This repository contains my coursework and projects completed during the Deep Learning Specialization offered by DeepLearning.AI and Stanford Online.
The specialization is designed to provide a solid foundation in deep learning and equip learners with the skills to build real-world AI applications.
- Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications.
- Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow.
- Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning
- Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data.
- Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering.