable for designing, implementing, and testing deep learning models that can translate complicated concerns. Thus they operate with enormous datasets, produce and train neural networks, and deploy instances in exhibition settings.
  • Data Scientist: Data scientists perform with extensive datasets to recognize habits, make forecasts, and design standards that can translate intricate business issues. Deep learning is a helpful instrument in data science and is utilized to make more authentic models for anticipating consumer manners and improving business function.
  • Research Scientist: Research scientists operate on creating unique algorithms and strategies for deep learning. Thus they work in research labs, academia, or technology companies and are accountable for driving the limitations of what's feasible in deep learning.

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