CNNs & Image Models

Computer Vision with PyTorch

This highly-rated course on deep learning for computer vision includes a comprehensive module on optimization algorithms for training neural networks.

This course explores advanced Convolutional Neural Networks (CNNs), Transfer Learning, and Recurrent Neural Networks (RNNs). It delves into sophisticated architectures like VGG16 and their practical applications.

Part of the DeepLearning.AI TensorFlow Developer Specialization, this course teaches best practices for using TensorFlow to build scalable AI-powered algorithms. You'll learn advanced techniques to improve computer vision models, including strategies to prevent overfitting like augmentation and dropout.

An introductory course to computer vision that covers image processing and the practical application of the OpenCV library with Python for AI and Machine Learning tasks. It provides insights into various methods for working with images.

This course will teach you Deep learning focusing on Convolution Neural Net architectures. It is structured to help you genuinely learn Deep Learning by starting from the basics until advanced concepts. We will begin learning what it is under the hood of Deep learning frameworks like TensorFlow and PyTorch, then move to advanced Deep learning Architecture with PyTorch.During our journey, we will also have projects exploring some critical concepts of Deep learning and computer vision, such as: what is an image; what are convolutions; how to implement a vanilla neural network; how back-propagation works; how to use transfer learning and more.All examples are written in Python and Jupyter notebooks with tons of comments to help you to follow the implementation. Even if you don’t know Python well, you will be able to follow the code and learn from the examples.The advanced part of this project will require GPU but don’t worry because those examples are ready to run on Google Colab with just one click, no setup required, and it is free! You will only need to have a Google account. By following this course until the end, you will get insights, and you will feel empowered to leverage all recent innovations in the Deep Learning field to improve the experience of your projects.

كورس لتعليم اساسيات التعلم العميق والشبكات العصبية الالتفافية للمبتدئين وحتى المستوى المتقدمسواء كنت طالباً فى علوم الحاسب او طالباً  فى الهندسة أو مبرمجاً وتعشق مجال الذكاء الاصطناعى , فإن هذا الكورس سيساعدك علي فهم أساسيات التعلم الشبكات العصبيه الالتفافية و الوصول إلى مستوى محترف وسوف يركز هذا الكورس على الجوانب النظرية وراء الخوارزميات والنماذج المنتشره هذه الايام للتعلم العميقThis course is focus on the theoretical aspects of the recent convolutional neural network based methods.Section 1: Introduction to Convolutional Neural Network (CNNs)Lecture 1: Introduction to Deep Learning Lecture 2: Image Net Challenge Lecture 3: Drawbacks of Previous Neural Networks Lecture 4: CNNs Motivation & History Section 2: Convolutional Neural Network Properties Lecture 5: Local Connectivity Lecture 6: Parameter Sharing Lecture 7: Pooling & Subsampling Section 3: Convolution Operation Lecture 8: Definition of Convolution Lecture 9: Image Convolution Example Lecture 10: Other Filters Section 4: Convolutional Neural Network Layers Lecture 11: Convolutional Layer Lecture 12: Strided Convolution Lecture 13: Strided Convolution with Padding Lecture 14: Convolution over Volume Lecture 15: Activation Function (ReLU)Lecture 16: Pooling Layer Lecture 17: Convolutional Network Lecture 18: Batch Normalization Layer Section 5: Convolutional Neural Network Architectures Lecture 19: Introduction to CNNs Architectures Lecture 20: Le Net-5Lecture 21: Alex Net & ZF Net Lecture 22: VGG Net Lecture 23: Google Net (Inception Networ