Convolutional Neural Networks in Python: CNN Computer Vision
Python for Computer Vision & Image Recognition – Deep Learning Convolutional Neural Network (CNN) – Keras & TensorFlow 2
What you’ll learn
- Get a solid understanding of Convolutional Neural Networks (CNN) and Deep Learning
- Build an end-to-end Image recognition project in Python
- Learn usage of Keras and Tensorflow libraries
- Use Artificial Neural Networks (ANN) to make predictions
- Use Pandas DataFrames to manipulate data and make statistical computations.
You’ve found the right Convolutional Neural Networks course!
After completing this course you will be able to:
- Create CNN models in Python using Keras and Tensorflow libraries and analyze their results.
- Confidently practice, discuss and understand Deep Learning concepts
How this course will help you?
Why should you choose this course?
This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.
What makes us qualified to teach you?
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Download Practice files, take Practice test, and complete Assignments
There is a final practical assignment for you to practically implement your learning.
Below are the course contents of this course on ANN:
By the end of this course, your confidence in creating a Convolutional Neural Network model in Python will soar. You’ll have a thorough understanding of how to use CNN to create predictive models and solve image recognition problems.
Go ahead and click the enroll button, and I’ll see you in lesson 1!
Below are some popular FAQs of students who want to start their Deep learning journey-
Why use Python for Deep Learning?
Here’s a brief history:
Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary.
Automatic language translation and medical diagnoses are examples of deep learning.
Who this course is for:
- People pursuing a career in data science
- Working Professionals beginning their Deep Learning journey