Deep Learning : Convolutional Neural Networks with Python

Description

Are you ready to unlock the power of deep learning and revolutionize your career? Dive into the captivating realm of Deep Learning with our comprehensive course Deep Learning: Convolutional Neural Networks (CNNs) using Python and Pytorch. Discover the power and versatility of CNNs, a cutting-edge technology revolutionizing the field of artificial intelligence. With hands-on Python tutorials, you'll unravel the intricacies of CNN architectures, mastering their design, implementation, and optimization. One of the key advantages of deep CNN is its ability to automatically learn features at different levels of abstraction. Lower layers of the network learn low-level features, such as edges or textures, while higher layers learn more complex and abstract features. This hierarchical representation allows deep learning models to capture and understand complex patterns in the data, enabling them to excel in tasks such as image recognition, natural language processing, speech recognition, and many others.

Introducing our comprehensive deep CNNs with python course, where you'll dive deep into Convolutional Neural Networks and emerge with the skills you need to succeed in the modern era of AI. Computer Vision refers to AI algorithms designed to extract knowledge from images or videos. Computer vision is a field of artificial intelligence (AI) that enables computers to understand and interpret visual information from digital images or videos. It involves developing deep learning algorithms and techniques that allow machines to analyze, process, and extract meaningful insights from visual data, much like the human visual system. Convolutional Neural Networks (CNNs) are most commonly used Deep Learning technique for computer vision tasks. CNNs are well-suited for processing grid-like input data, such as images, due to their ability to capture spatial hierarchies and local patterns.

In today's data-driven world, Convolutional Neural Networks  stand at the forefront of image recognition, object detection, and visual understanding tasks. Understanding CNNs is not only essential for aspiring data scientists and machine learning engineers but also for professionals seeking to leverage state-of-the-art technology to drive innovation in various domains. From self-driving cars and medical imaging to facial recognition and augmented reality, CNNs find applications across diverse industries. Whether you're interested in revolutionizing healthcare, enhancing autonomous systems, or developing cutting-edge computer vision applications, this course equips you with the knowledge and skills to excel in any CNN-related endeavor.

Course Key Learning Outcomes:

• Deep Convolutional Neural Networks with Python and Pytorch Basics to Expert

• Introduction to Deep Learning and its Building Blocks Artificial Neurons

• Define Convolutional Neural Network Architecture from Scratch with Python and Pytorch

• Hyperparameters Optimization For Convolutional Neural Networks to Improve Model Performance

• Custom Datasets with Augmentations to Increase Image Data Variability

• Training and Testing Convolutional Neural Network using Pytorch

• Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs

• Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score

• Advanced CNNs for Segmentation, Object tracking, and Pose Estimation.

• Pretrained Convolutional Neural Networks and their Applications

• Transfer Learning using Convolutional Neural Networks Models

• Convolutional Neural Networks Encoder Decoder Architectures

• YOLO Convolutional Neural Networks for Computer Vision Tasks

• Region-based Convolutional Neural Networks for Object Detection

In this comprehensive course you will start from building Deep Convolutional Neural Networks  architecture from scratch with Dataset Augmentation with different transformations to increase image variability , HyperParameteres Optimization before training the model to improve performance, Model validation on Test Images, Performance metrics calculation including Accuracy, Precision, Recall, F1 score and Confusion matrix visualization to see detailed insights into the model's performance, beyond simple metrics. Then you will move forward to advanced CNN Architectures Including RESNT, ALEXNET for Images Classification, UNET, PSPNET encoder decoder Architectures for semantic segmentation, Region based CNN for OD and YOLO CNNs for real time object Detection, classification instance segmentation, object tracking, and pose estimation.

Join us on this exciting journey, where you'll not only grasp the core concepts but also unlock the door to advanced CNN architectures, equipping yourself with the skills needed to conquer the most challenging computer vision tasks with confidence and expertise. You will follow a complete pipeline to deep dive into CNN for real world applications. I will provide you the complete python code to build, train, test, and deploy CNN from scratch for different Artificial Intelligence tasks.

Don't miss out on this incredible opportunity to take your skills to the next level. Enroll now and join the thousands of students who've already transformed their careers with our courses. “ Thank you and see you inside the class" !

Who this course is for:

• This course is designed for individuals with a keen interest in Deep Learning and Convolutional Neural Networks (CNNs) with Python and Pytorch to solve Real-World AI Problems.
• Whether you're a beginner looking to build a strong foundation in Computer Vision, Object Tracking, Segmentation, Pose Estimation, Classification, Object Detection or an experienced professional aiming to enhance your skills, this course provides valuable insights and hands-on experience with CNNs.