If you're interested in learning about Convolutional Neural Networks (CNNs) and want to apply them to image recognition problems, then the course "Convolutional Neural Networks in Python: CNN Computer Vision" may be the perfect fit for you. This comprehensive course is taught by Abhishek and Pukhraj and focuses on teaching you everything you need to know about creating an Image Recognition model in Python using CNNs.
Course Overview
The course is designed for analysts, ML scientists, and students who want to learn and apply Deep learning in real-world image recognition problems. The aim of the course is to provide a solid base for students in deep learning by teaching them some of the most advanced concepts of Deep Learning and their implementation in Python without making it too mathematical.
The course consists of six parts:
- Python basics
- ANN Theoretical concepts
- Creating ANN model in Python
- CNN Theoretical concepts
- Creating CNN model in Python
- End-to-End Image Recognition project in Python
Each part is designed to help you master the concepts of Deep Learning and apply them to image recognition problems. By the end of this course, you will be confident in creating a Convolutional Neural Network model in Python and will have a thorough understanding of how to use CNN to solve image recognition problems.
The Instructors
The course is taught by Abhishek and Pukhraj, who are managers in a Global Analytics Consulting firm. They have helped businesses solve their business problems using Deep learning techniques and have used their experience to include the practical aspects of data analysis in this course.
Abhishek and Pukhraj are also the creators of some of the most popular online courses in Deep Learning with over 1,300,000 enrollments and thousands of 5-star reviews.
Course Content
The first part of the course covers Python basics and gets you started with Python. It will help you set up the python and Jupyter environment on your system and teach you how to perform some basic operations in Python. You will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
The second part will give you a solid understanding of the concepts involved in Neural Networks. You will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, you will understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.
In the third part, you will learn how to create ANN models in Python. You will start this section by creating an ANN model using the Sequential API to solve a classification problem. You will learn how to define network architecture, configure the model and train the model. Then you will evaluate the performance of the trained model and use it to predict new data. Lastly, you will learn how to save and restore models. You will also understand the importance of libraries such as Keras and TensorFlow in this part.
The fourth part is about the Convolutional Neural Networks (CNNs) theoretical concepts. You will learn about convolutional and pooling layers which are the building blocks of CNN models. In this section, you will start with the basic theory of the convolutional layer, stride, filters, and feature maps. You will also explain how gray-scale images are different from colored images. Lastly, you will discuss the pooling layer which brings computational efficiency into our model.
The fifth part will teach you how to create CNN models in Python. You will take the same problem of recognizing fashion objects and apply a CNN model to it. You will compare the performance of the CNN model with the ANN model and notice that the accuracy increases by 9-10% when using CNN. You can further improve accuracy by using certain techniques, which are explored in the next part.
In the sixth part, you will build a complete image recognition project on colored images. You will take a Kaggle image recognition competition and build a CNN model to solve it. With a simple model, you will achieve nearly 70% accuracy on the test set. Then you will learn concepts like Data Augmentation and Transfer Learning, which help improve accuracy from 70% to nearly 97% (as good as the winners of that competition).
Course Benefits
The course teaches you all the steps of creating a Neural network based model, i.e., a Deep Learning model, to solve business problems. By the end of the course, your confidence in creating a Convolutional Neural Network model in Python will soar. The course will help you in:
- Identifying the Image Recognition problems which can be solved using CNN models.
- Creating CNN models in Python using Keras and TensorFlow libraries and analyzing their results.
- Confidently practicing, discussing and understanding Deep Learning concepts.
- Having a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16, etc.
Why Choose This Course?
The 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 this course believes that having a strong theoretical understanding of the concepts enables us to create a good model. And after running the analysis, one should be able to judge how good the model is and interpret the results to actually help the business.
The course is taught by Abhishek and Pukhraj, who are experienced consultants and instructors, with a proven track record in data science. They have created some of the most popular online courses in Deep Learning with over 1,300,000 enrollments and thousands of 5-star reviews.
You will also get a verifiable Certificate of Completion upon completion of the course. 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 a direct message to Abhishek and Pukhraj.
The course is perfect for analysts, ML scientists, and students who have some experience in data analysis and want to learn and apply Deep learning in real-world image recognition problems.
FAQs
Why use Python for Deep Learning?
Python is one of the most important skills for a career in Deep Learning. It's the programming language of choice for data science, and it's expected that its use in Deep Learning will continue with increasing development in the Python ecosystem.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Data mining discovers previously unknown patterns and knowledge, while machine learning reproduces known patterns and knowledge. Deep learning uses advanced computing power and special types of neural networks and applies them to learn, understand, and identify complicated patterns.
Enroll Today
If you're interested in learning about Convolutional Neural Networks in Python and want to apply them to image recognition problems, then enroll today and start your journey into the world of Deep Learning!
