Because learning Python by Chainer seems to be a paragraph I will review the plan
Learn the specialized books introduced below and output applications using machine learning.
I will do my best! !! !!
Study with this book
** Implementation of neural network (TensorFlow or PyTorch) **
Typical algorithm for supervised learning (As a result of visiting a bookstore, it seems necessary to study specialized books on "machine learning")
Multiple regression analysis, ridge regression, lasso regression, logistic regression, k-nearest neighbor method, support vector machine, decision tree, random forest, typical algorithm of unsupervised learning, k-means clustering, principal component analysis, typical hyperparameters Adjustment method, grid search, random search, Bayesian optimization, typical evaluation index of classification, correct answer rate, precision rate, recall rate, F value </ b>
The following seems to be good if you study appropriately according to the program you want to make, so the order of learning is likely to be in the second half instead of now
Image data, convolutional neural network (CNN), object detection algorithm (R-CNN, YOLO, SSD, etc.), semantic segmentation algorithm, text data, text data feature extraction method (Bag of words, Word2Vec, etc.) , Machine translation algorithms (Seq2Seq, Attention, etc.)
Time-series data (1/1 number of visitors is data that has a context in the data of 100 people)
Recurrent neural networks (RNN, LSTM, GRU, etc.)
Convolutional Neural Network (CNN)
Table data (data as described in an Excel sheet)
Feature engineering
Evolving machine learning algorithms (XGBoost, LightGBM, etc.) </ B>
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