Online Store - 8925533488 /89
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Covai - 8925533486 /87
2021-05-24 15:30 to 2021-05-28 17:00
May 24, 2021
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About the Program
This course is designed to provide an exposure to the fundamentals of Artificial Intelligence and Deep Learning. Participants will learn to apply deep learning techniques to solve the real-world research and industry problems. Hands-on training and practice sessions will help participants gain confidence on deep learning concepts by creating their own neural networks, object detection models etc. The course will be useful for faculty of engineering and sciences who are interested in the learning recent AI trends and their applications in areas like image processing, computer vision and robotics.
Technologies and Tools Covered
Importance of the Course
|1||Introduction to Python Programming|
|2||Software Installation IDE, Anaconda Navigator|
|3||Introduction to Machine Learning and Deep Learning Package|
|4||Training Machine Learning Model|
|5||Inference from Machine Learning Model|
Here’s how to make a Sequential Model and a few commonly used layers in deep learning.
Example of how to make convolutional layer as the input layer with the input with filters of size and use ReLU as an activation function. How to down sample the input representation, use MaxPool2d and specify the kernel size. Adding a Fully Connected Layer with just specifying the output Size. Adding dropout layer with Compiling, Training, and Evaluate
|1||Installing Procedure of KERAS|
|2||Loading a Data|
|3||Define a Neural network in keras|
|4||Compile a Keras model using the efficient numerical backend.|
|5||Train a model on data.|
|6||Evaluate a model on data and make predictions with the model.|
CNN and Project Working
|1||Building a CNN model|
|2||CNN for image classification|
|3||Introduction to LSTM, GRU|
|4||Applications in machine translation language modelling and sentiment classification.|
Projects & Assignments
Practical 1 : Using CSV Dataset.
Practical 2 : Using Image Classification.
Practical 3 : Building an AI application for sentiment classification
Practical 4 : AI Face Recognition using Deep Learning .
The Program is mix of Theory sessions, Quizzes, Hands on Sessions, Interaction with Experts, Assignments and Practical Exercises. Maximum Impetus is given to Hands on Sessions so as to enable the participants with the maximum knowledge transfer and satisfaction. The ratio of the theory, practical sessions will be 30:70.