Live Coding on Breast Cancer Cell Detection using ML

Live Coding on Breast Cancer Cell Detection using ML

Details

Target Audience

Students, Professionals, Entrepreneurs, Healthcare professionals, anyone who is interested.

Agenda

SESSION 1 – Tanmoy Deb

Overview

In recent times, Breast cancer the most common cancer among women worldwide accounting for 25 percent of all cancer cases and affected 3.5 million people in[masked] early diagnosis significantly increases the chances of survival. The key challenge in cancer detection is how to classify tumors into malignant or benign. The machine learning techniques can dramatically improves the accuracy of diagnosis. Research indicates that most experienced physicians can diagnose cancer with 79 percent accuracy while 91 percent correct diagnosis is achieved using machine learning techniques.

To understand this problem set and understanding the Machine learning approaches world wide researchers are developing autonomous, user friendly solution to predict cancer detection. This may improve overall healthcare system, policies drastically in next coming years. So we are organizing a full day meetup to Host, interact about this topics.

Topics

#Introduction and importance of Machine Learning in Medical Applications
#Current Market Trends
#Introduction to Cancer Dataset and Significance
#Visualization of Dataset
#Understand the dataset
#Feature Selection and Random Forest Classification
#Feature Extraction
#Estimation and Classification
#How to improve the results, understand the parameters.

#Deployment strategy, Cost, Estimation.

SESSION 2 – Gaurav Sawaswat

Overview

Developing more specialized chips; reducing the computation required during deep learning is becoming very common these days. It is becoming common to run a small, efficient deep-learning model on the device itself. This idea, known as “AI on the edge,” could benefit from specialized, fixed chip architectures that are more efficient. Data centers that power “AI on the cloud,” on the other hand, would run on fully flexible and programmable chip architectures, to handle a much broader spectrum of learning tasks. This is a multi billion dollar industry shaping worldwide. This session will give overview of this.

Topics

#IP market for Artificial Intelligence
#Growth report on semiconductor market in VLSI and Machine Learning based systems
#Market Analysis on neural networks in semiconductor market

SESSION 3

#Announcements
#New Initiatives
#How to get job in Machine Learning.

STRATEGIC PARTNER

NASSCOM CoE-IoT & AI, Plot No1, Udyog Vihar Phase 1, Old Delhi –Gurgaon Road, Gurugram, Haryana -[masked]

SPEAKERS

Tanmoy Deb, Founder Equonix Tech Lab

Gaurav Saraswat, Global Business Development Manager, Semiconductor and IP, Equonix Tech Lab

ORGANIZERS MESSAGE

Get a chance to hear from the experts sharing their knowledge, doing Hands on practice with Industry experts and the work they are doing in this field.

Equonix Tech Lab believes that the outcome of any conference is based on its learning and experience for the delegates. There is already vast content accessible in the open world and we believe there is a need for the event where the participants can get more information on AI from there leaders.

NOTE

RSVP here is not final confirmation for the events seat. After payment you have to confirm at [masked].

HOW TO RESERVE YOUR SEAT

We need confirmation from all of you immediately. There is a fee
for attending this event.

For early bird it will be 300/-

You can send payment for confirmation via online:

  1. Use this link for transfer.

  2. You can send via paytm. The number is[masked].

REQUIRED TOOLS

  1. Windows 7 or later. Only 64 bit.
  2. Ubuntu 16.4 or above (if you don’t have windows)
  3. The system must be 64-bit, x86 desktops or laptops.
  4. Python 3.6 (otherwise the projects will not work)
  5. PyDev on Eclipse

Reference :

[1] Installation process of Python 3.6.x or any other version on Laptop : https://www.howtogeek.com/197947/how-to-install-python-on-windows/

[2] Installation of PyDev : https://www.rose-hulman.edu/class/csse/resources/Eclipse/eclipse-python-configuration.htm
Note : Use Eclipse Photon latest from eclipse.org

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