Share Description
In this course, you will :
- Learn all facets of PyTorch, from simple models to cutting-edge models.
- CNNs (Image-, Audio-, and Object Detection), RNNs, Transformers, Style Transfer, Autoencoders, GANs, and Recommenders are all examples of Natural Language Processing (NLP).
- Top-tier algorithms, such as Transformers, can be adapted to specific datasets.
- CNN models for picture categorization, object identification, and Style Transfer are being developed.
- RNN models, Autoencoders, and Generative Adversarial Networks are all being developed.
- Learn about new frameworks (such as PyTorch Lightning) and models (such as OpenAI ChatGPT).
Syllabus :
- Machine Learning
- Deep Learning Introduction
- Model Evaluation
- Neural Network from Scratch (opt. but highly recommended)
- Tensors
- PyTorch Modeling Introduction
- Classification Models
- CNN: Image Classification
- CNN: Audio Classification
- CNN: Object Detection
- Style Transfer
- Pretrained Networks and Transfer Learning
- Recurrent Neural Networks
- Recommender Systems
- Autoencoders
- Generative Adversarial Networks
- Graph Neural Networks
- Transformers
- PyTorch Lightning
- Semi-Supervised Learning
- Natural Language Processing (NLP)
- Miscellanious Topics
- Model Debugging
- Model Deployment
