Dive deep into convolutional neural networks with Python. Gain practical skills and insights—enroll in our course today to get started! Read more.
Mazhar Hussain is currently in the role of Deep Learning and Computer Vision Engineer. He has extensive teaching experience at University Higher Education level and Online over a decade. He has published several research papers on Deep Learning in well-reputed Journals and Conferences. He believes on comprehensive practical trainings with stunning support for his students where all his courses are 100% hands-on with step-by-step problem-based learning, demos and examples. Mazhar Hussain is te
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Who this course is for:
- Individuals eager to explore Deep Learning and Convolutional Neural Networks (CNNs) using Python and PyTorch.
- Beginners seeking a solid foundation in:
- Computer Vision
- Object Tracking
- Segmentation
- Pose Estimation
- Classification
- Object Detection
- Professionals seeking to elevate their expertise in these fields.
What you’ll learn:
- Deep Convolutional Neural Networks with Python and PyTorch, from basics to advanced
- Introduction to Deep Learning and its foundational concepts
- Designing Convolutional Neural Network architectures from scratch
- Hyperparameter optimization for improved model performance
- Creating custom datasets with augmentations to increase image variability
- Training and testing CNNs using PyTorch
- Evaluating CNNs with performance metrics (Accuracy, Precision, Recall, F1 Score)
- Visualizing confusion matrices and calculating precision, recall, and F1 scores
- Advanced CNNs for segmentation, object tracking, and pose estimation
- Utilizing pretrained CNNs and exploring transfer learning
- Implementing encoder-decoder architectures and YOLO for computer vision tasks
- Region-based CNNs for object detection
Requirements:
- A Google Gmail account is necessary to get started with Google Colab for writing Python code.
- Python programming experience is beneficial but not required.
Are you ready to unlock the power of deep learning and revolutionize your career? Dive into our comprehensive course, “Deep Learning: Convolutional Neural Networks (CNNs) with Python and PyTorch.” Discover the versatility of CNNs, a cutting-edge technology transforming AI. Through hands-on Python tutorials, you’ll master CNN architecture design, implementation, and optimization. The hierarchical structure of deep CNNs enables them to automatically learn features at various abstraction levels, excelling in image recognition, natural language processing, and more.
In this course, you’ll build Convolutional Neural Networks with Python from scratch, apply dataset augmentations to enhance image variability, and optimize hyperparameters before training your model. You’ll validate models on test images and explore performance metrics beyond simple calculations, gaining insights into your model’s effectiveness. Move on to advanced architectures, including ResNet and AlexNet for image classification, U-Net and PSPNet for semantic segmentation, and YOLO for real-time object detection.
Join us on this exciting journey to not only grasp core concepts but also unlock advanced CNN architectures. Equip yourself with the skills to address complex computer vision tasks with confidence. I will provide the complete Python code to build, train, test, and deploy CNNs for various AI applications. Don’t miss this incredible opportunity to elevate your skills—enroll now and join thousands of students who have transformed their careers with our courses. Thank you, and see you in class!
Interested in learning from the best in the industry? Join my courses and gain access to expert knowledge.
Our Promise to You
By the end of this course, you will have learned to build and optimize Convolutional Neural Networks with Python and PyTorch.
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.
Get started today!
Course Curriculum
| Section 1 - Introduction To Course | |||
| Introduction | 00:00:00 | ||
| Section 2 - Artificial Neurons - The Building Blocks Of Deep Learning | |||
| Introduction To Deep Learning And Artificial Neurons | 00:00:00 | ||
| Section 3 - Introduction To Convolutional Neural Networks (CNNs) | |||
| Introduction To Convolutional Neural Networks (CNNs) | 00:00:00 | ||
| Section 4 - Coding Convolutional Neural Networks From Scratch In Python | |||
| Google Colab Environment For Writing Python And Pytorch Code | 00:00:00 | ||
| Coding Convolutional Neural Network Architecture From Scratch Using Python | 00:00:00 | ||
| Section 5 - Dataset And Its Augmentation | |||
| Dataset And Its Augmentation | 00:00:00 | ||
| Section 6 - Hyperparameters Optimization For Convolutional Neural Networks | |||
| Hyperparameters Optimization For Training Models | 00:00:00 | ||
| Section 7 - Training Convolutional Neural Network From Scratch | |||
| Training Convolutional Neural Network From Scratch | 00:00:00 | ||
| Section 8 - Validating Convolutional Neural Network On Test Images | |||
| Validating Convolutional Neural Network On Test Images | 00:00:00 | ||
| Section 9 - Performance Metrics (Accuracy, Precision, Recall, F1 Score) To Evaluate CNNs | |||
| Performance Metrics (Accuracy, Precision, Recall, F1 Score) To Evaluate CNNs | 00:00:00 | ||
| Section 10 - Visualize Confusion Matrix And Calculate Precision, Recall, And F1 Score | |||
| Visualize Confusion Matrix And Calculate Precision, Recall, And F1 Score | 00:00:00 | ||
| Section 11 - Resources Python Code For Convolutional Neural Networks From Scratch | |||
| Resources: Python Code For Convolutional Neural Networks From Scratch | 00:00:00 | ||
| Section 12 - Pretrained Convolutional Neural Networks | |||
| Pretrained Convolutional Neural Networks With Python | 00:00:00 | ||
| Code Single Label Classification | 00:00:00 | ||
| Code Multi Label Classification | 00:00:00 | ||
| Section 13 - Transfer Learning using Convolutional Neural Networks | |||
| What is Transfer Learning | 00:00:00 | ||
| Transfer Learning By Fine Tuning CNNs Models | 00:00:00 | ||
| Transfer Learning with CNNs Models As Fixed Feature Extractor | 00:00:00 | ||
| Dataset | 00:00:00 | ||
| Code For Transfer Learning | 00:00:00 | ||
| Section 14 - Convolutional Neural Networks Encoder-Decoder Architectures | |||
| Convolutional Neural Networks Based Encoders | 00:00:00 | ||
| Convolutional Neural Networks Based Decoders | 00:00:00 | ||
| Multi-Task Contextual Encoder-Decoder Network | 00:00:00 | ||
| Resources: Encoder-Decoder For Semantic Segmentation With PyTorch Code | 00:00:00 | ||
| Section 15 - YOLO Convolutional Neural Networks | |||
| YOLO Convolutional Neural Networks Architecture | 00:00:00 | ||
| How YOLO Works To Identify Objects | 00:00:00 | ||
| Latest YOLOv8 Deep Convolutional Neural Network | 00:00:00 | ||
| YOLO Object Detection With Python | 00:00:00 | ||
| Section 16 - Region-Based Convolutional Neural Networks | |||
| Region-Based Convolutional Neural Networks (RCNN, FAST RCNN, FASTER RCNN) | 00:00:00 | ||
| Detectron2 For Object Detection With PyTorch | 00:00:00 | ||
| Perform Object Detection Using Detectron2 Models | 00:00:00 | ||
| Resources: Python And PyTorch Code For Object Detection | 00:00:00 | ||
About This Course
Who this course is for:
- Individuals eager to explore Deep Learning and Convolutional Neural Networks (CNNs) using Python and PyTorch.
- Beginners seeking a solid foundation in:
- Computer Vision
- Object Tracking
- Segmentation
- Pose Estimation
- Classification
- Object Detection
- Professionals seeking to elevate their expertise in these fields.
What you’ll learn:
- Deep Convolutional Neural Networks with Python and PyTorch, from basics to advanced
- Introduction to Deep Learning and its foundational concepts
- Designing Convolutional Neural Network architectures from scratch
- Hyperparameter optimization for improved model performance
- Creating custom datasets with augmentations to increase image variability
- Training and testing CNNs using PyTorch
- Evaluating CNNs with performance metrics (Accuracy, Precision, Recall, F1 Score)
- Visualizing confusion matrices and calculating precision, recall, and F1 scores
- Advanced CNNs for segmentation, object tracking, and pose estimation
- Utilizing pretrained CNNs and exploring transfer learning
- Implementing encoder-decoder architectures and YOLO for computer vision tasks
- Region-based CNNs for object detection
Requirements:
- A Google Gmail account is necessary to get started with Google Colab for writing Python code.
- Python programming experience is beneficial but not required.
Are you ready to unlock the power of deep learning and revolutionize your career? Dive into our comprehensive course, “Deep Learning: Convolutional Neural Networks (CNNs) with Python and PyTorch.” Discover the versatility of CNNs, a cutting-edge technology transforming AI. Through hands-on Python tutorials, you’ll master CNN architecture design, implementation, and optimization. The hierarchical structure of deep CNNs enables them to automatically learn features at various abstraction levels, excelling in image recognition, natural language processing, and more.
In this course, you’ll build Convolutional Neural Networks with Python from scratch, apply dataset augmentations to enhance image variability, and optimize hyperparameters before training your model. You’ll validate models on test images and explore performance metrics beyond simple calculations, gaining insights into your model’s effectiveness. Move on to advanced architectures, including ResNet and AlexNet for image classification, U-Net and PSPNet for semantic segmentation, and YOLO for real-time object detection.
Join us on this exciting journey to not only grasp core concepts but also unlock advanced CNN architectures. Equip yourself with the skills to address complex computer vision tasks with confidence. I will provide the complete Python code to build, train, test, and deploy CNNs for various AI applications. Don’t miss this incredible opportunity to elevate your skills—enroll now and join thousands of students who have transformed their careers with our courses. Thank you, and see you in class!
Interested in learning from the best in the industry? Join my courses and gain access to expert knowledge.
Our Promise to You
By the end of this course, you will have learned to build and optimize Convolutional Neural Networks with Python and PyTorch.
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.
Get started today!
Course Curriculum
| Section 1 - Introduction To Course | |||
| Introduction | 00:00:00 | ||
| Section 2 - Artificial Neurons - The Building Blocks Of Deep Learning | |||
| Introduction To Deep Learning And Artificial Neurons | 00:00:00 | ||
| Section 3 - Introduction To Convolutional Neural Networks (CNNs) | |||
| Introduction To Convolutional Neural Networks (CNNs) | 00:00:00 | ||
| Section 4 - Coding Convolutional Neural Networks From Scratch In Python | |||
| Google Colab Environment For Writing Python And Pytorch Code | 00:00:00 | ||
| Coding Convolutional Neural Network Architecture From Scratch Using Python | 00:00:00 | ||
| Section 5 - Dataset And Its Augmentation | |||
| Dataset And Its Augmentation | 00:00:00 | ||
| Section 6 - Hyperparameters Optimization For Convolutional Neural Networks | |||
| Hyperparameters Optimization For Training Models | 00:00:00 | ||
| Section 7 - Training Convolutional Neural Network From Scratch | |||
| Training Convolutional Neural Network From Scratch | 00:00:00 | ||
| Section 8 - Validating Convolutional Neural Network On Test Images | |||
| Validating Convolutional Neural Network On Test Images | 00:00:00 | ||
| Section 9 - Performance Metrics (Accuracy, Precision, Recall, F1 Score) To Evaluate CNNs | |||
| Performance Metrics (Accuracy, Precision, Recall, F1 Score) To Evaluate CNNs | 00:00:00 | ||
| Section 10 - Visualize Confusion Matrix And Calculate Precision, Recall, And F1 Score | |||
| Visualize Confusion Matrix And Calculate Precision, Recall, And F1 Score | 00:00:00 | ||
| Section 11 - Resources Python Code For Convolutional Neural Networks From Scratch | |||
| Resources: Python Code For Convolutional Neural Networks From Scratch | 00:00:00 | ||
| Section 12 - Pretrained Convolutional Neural Networks | |||
| Pretrained Convolutional Neural Networks With Python | 00:00:00 | ||
| Code Single Label Classification | 00:00:00 | ||
| Code Multi Label Classification | 00:00:00 | ||
| Section 13 - Transfer Learning using Convolutional Neural Networks | |||
| What is Transfer Learning | 00:00:00 | ||
| Transfer Learning By Fine Tuning CNNs Models | 00:00:00 | ||
| Transfer Learning with CNNs Models As Fixed Feature Extractor | 00:00:00 | ||
| Dataset | 00:00:00 | ||
| Code For Transfer Learning | 00:00:00 | ||
| Section 14 - Convolutional Neural Networks Encoder-Decoder Architectures | |||
| Convolutional Neural Networks Based Encoders | 00:00:00 | ||
| Convolutional Neural Networks Based Decoders | 00:00:00 | ||
| Multi-Task Contextual Encoder-Decoder Network | 00:00:00 | ||
| Resources: Encoder-Decoder For Semantic Segmentation With PyTorch Code | 00:00:00 | ||
| Section 15 - YOLO Convolutional Neural Networks | |||
| YOLO Convolutional Neural Networks Architecture | 00:00:00 | ||
| How YOLO Works To Identify Objects | 00:00:00 | ||
| Latest YOLOv8 Deep Convolutional Neural Network | 00:00:00 | ||
| YOLO Object Detection With Python | 00:00:00 | ||
| Section 16 - Region-Based Convolutional Neural Networks | |||
| Region-Based Convolutional Neural Networks (RCNN, FAST RCNN, FASTER RCNN) | 00:00:00 | ||
| Detectron2 For Object Detection With PyTorch | 00:00:00 | ||
| Perform Object Detection Using Detectron2 Models | 00:00:00 | ||
| Resources: Python And PyTorch Code For Object Detection | 00:00:00 | ||
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