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INTRODUCTION TO NLP & Deep Learning 4
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Understanding Textual DataLecture1.1
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What is Natural Language Processing?Lecture1.2
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Industry applications of NLPLecture1.3
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Machine Learning vs NLP vs Deep LearningLecture1.4
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PART A – Natural Language Processing 1
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Introduction to NLP 4
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Areas of applicationLecture3.1
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Understanding textLecture3.2
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Text encodingLecture3.3
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Regular ExpressionsLecture3.4
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Basic Text Pre-Processing 5
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Bag of Words.Lecture4.1
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TokenizationLecture4.2
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StemmingLecture4.3
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LemmatizationLecture4.4
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Count vectorizer vs TF-IDF vectorizerLecture4.5
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Advanced Lexical Processing 5
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Phonetic HashingLecture5.1
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Distance calculationLecture5.2
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Levenshtein DistanceLecture5.3
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SoundexLecture5.4
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Mutual InformationLecture5.5
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Introduction to syntactic processing 6
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What and why of syntactic processingLecture6.1
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ParsingLecture6.2
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Parts-of-Speech taggingLecture6.3
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Different approaches of POS taggingLecture6.4
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Hidden Markov ModelsLecture6.5
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Stochastic ParsingLecture6.6
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Parsing 5
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Limitations of shallow parsingLecture7.1
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Constituency Free Demo GrammarsLecture7.2
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Top Down parsingLecture7.3
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Bottom up parsingLecture7.4
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Dependency ParsingLecture7.5
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Information Extraction 6
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Introduction to IELecture8.1
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Rule based modelsLecture8.2
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Named Entity RecognitionLecture8.3
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Shallow algorithm for NERLecture8.4
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Probabilistic models for NERLecture8.5
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Conditional Random FieldsLecture8.6
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Semantic Processing 5
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Entities and TypesLecture9.1
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Schema and knowledge graphsLecture9.2
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AssociationsLecture9.3
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WordNet and Concept NetLecture9.4
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Lesk AlgorithmLecture9.5
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Advanced Semantics 8
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Distributional SemanticsLecture10.1
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Occurrence & Co-occurrence matrixLecture10.2
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Word EmbeddingsLecture10.3
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LSA & PLSALecture10.4
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Skip gramsLecture10.5
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Word2Vec and GloveLecture10.6
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Topic ModellingLecture10.7
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Latent Dirichlet AllocationLecture10.8
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PART B – DEEP LEARNING 1
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Introduction to Deep Learning 5
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Introduction to Deep LearningLecture12.1
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Machine Learning vs Deep LearningLecture12.2
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What is unstructured data?Lecture12.3
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Industry applications of Deep LearningLecture12.4
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Prerequisites for being Deep Learning expertLecture12.5
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Artificial Neural Networks 5
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How human brain works?Lecture13.1
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Inspiration to Artificial Neural NetworksLecture13.2
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Introduction to PerceptronLecture13.3
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Binary & Multi classification using PerceptronLecture13.4
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How artificial neuron works?Lecture13.5
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Structure of Artificial Neural Networks 5
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Different layers in Neural NetworksLecture14.1
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Parameters and Hyper parameters in ANN’sLecture14.2
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Activation FunctionsLecture14.3
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Assumptions of ANN’sLecture14.4
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Representation of Text, Images, Videos in ANN’sLecture14.5
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Feed Forward & Backpropagation 6
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Weights and Bias calculationLecture15.1
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Information flow between layersLecture15.2
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Feedforward algorithmLecture15.3
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Calculating Loss functionLecture15.4
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Updating weights and biasLecture15.5
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Chain rule of back propagationLecture15.6
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Tuning the Neural Networks 7
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OptimizersLecture16.1
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RegularizationLecture16.2
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DropoutsLecture16.3
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Batch NormalizationLecture16.4
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Gradient DescentLecture16.5
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Number of layersLecture16.6
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Time taken to train a neural networkLecture16.7
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Convolutional Neural Networks 9
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Introduction to CNNLecture17.1
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Reading image dataLecture17.2
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Data augmentationLecture17.3
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What is convolution?Lecture17.4
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FiltersLecture17.5
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Striding and PaddingLecture17.6
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Pooling layersLecture17.7
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Activation functionsLecture17.8
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Feature extractionLecture17.9
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CNN Architectures 8
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Overview of different architectures in CNNLecture18.1
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VGG NetLecture18.2
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ResNetLecture18.3
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Inception and GoogleNetLecture18.4
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Transfer LearningLecture18.5
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Use cases of Transfer learningLecture18.6
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Object detectionLecture18.7
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Case Study – Classify images using Transfer LearningLecture18.8
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Recurrent Neural Networks 7
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Introduction to sequence modelsLecture19.1
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Architecture of Recurrent networksLecture19.2
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Types of RNN’sLecture19.3
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Feedforward approach in RNNLecture19.4
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Backpropagation Through TimeLecture19.5
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Vanishing & Exploding gradients in RNN’sLecture19.6
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Word EmbeddingsLecture19.7
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RNN architectures 4
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Bidirectional RNN’sLecture20.1
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Gating mechanismLecture20.2
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Long Short-Term Memory (LSTM)Lecture20.3
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Gated Recurrent Units (GRU)Lecture20.4
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GAN & Auto Encoders 6
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Introduction to Generative Adverbials NetworksLecture21.1
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How GAN’s work?Lecture21.2
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Image generation using GAN’sLecture21.3
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Industry use cases on GAN’sLecture21.4
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Autoencoders (standard, denoising, contractive)Lecture21.5
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Variational Auto EncodersLecture21.6
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Reinforcement Learning 7
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Introduction to RLLecture22.1
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Agent-EnvironmentLecture22.2
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Actions & PoliciesLecture22.3
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Exploration and ExploitationLecture22.4
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State Value Function & Action Value functionLecture22.5
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Policy iteration & ImprovementsLecture22.6
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Monte Carlo & Q-Learning methodsLecture22.7
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