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Are you interested in the field of Computer Vision and the impact it has on various industries? The demand for experts in this field is skyrocketing, and proficiency in Deep Learning is a must-have skill. One of the best courses to learn and master the concepts of Deep Learning for Computer Vision is Master Deep Learning for Computer Vision in TensorFlow[2023].
Course Description
This course is designed for both beginners and experienced professionals and provides a step-by-step and project-based approach to learning Deep Learning using TensorFlow 2 and Huggingface. The course starts with building simple models such as linear regression for car price prediction and binary classifiers for malaria prediction using TensorFlow and proceeds to advanced models such as object detection models with YOLO and image generation models with GANs. By the end of the course, you will have the skill sets needed to develop modern deep learning solutions for Computer Vision, commonly encountered in big tech companies.
Why is Deep Learning based Computer Vision so popular?
Deep Learning has had a profound impact on various industries, and one of the fields where it has shown significant influence is Computer Vision. Giving computers the power to understand their surroundings via a camera has revolutionized the following industries:
- Medical diagnostics: Assisting doctors in carrying out medical diagnostics more efficiently.
- Agriculture: Enabling farmers to harvest their products with robots, with less human intervention.
- Self-driving cars: Developing cameras that have an eye and a brain, resulting in autonomous vehicles.
- Surveillance: Implementing smart CCTV systems for quick response surveillance.
- Art creation: Creating art with GANs, VAEs, and Diffusion Models.
- Sports analytics: Monitoring players’ movements automatically using sophisticated computer vision algorithms.
What will you learn?
The course covers various topics in Deep Learning, such as:
1. The Basics of TensorFlow:
- Tensors
- Model building, training, and evaluation
2. Deep Learning algorithms like Convolutional neural networks and Vision Transformers
3. Evaluation of Classification Models:
- Precision
- Recall
- Accuracy
- F1-score
- Confusion Matrix
- ROC Curve
4. Mitigating overfitting with Data augmentation
5. Advanced Tensorflow concepts:
- Custom Losses and Metrics
- Eager and Graph Modes and Custom Training Loops
- Tensorboard
6. Machine Learning Operations (MLOps) with Weights and Biases:
- Experiment Tracking
- Hyperparameter Tuning
- Dataset Versioning
- Model Versioning
7. Binary Classification
- Malaria detection
8. Multi-class Classification
- Human Emotions Detection
9. Transfer learning with modern Convnets and Vision Transformers:
- Vggnet
- Resnet
- Mobilenet
- Efficientnet
- VITs
10. Object Detection with YOLO (You Only Look Once)
11. Image Segmentation with UNet
12. People Counting with Csrnet
13. Model Deployment:
- Distillation
- Onnx format
- Quantization
- Fastapi
- Heroku Cloud
14. Digit generation with Variational Autoencoders
15. Face generation with Generative Adversarial Neural Networks
This course is offered by Neuralearn, and they give high importance to feedback. Your reviews and questions in the forum will help them improve the course.
Course Rating and Reviews
With 59 reviews, Master Deep Learning for Computer Vision in TensorFlow[2023] has an impressive course rating aggregate of 4.62361. Students have praised the step-by-step and project-based approach to learning. One reviewer mentioned - "The course is truly a masterclass in Deep Learning for Computer Vision, and the projects are challenging and fun to work on." Another reviewer stated - "I had no prior experience in Deep Learning, and this course has been a fantastic learning experience."
If you are keen to learn and master the art of Deep Learning for Computer Vision, then Master Deep Learning for Computer Vision in TensorFlow[2023] is the course for you. The course comprises of various projects that will give you a hands-on experience and imbibe the right skill sets to take on any real-world challenge.
