Faculty Development Program (FDP) on Advanced Deep Learning Techniques
ICFOSS
is organising a 5-day Faculty Development Program (FDP) on “Advanced
Deep Learning Techniques” from 16th
August 2022 to 20th August 2022. The
contents will be
coveredpractically
with concepts of theoretical knowledge. Participants
will be provided with lab facilities for hands-on training.
Objectives:
This
programme intends to facilitate up-gradation of knowledge, skill and
assistance to the participants.
Course
Highlights:
Live
Classes
Structured
curriculum by industry veterans
Customized
for time flexibility
Custom
learning path
Practical
experience through simulations tests
Certification
Prerequisite:
Basic knowledge in Python
and Machine
learning is preferable.
Mode
of Training:
This
hands-on program is meant to explore the concepts of advanced deep
learning techniques using Transfer learning, Auto encoders, and GAN
along with some sessions on python and concepts of Machine learning
and Neural networks.
Modules:
Introduction
to Python, Familiarization with Numpy, Pandas and
Matplotlib
(Installation process, Google Colab). Hands on –
Python Programming, Linear Algebra, Regularization and Optimization –
Mathematical Concepts, Fundamentals of ANN, Deep Neural
Network,
CNN, RNN, Hands on -ANN in Tensorflow , Convolutional Neural
Networks
(CNN), Transfer Learning(Alexnet) , Auto Encoders ,
Variational Auto Encoders and GAN
Targeted
Participants:
Faculty
Members , Research scholars and PG students.
30
No.s / Batch on first-come-first- served basis.
Certification
Criteria:
80%
of attendance
Dates:
From 16th
August to 20th
August 2022
Registration
Fee: Faculty Members:Rs. 2,000
/-
PG
students and Research scholars: Rs.1,000/-
Last
date of application : 12th
August 2022
For
online payment, the bank accounts details of ICFOSS is as follows:
Account
Name
:
ICFOSS
Account
Number
:
67242303296
IFSC
:
SBIN0070737
Name
of Bank
:
State
Bank of India
Branch
:
Technopark,
Thejaswini, Thiruvananthapuram
Candidates
are requested to upload the below given particulars in the following
size & format, during the time of registration: