CNN for Computer Vision with Keras and TensorFlow in Python
Python for Computer Vision & Image Recognition - Deep Learning Convolutional Neural Network (CNN) - Keras & TensorFlow 2
Updated on Jul, 2024
Language - English
Lectures -53
Resources -2
Duration -7 hours
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Course Description
You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create an Image Recognition model in Python, right?
You've found the right Convolutional Neural Networks course!
After completing this course, you will be able to:
Identify the Image Recognition problems that can be solved using CNN Models.
Create CNN models in Python using Keras and Tensorflow libraries and analyze their results.
Confidently practice, discuss, and understand Deep Learning concepts
Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural Networks course.
If you are an Analyst, an ML scientist, or a student who wants to learn and apply Deep learning in real-world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.
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.
Most courses only focus on teaching how to run the analysis, but we believe that having a strong theoretical understanding of the concepts enables us to create a good model. After running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in a global analytics Consulting firm, we have helped businesses solve their business problems using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet, or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Practice test, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take practice tests to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.
Who this course is for:
- People pursuing a career in data science
- Working Professionals beginning their Deep Learning journey
- Anyone curious to master image recognition from the Beginner level in a short span of time
Goals
- 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.
Prerequisites
- Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
2 Lectures
-
Introduction
03:29
03:29
-
Course resources
Setting up Python and Jupyter Notebook
9 Lectures
Single Cells - Perceptron and Sigmoid Neuron
3 Lectures
Neural Networks - Stacking cells to create network
3 Lectures
Important concepts: Common Interview questions
1 Lectures
Standard Model Parameters
1 Lectures
Tensorflow and Keras
2 Lectures
Python - Dataset for classification problem
2 Lectures
Python - Building and training the Model
4 Lectures
Saving and Restoring Models
1 Lectures
Hyperparameter Tuning
1 Lectures
CNN - Basics
6 Lectures
Creating CNN model in Python
3 Lectures
Analyzing impact of Pooling layer
1 Lectures
Project : Creating CNN model from scratch
5 Lectures
Project : Data Augmentation for avoiding overfitting
2 Lectures
Transfer Learning : Basics
5 Lectures
Transfer Learning in Python
1 Lectures
Instructor Details
Abhishek and Pukhraj
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