Title & Categories to be updated 1672369886.2455967271

After finishing this course you will know: - How to train models that achieve state-of-the-art results in: Computer vision, including image classification (e.g., classifying pet photos by breed), and image localization and detection (e.g., finding where the animals in an image are) - Natural language processing (NLP), including document classification (e.g., movie review sentiment analysis) and language modeling - Tabular data (e.g., sales prediction) with categorical data, continuous data, and mixed data, including time series - Collaborative filtering (e.g., movie recommendation) - How to turn your models into web applications, and deploy them - Why and how deep learning models work, and how to use that knowledge to improve the accuracy, speed, and reliability of your models - The latest deep learning techniques that really matter in practice - How to implement stochastic gradient descent and a complete training loop from scratch - How to think about the ethical implications of your work, to help ensure that you're making the world a better place and that your work isn't misused for harm Here are some of the techniques covered (don't worry if none of these words mean anything to you yet--you'll learn them all soon): - Random forests and gradient boosting - Affine functions and nonlinearities - Parameters and activations - Random initialization and transfer learning - SGD, Momentum, Adam, and other optimizers - Convolutions - Batch normalization - Dropout - Data augmentation - Weight decay - Image classification and regression - Entity and word embeddings - Recurrent neural networks (RNNs) - Segmentation and much more https://course.fast.ai/