Course curriculum

  • 1

    Introduction

    • Introduction to Course
    • Introduction to Instructor
    • Introduction to Co-Instructor
    • Course Introduction
    • Links for the Course's Materials and Codes
  • 2

    Introduction(Regular Expressions)

    • what is regular expression
    • why regular expression
    • ELIZA chatbot
    • Python regular expression package
  • 3

    Meta Characters(Regular Expressions)

    • meta characters
    • meta characters_bigBrackets_exercise
    • meta characters_bigBrackets_exercise_solution
    • meta characters_bigBrackets_exercise_2
    • meta characters_bigBrackets_exercise_2_solution
    • meta characters_cap
    • meta characters_cap_exercise_3
    • meta characters_cap_exercise_3_solution
    • backSlash
    • backSlash_continued
    • backSlash_continued 01
    • backSlash_Squared_brackets_exercise
    • backSlash_Squared_brackets_exercise_solution
    • backSlash_Squared_brackets_exercise_another_solution
    • backSlash_exercise
    • backSlash_exercise_Solution_and_Special Sequences_exercise
    • Solution_and_Special Sequences_exercise_solution
    • Meta_character_esterics
    • Meta_character_esterics_exercise
    • Meta_character_esterics_exercise_solution
    • Meta_character_esterics_Homework
    • Meta_character_esterics_GreedyMatching
    • Meta_character_Plus_and_questionMark
    • Meta_character_curly_brackets_exercise
    • Meta_character_curly_brackets_exercise_solution
  • 4

    Pattern Objects(Regular Expressions)

    • pattern_objects
    • pattern_objects_match_method_exersize
    • pattern_objects_match_method_exersize_solution
    • pattern_objects_match_method_vs_search_method
    • pattern_objects_finditer_method
    • pattern_objects_finditer_method_exersize_solution
  • 5

    More Meta Characters(Regular Expressions)

    • meta_characters_logical_or
    • meta_characters_beginning_and_end_patterns
    • meta_characters_paranthesis
  • 6

    String Modification(Regular Expressions)

    • String_modification
    • Word_tokenizer_using_split_method
    • sub_method_exercise
    • sub_method_exercise_solution
  • 7

    Words and Tokens(Text Preprocessing)

    • what is a word
    • definition of word is task dependent
    • Vocabulary and Corpus
    • Tokens
    • Tokenization in spaCy
  • 8

    Sentiment Classification(Text Preprocessing)

    • Yelp_Reviews_classification_mini_project_introduction
    • Yelp_Reviews_classification_mini_project_Vocabulary_initialization
    • Yelp_Reviews_classification_mini_project_Adding_tokens_to_vocabulary
    • Yelp_Reviews_classification_mini_project_look_up_functions_in_vocabulary
    • Yelp_Reviews_classification_mini_project_building_vocabulary_from_data
    • Yelp_Reviews_classification_mini_project_one_hot_encoding
    • Yelp_Reviews_classification_mini_project_one_hot_encoding_implementation
    • Yelp_Reviews_classification_mini_project_Encoding_documents
    • Yelp_Reviews_classification_mini_project_Encoding_documents_implementation
    • Yelp_Reviews_classification_mini_project_train_test_splits
    • Yelp_Reviews_classification_mini_project_FeatureComputation
    • Yelp_Reviews_classification_mini_project_classification
  • 9

    Language Independent Tokenization(Text Preprocessing)

    • Tokenization_in_detial_introduction
    • Tokenization_is_hard
    • Tokenization_Byte_pair_encoding
    • Tokenization_Byte_pair_encoding_example
    • Tokenization_Byte_pair_encoding_on_test_data
    • Tokenization_Byte_pair_encoding_Implementation_getPairCounts
    • Tokenization_Byte_pair_encoding_Implementation_mergeInCorpus
    • Tokenization_Byte_pair_encoding_Implementation_BPE_training
    • Tokenization_Byte_pair_encoding_Implementation_BPE_encoding
    • Tokenization_Byte_pair_encoding_Implementation_BPE_encoding_one_pair
    • Tokenization_Byte_pair_encoding_Implementation_BPE_encoding_one_pair
  • 10

    Text Nomalization(Text Preprocessing)

    • Word_normalization_CaseFolding
    • Word_normalization_Lematization
    • Word_normalization_Stemming
    • Word_normalization_Sentence_segmentation
  • 11

    String Matching and Spelling Correction(Text Preprocessing)

    • Spelling_correction_Minimum_Edit_Distance_intro
    • Spelling_correction_Minimum_Edit_Distance_Example
    • Spelling_correction_Minimum_Edit_Distance_Table_filling
    • Spelling_correction_Minimum_Edit_Distance_Dynamic_programming
    • Spelling_correction_Minimum_Edit_Distance_psudoCode
    • Spelling_correction_Minimum_Edit_Distance_implementation
    • Spelling_correction_Minimum_Edit_Distance_implementation_bugFixing
    • Spelling_correction_implementation
  • 12

    Language Modeling

    • what is a language model
    • language model_formal_definition
    • language model_curse_of_dimensionality
    • language model_markov_assumption_and_n-grams
    • language model_implementation_setup
    • language model_implementation_ngrams_function
    • language model_implementation_updateCounts_function
    • language model_implementation_probabilityModel_funciton
    • language model_implementation_readingCorpus
    • language model_implementation_samplingText
  • 13

    Topic Modelling with Word and Document Representations

    • one hot vectors
    • one hot vectors_implementaton
    • one hot vectors_limitations
    • one hot vectors_uses_as_target_labeling
    • term_frequency_for_document_representations
    • term_frequency_for_document_representations_implementations
    • term_frequency_for_word_representations
    • TfIdf_for_document_representations
    • TfIdf_for_document_representations_implementation_reading_corpus
    • TfIdf_for_document_representations_implementation_computing_document_frequency
    • TfIdf_for_document_representations_implementation_computing_tfidf
    • TopicModeling_with_TFIDF_1
    • TopicModeling_with_TFIDF_3
    • TopicModeling_with_TFIDF_4
    • TopicModeling_with_TFIDF_5
    • Topic_modeling_with_Gensim
  • 14

    Word Embeddings LSI

    • wordCoocurrenceMatrix
    • wordCoocurrenceMatrix_vs_wordDocumentMatrix
    • wordCoocurrenceMatrix_implementation_preparing_data
    • wordCoocurrenceMatrix_implementation_preparing_data_2
    • wordCoocurrenceMatrix_implementation_preparing_data_gettingVocabulary
    • wordCoocurrenceMatrix_implementation_final_function
    • wordCoocurrenceMatrix_implementation_handling_memory_issues_on_large_corpus
    • wordCoocurrenceMatrix_sparsity
    • wordCoocurrenceMatrix_positive_point_wise_mutual_information_PPMI
    • PCA_for_dense_embeddings
    • Latent_Semantic_Analysis
    • Latent_Semantic_Analysis_implementation
  • 15

    Word Semantics

    • cosine_similarity
    • cosine_similarity_geting_norms_of_vectors
    • cosine_similarity_normalizing_vectors
    • cosine_similarity_with_more_than_one_vectors
    • cosine_similarity_getting_most_similar_words_in_the_vocabulary
    • cosine_similarity_getting_most_similar_words_in_the_vocabulary_fixingBug_of_dimensions
    • cosine_similarity_word2vec_embeddings
    • wordsAnalogies
    • wordsAnalogies_implemenation_1
    • wordsAnalogies_implemenation_2
    • wordsvisualizations
    • wordsvisualizations_implementaion
    • wordsvisualizations_implementaion_2
  • 16

    Word2vec(Optional)

    • static_and_dynamic_embeddings
    • self_supervision
    • word2vec_algorithm_abstract
    • word2vec_why_negative_sampling
    • word2vec_what_is_skip_gram
    • word2vec_how_to_define_probability_law
    • word2vec_sigmoid
    • word2vec_formalizing_loss_function
    • word2vec_loss_function
    • word2vec_gradient_descent_step
    • word2vec_implemenation_preparing_data
    • word2vec_implemenation_gradient_step
    • word2vec_implemenation_driver_function
  • 17

    Need of Deep Learning for NLP(NLP with Deep Learning DNN)

    • Why_RNNs_for_NLP
    • PyTorch_installation_and_Tensors_intro
    • automatic_diffrenciation_pytorch
  • 18

    Introduction(NLP with Deep Learning DNN)

    • Why_DNNs_in_machineLearning
    • Representational_power_and_data_utilization_capacity_of_DNN
    • perceptron
    • perceptron_implementation
    • DNN_architecture
    • DNN_forwardStep_implementation
    • DNN_why_activation_function_is_required
    • DNN_properties_of_activation_function
    • DNN_activation_functions_in_pytorch
    • DNN_what_is_loss_function
    • DNN_loss_function_in_pytorch
  • 19

    Training(NLP with Deep Learning DNN)

    • DNN_Gradient_descent
    • DNN_Gradient_descent_implementation
    • DNN_Gradient_descent_stochastic_batch_minibatch
    • DNN_Gradient_descent_summary
    • DNN_implemenation_gradient_step
    • DNN_implemenation_Stochastic_gradient_descent
    • DNN_implemenation_batch_gradient_descent
    • DNN_implemenation_minibatch_gradient_descent
    • DNN_implemenation_in_PyTorch
  • 20

    Hyper parameters(NLP with Deep Learning DNN)

    • DNN_weights_initializations
    • DNN_learning_rate
    • DNN_batch_normalization
    • DNN_batch_normalization_implementation
    • DNN_optimizations
    • DNN_dropout
    • DNN_dropout_in_pyTorch
    • DNN_early_stopping
    • DNN_hyperparameters
    • DNN_Pytorch_CIFAR10_example
  • 21

    Introduction(NLP with Deep Learning RNN)

    • what_is_rnn
    • understanding_rnn_with_a_simple_example
    • RNN_applications_Human_activity_recognition
    • RNN_applications_image_captioning
    • RNN_applications_machine_Translation
    • RNN_applications_speech_recognition_stock_price_prediction
    • RNN_models
  • 22

    Mini-project Language Modelling(NLP with Deep Learning RNN)

    • Language_modeling_Next_word_prediction
    • Language_modeling_Next_word_prediction_vocabulary_index
    • Language_modeling_Next_word_prediction_vocabulary_index_embeddings
    • Language_modeling_Next_word_prediction_RNN_architecture
    • Language_modeling_Next_word_prediction_Python_1
    • Language_modeling_Next_word_prediction_Python_2
    • Language_modeling_Next_word_prediction_Python_3
    • Language_modeling_Next_word_prediction_Python_4
    • Language_modeling_Next_word_prediction_Python_5
    • Language_modeling_Next_word_prediction_Python_6
  • 23

    Mini-project Sentiment Classification(NLP with Deep Learning RNN)

    • vocabulary_implementation
    • vocabulary_implementation_helpers
    • vocabulary_implementation_from_file
    • Vectorizer
    • RNN_setup
    • RNN_setup
  • 24

    RNN in PyTorch(NLP with Deep Learning RNN)

    • RNN_in_PyTorch_intro
    • RNN_in_PyTorch_Embedding_layer
    • RNN_in_PyTorch_nn_RNN
    • RNN_in_PyTorch_output_shapes
    • RNN_in_PyTorch_GatedUnits
    • RNN_in_PyTorch_GatedUnits_GRU_LSTM
    • RNN_in_PyTorch_bidirectional_RNN
    • RNN_in_PyTorch_bidirectional_RNN_output_shapes
    • RNN_in_PyTorch_bidirectional_RNN_output_shapes_seperation
    • RNN_in_PyTorch_example
  • 25

    Advanced RNN models(NLP with Deep Learning RNN)

    • RNN_Encoder_decoder
    • RNN_Attention
  • 26

    Neural Machine Translation

    • introduction_to_dataset_and_packages
    • implementing_language_class
    • Testing_language_class_and_implementing_normalization
    • reading_dataFile
    • reading_building_vocabulary
    • EncoderRNN
    • DecoderRNN
    • DecoderRNN_forward_step
    • DecoderRNN_helper_functions
    • trainingModule
    • Stochastic_Gradient_descent
    • NMT_training
    • NMT_evaluation