Python Machine Learning - Second Edition > PACKT 원서리스트

Table of Contents

1: GIVING COMPUTERS THE ABILITY TO LEARN FROM DATA
2: TRAINING SIMPLE MACHINE LEARNING ALGORITHMS FOR CLASSIFICATION
3: A TOUR OF MACHINE LEARNING CLASSIFIERS USING SCIKIT-LEARN
4: BUILDING GOOD TRAINING SETS – DATA PREPROCESSING
5: COMPRESSING DATA VIA DIMENSIONALITY REDUCTION
6: LEARNING BEST PRACTICES FOR MODEL EVALUATION AND HYPERPARAMETER TUNING
7: COMBINING DIFFERENT MODELS FOR ENSEMBLE LEARNING
8: APPLYING MACHINE LEARNING TO SENTIMENT ANALYSIS
9: EMBEDDING A MACHINE LEARNING MODEL INTO A WEB APPLICATION
10: PREDICTING CONTINUOUS TARGET VARIABLES WITH REGRESSION ANALYSIS
11: WORKING WITH UNLABELED DATA – CLUSTERING ANALYSIS
12: IMPLEMENTING A MULTILAYER ARTIFICIAL NEURAL NETWORK FROM SCRATCH
13: PARALLELIZING NEURAL NETWORK TRAINING WITH TENSORFLOW
14: GOING DEEPER – THE MECHANICS OF TENSORFLOW
15: CLASSIFYING IMAGES WITH DEEP CONVOLUTIONAL NEURAL NETWORKS
16: MODELING SEQUENTIAL DATA USING RECURRENT NEURAL NETWORKS