Deep Learning Specialization by Andrew Ng et al.
https://www.coursera.org/specializations/deep-learning#courses
By the end you’ll be able to
• 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
ニューラルネットワークとディープラーニング
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Structuring Machine Learning Projects
Convolutional Neural Networks
Sequence model