Convolutional Neural Networks in Python: CNN Computer Vision

Python for Computer Vision & Image Recognition - Deep Learning Convolutional Neural Network (CNN) - Keras & TensorFlow 2

4.49 (1413 reviews)

Udemy

platform

English

language

Data Science

What you will 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.

Course Gallery

Convolutional Neural Networks in Python: CNN Computer Vision – Screenshot 1 — Screenshot 1 – Convolutional Neural Networks in Python: CNN Computer Vision

Convolutional Neural Networks in Python: CNN Computer Vision – Screenshot 2 — Screenshot 2 – Convolutional Neural Networks in Python: CNN Computer Vision

Convolutional Neural Networks in Python: CNN Computer Vision – Screenshot 3 — Screenshot 3 – Convolutional Neural Networks in Python: CNN Computer Vision

Convolutional Neural Networks in Python: CNN Computer Vision – Screenshot 4 — Screenshot 4 – Convolutional Neural Networks in Python: CNN Computer Vision

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Comidoc Review

Our Verdict

This Udemy course provides a thorough introduction to Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN), specifically tailored for image recognition projects in Python using Keras & TensorFlow 2. The course has garnered a strong global rating of 4.47, with over 129k students subscribed.\n\nThroughout the 8-hour course, the instructors precisely explain complex topics in simple words and provide numerous examples to help learners grasp key concepts easily. Students who previously struggled with understanding CNNs attested to having 'much clarification' and were able to make well-trained models after completing the course, making it perfect for those seeking a solid understanding of CNN theory alongside practical implementation.\n\nWe recommend this course to both beginners looking for an in-depth yet accessible overview of deep learning and computer vision as well as intermediate learners seeking to enhance their knowledge with real-world examples and practical exercises. However, adding more hands-on coding exercises to better solidify the theory presented would improve the learning experience further.

What We Liked

Covers computer vision basics in depth, great for beginners
Thorough introduction to both ANN and CNN concepts
State-of-the-art content and clear instructions
Numerous easy-to-understand examples included

Potential Drawbacks

Code implementation could be improved with more hands on exercises
Some real-world examples would help illustrate theory better
Minor subtitle errors detected in course videos