Convolutional Neural Networks (CNN)
The Ultimate Training for Convolutional Neural Networks (CNN)
Classes : 20 Days : 2 months Duration : Weekdays / Weekends
A CNN is the top choice for image classification and more generally, computer vision. Examples of this are medical image analysis, image recognition, face recognition, generating and enhancing images, and full-motion video analysis.
CNNs are one of the top technologies powering self-driving cars. In this course, you`ll follow hands-on examples to build a CNN, train it using a custom scale tier on Machine Learning Engine, and visualize its performance.
This course will satisfy your curiosity by explaining the features of neural networks in processing sequences such as text, sound, videos and images.
The course introduces the fundamental operations and parameters of convolution. The multiple approaches for understanding and visualizing CNNs are discussed. The concept of understanding and processing tasks for face recognition and verification are disclosed. The various functions of CNN architectures for extending the support beyond residual neural networks are described.
This is an enlightening course. Why Wait? Register today, and become dominant in various computer vision tasks using CNNs.
Trainers:
Experts from the field of Maths, Data Science and Management, each with over 25 years of International experience working in EU/US/Australia
What you'll learn
:- Get a solid understanding of Convolution Neural Networks (CNN) and Deep Learning
:- Learn usage of Keras and Tensorflow libraries
:- Understand the business scenarios where Convolution Neural Networks (CNN) is applicable
:- Building and Train an Convolution Neural Networks (CNN) in Python
:- Use Convolution Neural Networks (CNN) to make predictions
Who this course is for:
:- People pursuing a career in Data Science
:- Anyone curious to master CNN from Beginner level in short span of time
:- Data Analysts and Engineers
:- University Students
:- Scientists and Researchers
:- You are hands-on with Machine Learning using Python.
:- You have theoretical knowledge of Artificial Neural Networks (ANN).
:- You have a genuine interest in CNN.
:- Introduction to CNN
:- Backpropagation in CNN
:- CNN architecture
:- Fine Tuning CNN and Hyperparameters
:- Visualization and Understanding CNN
:- Methods of CNN visualization
:- Class Attribution Map methods
:- Object Detection and Segementation
:- Dense Sampling Methods
:- Human Faces
:- Human Pose and Crowd
:- Animal faces and Other image tasks
:- Deep Convolutional Models
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Student Reviews
Bhawana
Fabulous NLP + ML course
I have eleven plus years of experience taking training courses. I do not usually complete surveys.
Your instructor was excellent, the best I've experienced on a software subject, and I couldn't imagine him doing a better job of seamlessly walking students through a breadth of information for such complex subject like AI and ML. he did a fabulous job pacing everything and addressing student questions. I am very impressed.
Harish
Excellent ML course!
The course was well structured and easy to understand. Good pace of learning.
The institute believes to provide knowledge as well as guidance in detail to each & every student.
I completed my ML course from the institute. Their international exp does help a lot !
Thanks for the training sir.