Deep Learning with Computer Vision & NLP (NRIVA)

This live training in Deep Learning and NLP will cover the fundamentals and help progress in your career in one of the most trending fields. Instructors are industry experts and teach you live with interactive projects and courseware. retention is better when you learn in a group as a live class.

Live Class: Monday, 28 Nov

Enrolled: 40

Duration: 45 Hours

Offered by: infyni

$500 85% off

$75

Enrollment Closed Notify Me

About Course

This live online training in Deep Learning with Computer Vision & NLP is organized by NRIVA for members only. The training will cover the following : 

  • Introduction to AI and Deep Learning 
  • Deep Learning essentials 
  • Pytorch Basics 
  • Introduction to Images & OpenCV Basics 
  • Convolutional Neural Networks 
  • Recurrent Neural Network 
  • Transformers, BERT & GPT 
  • NLP using Spacy

The following prerequisites will be needed prior to our training : 

  • Python Programming 
  • Basic understanding of Machine Learning 
  • Basics of Statistics 


Course Offerings

  • Instructor-led interactive classes
  • Clarify your doubts during class
  • Access recordings of the class
  • Attend on mobile or tablet
  • Live projects to practice
  • Case studies to learn from
  • Lifetime mentorship support
  • Industry specific curriculum
  • Certificate of Completion
  • Employability Opportunity
  • Topics
  • Instructor
  • Python programming
  •  Good understanding of Machine Learning concepts
  •  Statistics basics
  •  Understanding of 12th Standard mathematics
  • Introduction to Deep Learning
  • Working of a Deep Network
  • History and evolution of various deep learning algorithms
  • Why Deep Learning
  • What is Perceptron
  • What is Neuron
  • Sigmoid neuron
  • Activation functions
  • Cost function
  • Optimization
  • Dense networks
  • Regularization
  • Layered structures and Types of layers
  • Forward pass
  • Back propagation - chain rule and evaluation metrics
  • Gradient Descent
  • SGD (for a SoftMax classifier example)
  • Nestorov's momentum
  • RMSProp
  • Adam
  • Computational Graph and Deep Learning framework
  • Pytorch installation
  • Torch Tensors & Numpy bridge
  • Automatic Differentiation
  • Loss Functions
  • Weigh Initialization
  • Regression with Deep learning
  • Classification with Deep Learning
  • Opening Image files using Python
  • Opening Image files with OpenCV
  • Drawing on Images - Basic Shapes
  • Drawing on Images - Text and Polygons
  • Direct Drawing on Images with a mouse
  • Introduction to CNN (Convolutional Neural Networks)
  • Applications of CNN
  • CNN Architecture
  • Convolution
  • Pooling layers
  • CNN illustrations
  • Image classification with CNN
  • Object detection and image segmentation
  • Fundamentals of RNN (Recurrent Neural Network)
  • Applications of RNN
  • Modelling sequencing
  • Types of RNNs - LSTM, GRU
  • Transformers Architecture
  • BERT – State of the art NLP technique
  • Getting started with SpaCy
  • Core operations with SpaCy
  • SpaCy features
  • Linguistic features
  • Rule based matching
  • Working with word vectors and semantic similarity

...
Shravan Kumar Veldurthi
About Instructor

I have more than 8 years experience in AIML domain. I worked on Machine Learning, Deep Learning, NLP, CV projects. I mentored more than 1500 students in AIML domain.


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