Enroll Course: https://www.udemy.com/course/ittensive-machine-vision-recognition/
Embark on a fascinating journey into the world of Computer Vision with ITtensive’s comprehensive Udemy course, “Машинное зрение: распознавание объектов на Python” (Machine Vision: Object Recognition in Python). This course is an exceptional starting point for anyone looking to understand and implement image recognition using neural networks in Python. It’s structured into three core parts, providing a robust foundation and practical application.
The first section delves into the fundamentals of neural networks. You’ll learn about neurons, layers, connections, backpropagation, and multilayer perceptrons. Crucially, the course covers the intricacies of training and optimizing these networks. You’ll then be introduced to Convolutional Neural Networks (CNNs) and explore popular architectures like LeNet, AlexNet, VGG, and ResNet, gaining a solid theoretical understanding before diving into practical implementation.
The second part is where theory meets practice. Using Python and the Keras library, you’ll build and train neural network models to recognize handwritten digits from the MNIST dataset. This section meticulously covers all the applied aspects of working with neural networks in Keras, including:
* Understanding digitized images.
* Creating models and layers.
* Reshaping multidimensional arrays.
* Image generators and augmentation.
* Training, testing, and validation datasets.
* Optimization functions and training batches.
* Practical network optimization.
* Visualizing the training process.
* Batch normalization, regularization, and dropout.
* Weight initialization methods.
The third and final section focuses on a real-world application: recognizing car license plates. You’ll work with a dataset of car license plate images, learning to load, filter, and transform them. The course covers training generators from directories, manipulating image contrast, sharpness, and histogram masks. You’ll learn to recognize one of 21 classes of images (digits and letters) and apply your trained model to real-world data. The capstone project involves developing your own trained neural network capable of recognizing car license plates from photographs.
**Recommendation:**
This course is highly recommended for aspiring machine learning engineers, data scientists, and developers who want to gain hands-on experience in computer vision. The progression from theoretical concepts to practical implementation with detailed explanations of Keras functionalities makes it an invaluable resource. While the course title is in Russian, the comprehensive nature of the content and the practical focus make it accessible and beneficial for a global audience interested in this cutting-edge field. To access the course, remember to contact support@ittensive.com with the course title or the group of courses you wish to enroll in.
**Tags:**
Computer Vision, Machine Learning, Neural Networks, Deep Learning, Python, Keras, Object Recognition, Image Recognition, CNN, MNIST
Enroll Course: https://www.udemy.com/course/ittensive-machine-vision-recognition/