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Category: Education Series
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Contact Email: training@nriva.org
Event For: Member
Speaker Name:
Deep Learning with Computer Vision & NLP

Dear Vasavites

Deep Learning with Computer Vision and NLP

Started on Nov 28th but registrations are open until Nov 30th.


Duration: 45 Hours

About Instructor: Shravan is a senior Data Scientist having experience of  8+ years in ML & Deep Learning with overall 20+ years of industry experience. He has extensively worked in Pattern recognition, forecasting, classification, clustering, regression/fitting, Artificial Neural Network, RNN and CNN with Latest deep learning models BERT, GPU, NLP, Computer Vision. His primary tech skills are in Python, TensorFlow, Keras, Pytorch, CNN, RNN in Machine Learning & Deep Learning. He has trained more than 5K international students in ML & DL.



Prerequisites:
Python programming
Good understanding of Machine Learning concepts
Statistics basics
Understanding of 12th Standard mathematics


Introduction to AI and Deep Learning (2 Hours):
Introduction to Deep Learning
Working of a Deep Network
History and Evolution of Various Deep Learning Algorithms
Why Deep Learning

Deep Learning Essentials (7 Hours):
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

PyTorch Basics (6 Hours)
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

Introduction to Images and OpenCV Basics (3 Hours)
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

Convolutional Neural Networks (8 Hours)
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

Recurrent Neural Networks (6 Hours)

Fundamentals of RNN (Recurrent Neural Network)
Applications of RNN
Modelling sequencing
Types of RNNs - LSTM, GRU

Transformers, BERT & GPT (7 Hours)
Transformers Architecture
BERT – State of the art NLP technique

NLP using Spacy (6 Hours)
Getting started with SpaCy
Core operations with SpaCy
SpaCy features
Linguistic features
Rule based matching
Working with word vectors and semantic similarity