Master and Deploy Image Classification solutions with Tensorflow using models like Convnets and Vision Transformers
Image classification models find themselves in different places today, like farms, hospitals, industries, schools, and highways,…
With the creation of much more efficient deep learning models from the early 2010s, we have seen a great improvement in the state of the art in the domain of image classification.
In this course, we shall take you on an amazing journey in which you’ll master different concepts with a step-by-step approach. We shall start by understanding how image classification algorithms work, and deploying them to the cloud while observing best practices. We are going to be using Tensorflow 2 (the world’s most popular library for deep learning, built by Google) and Huggingface
You will learn:
- The Basics of Tensorflow (Tensors, Model building, training, and evaluation)
- Deep Learning algorithms like Convolutional neural networks and Vision Transformers
- Evaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)
- Mitigating overfitting with Data augmentation
- Advanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, Tensorboard
- Machine Learning Operations (MLOps) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)
- Binary Classification with Malaria detection
- Multi-class Classification with Human Emotions Detection
- Transfer learning with modern Convnets (Vggnet, Resnet, Mobilenet, Efficientnet)
- Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)
If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!
This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.
