Anup Vasu Padaki Control Engineer, Software Engineer
1 year experience0 projects worked India
$10.00 /hr $0 earned
AI EngineersElectrical EngineeringMathematicsMatlab & MathematicaPython
I am a Control Engineer with an advanced degree in Systems, Controls and Mechatronics from Chalmers University of Technology in Sweden. My areas of interest are Deep Learning, Controls, Machine Learning, and Sensor-Fusion.
1. Working in Fuel Economy and Performance team Research and Development team in CEVT AB.
2. one of the only two to have worked on AI in CEVT AB at that time.
3. Worked on Fuel Economy Optimisation with Deep Learning for Plug-In Hybrid vehicles with real time values, models and constraints.
4. A faster python simulation platform was also developed for faster simulations.
October 2019 -
1. Part of Research and Development team, involved in Product development using Multiple Radar fusion and AI
2. Supervised Master Thesis in AI and Signal Processing
3. Was responsible for certification of Functional Safety Products
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Natural Language Processing in TensorFlow
TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train an LSTM on existing text to create original poetry!
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning
Convolutional Neural Networks in TensorFlow
advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. transfer learning and how learned features can be extracted from models.
AI for Everyone
– The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
– What AI realistically can–and cannot–do
– How to spot opportunities to apply AI to problems in your own organization
– What it feels like to build machine learning and data science projects
– How to work with an AI team and build an AI strategy in your company
– How to navigate ethical and societal discussions surrounding AI
Natural Language Processing with Sequence Models
a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets,
b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model,
c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and
d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning.
Natural Language Processing with Probabilistic Models
a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming,
b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is important for computational linguistics,
c) Write a better auto-complete algorithm using an N-gram language model, and
d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model.
Natural Language Processing with Classification and Vector Spaces
a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes,
b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, and
c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality sensitive hashing to relate words via approximate k-nearest neighbor search.
Add work education to your profile. (optional)
Bachelor of Engineering in Automation and Robotics
B.V.Bhoomraddi College of Engineering and Technology
August 2013 - June 2017
Master of Science in Systems, Controls and Mechatronics