PART 1: WORDY MACHINES (VECTOR MODELS OF NATURAL LANGUAGE)
PART 2: DEEPER LEARNING (NEURAL NETWORKS)
5 BABY STEPS WITH NEURAL NETWORKS (PERCEPTRONS AND BACKPROPAGATION)
6 REASONING WITH WORD VECTORS (WORD2VEC)
7 GETTING WORDS IN ORDER WITH CONVOLUTIONAL NEURAL NETWORKS (CNNS)
8 LOOPY (RECURRENT) NEURAL NETWORKS (RNNS)
9 IMPROVING RETENTION WITH LONG SHORT-TERM MEMORY NETWORKS (LSTMS)
10 SEQUENCE TO SEQUENCE MODELS AND ATTENTION (GENERATIVE MODELS)
PART 3: GETTING REAL (REAL WORLD NLP CHALLENGES)
11 INFORMATION EXTRACTION (NAMED ENTITY EXTRACTION AND QUESTION ANSWERING)
12 GETTING CHATTY (DIALOG ENGINES)
13 SCALING UP (OPTIMIZATION, PARALLELIZATION AND BATCH POCESSING)
APPENDICES
APPENDIX B: PLAYFUL PYTHON AND REGULAR EXPRESSIONS
APPENDIX C: VECTORS AND MATRICES (BASIC LINEAR ALGEBRA)
APPENDIX D: MACHINE LEARNING
APPENDIX E: AWS GPU
APPENDIX F: LOCALITY SENSITIVE HASHING
