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Date- July 15 th , 2020
AI Essentials (Linux, Python, GIT, Math for AI & Statistics): This module is the beginning of the course wherein we will start with the basics of OS and how ML algorithms are ported onto the server along with the introduction to Linux OS. The module will follow Python coding, Computer Science fundamentals and drawing upon libraries and automation scripts to solve complex problems quickly. Basic tools used for the coding like Jupyter Notebooks, Google Colab and Pycharm. A refresher of the key mathematical concepts required for AI, statistics, sets, linear algebra, vectors, and matrices.
This module drives straight into the ML basics and techniques to solve the most common data problems. Difference between supervised and un-supervised learning and their categories. regression, clustering, and classification problems with both structured and un-structured data. Cohorts will start working with dedicated AI libraries and tools using real case studies. Module will lead regularization techniques used in ML, data preparation techniques to handle missing data, data imbalance, data transformation and feature repository and CLARITY tool for data analytics, which is proprietary to Automaton AI.
Here we will start with industry-standard microservices messaging protocols such as kafka and RabbitMQ followed by understanding databases (relational & non-relational) and work on the ETL pipeline for large scale databases. The advanced DevOps tools for model deployments. Workings of the data centres managed and used for the DL modelling will be covered.
Here cohorts will work on the real-time DL training and inference infrastructure like state-of-the-art GPU architecture. Hands-on CUDA coding to understand the DL acceleration cycle on the hardware acceleration devices with an exposure to the DL and embedded edge devices such as Jetson Xavier, Jetson Nano, etc. work on OpenCV learning for a deep understanding of filters and algorithms used in the DL applications. The real-time video/media analytics tools using GStreamer and video analytics pipeline like Deepstream and real-time data labelling tool “ADVIT”, which is the proprietary tool of the Automaton AI.
Neural Networks and Deep Learning (DL)
Leveraging the machine learning knowledge acquired previously, this module will challenge cohorts to get to the cutting edge of current AI technology. Theoretical introduction to Neural network and their architecture and hands-on work with the most advanced tools and frameworks such as Pytorch, TensorFlow, and Keras applying the theoretical knowledge to some transfer learning applications. Cohort’s will learn about GloVe, word2vec models for text data preparation, moving to LSTM, BERT and Transformer networks. Focus on different GANs architecture which are used for Image upscaling, Neural style transfer, and different smart image operations like image denoising, dehazing, etc. Speaker recognition & verification models which is most widely used in the conversational AI.
The program would not be complete without putting all the knowledge into an end-to-end AI application.
The Bootcamp will be in English language and requires basic level knowledge of Python programming.
* (Talk to our advisor if you don’t meet the pre-requisites)
Mukesh has an extensive experience in the industry with more than 10 years of work experience. He holds various skills and has created multiple AI tools for various companies.
Vinay’s area of expertise includes Financial Analysis, Business analytics and data science. He has mastered in tools like advanced Microsoft Excel, VBA, Python, Power Bi & R
“Aatish is a Artificial intelligence and Deep learning professional, a application Engineer who has keen interest in delivering innovative and cutting edge AI, Machine Learning to enterprise and developing tomorrow’s advances in AI. He is Post Graduated from College of Engineering Pune (COEP) in VLSI and Embedded Systems