Course curriculum

  • 1

    Deep Learning:Deep Neural Network for Beginners Using Python

    • Introduction: Introduction to Instructor
    • Introduction: Introduction to Course
    • Basics of Deep Learning: Problem to Solve Part 1
    • Basics of Deep Learning: Problem to Solve Part 2
    • Basics of Deep Learning: Problem to Solve Part 3
    • Basics of Deep Learning: Linear Equation
    • Basics of Deep Learning: Linear Equation Vectorized
    • Basics of Deep Learning: 3D Feature Space
    • Basics of Deep Learning: N Dimensional Space
    • Basics of Deep Learning: Theory of Perceptron
    • Basics of Deep Learning: Implementing Basic Perceptron
    • Basics of Deep Learning: Logical Gates for Perceptrons
    • Basics of Deep Learning: Perceptron Training Part 1
    • Basics of Deep Learning: Perceptron Training Part 2
    • Basics of Deep Learning: Learning Rate
    • Basics of Deep Learning: Perceptron Training Part 3
    • Basics of Deep Learning: Perceptron Algorithm
    • Basics of Deep Learning: Coading Perceptron Algo (Data Reading & Visualization)
    • Basics of Deep Learning: Coading Perceptron Algo (Perceptron Step)
    • Basics of Deep Learning: Coading Perceptron Algo (Training Perceptron)
    • Basics of Deep Learning: Coading Perceptron Algo (Visualizing the Results)
    • Basics of Deep Learning: Problem with Linear Solutions
    • Basics of Deep Learning: Solution to Problem
    • Basics of Deep Learning: Error Functions
    • Basics of Deep Learning: Discrete vs Continuous Error Function
    • Basics of Deep Learning: Sigmoid Function
    • Basics of Deep Learning: Multi-Class Problem
    • Basics of Deep Learning: Problem of Negative Scores
    • Basics of Deep Learning: Need of Softmax
    • Basics of Deep Learning: Coding Softmax
    • Basics of Deep Learning: One Hot Encoding
    • Basics of Deep Learning: Maximum Likelihood Part 1
    • Basics of Deep Learning: Maximum Likelihood Part 2
    • Basics of Deep Learning: Cross Entropy
    • Basics of Deep Learning: Cross Entropy Formulation
    • Basics of Deep Learning: Multi Class Cross Entropy
    • Basics of Deep Learning: Cross Entropy Implementation
    • Basics of Deep Learning: Sigmoid Function Implementation
    • Basics of Deep Learning: Output Function Implementation
    • Deep Learning: Introduction to Gradient Decent
    • Deep Learning: Convex Functions
    • Deep Learning: Use of Derivatives
    • Deep Learning: How Gradient Decent Works
    • Deep Learning: Gradient Step
    • Deep Learning: Logistic Regression Algorithm
    • Deep Learning: Data Visualization and Reading
    • Deep Learning: Updating Weights in Python
    • Deep Learning: Implementing Logistic Regression
    • Deep Learning: Visualization and Results
    • Deep Learning: Gradient Decent vs Perceptron
    • Deep Learning: Linear to Non Linear Boundaries
    • Deep Learning: Combining Probabilities
    • Deep Learning: Weighted Sums
    • Deep Learning: Neural Network Architecture
    • Deep Learning: Layers and DEEP Networks
    • Deep Learning:Multi Class Classification
    • Deep Learning: Basics of Feed Forward
    • Deep Learning: Feed Forward for DEEP Net
    • Deep Learning: Deep Learning Algo Overview
    • Deep Learning: Basics of Back Propagation
    • Deep Learning: Updating Weights
    • Deep Learning: Chain Rule for BackPropagation
    • Deep Learning: Sigma Prime
    • Deep Learning: Data Analysis NN Implementation
    • Deep Learning: One Hot Encoding (NN Implementation)
    • Deep Learning: Scaling the Data (NN Implementation)
    • Deep Learning: Splitting the Data (NN Implementation)
    • Deep Learning: Helper Functions (NN Implementation)
    • Deep Learning: Training (NN Implementation)
    • Deep Learning: Testing (NN Implementation)
    • Optimizations: Underfitting vs Overfitting
    • Optimizations: Early Stopping
    • Optimizations: Quiz
    • Optimizations: Solution & Regularization
    • Optimizations: L1 & L2 Regularization
    • Optimizations: Dropout
    • Optimizations: Local Minima Problem
    • Optimizations: Random Restart Solution
    • Optimizations: Vanishing Gradient Problem
    • Optimizations: Other Activation Functions
    • Final Project: Final Project Part 1
    • Final Project: Final Project Part 2
    • Final Project: Final Project Part 3
    • Final Project: Final Project Part 4
    • Final Project: Final Project Part 5
  • 2

    Deep Learning CNN: Convolutional Neural Networks with Python

    • Introduction: Instructor Introduction
    • Introduction: Why CNN
    • Introduction: Focus of the Course
    • Image Processing: Gray Scale Images
    • Image Processing: Gray Scale Images Quiz
    • Image Processing: Gray Scale Images Solution
    • Image Processing: RGB Images
    • Image Processing: RGB Images Quiz
    • Image Processing: RGB Images Solution
    • Image Processing: Reading and Showing Images in Python
    • Image Processing: Reading and Showing Images in Python Quiz
    • Image Processing: Reading and Showing Images in Python Solution
    • Image Processing: Converting an Image to Grayscale in Python
    • Image Processing: Converting an Image to Grayscale in Python Quiz
    • Image Processing: Converting an Image to Grayscale in Python Solution
    • Image Processing: Image Formation
    • Image Processing: Image Formation Quiz
    • Image Processing: Image Formation Solution
    • Image Processing: Image Blurring 1
    • Image Processing: Image Blurring 1 Quiz
    • Image Processing: Image Blurring 1 Solution
    • Image Processing: Image Blurring 2
    • Image Processing: Image Blurring 2 Quiz
    • Image Processing: Image Blurring 2 Solution
    • Image Processing: General Image Filtering
    • Image Processing: Convolution
    • Image Processing: Edge Detection
    • Image Processing: Image Sharpening
    • Image Processing: Implementation of Image Blurring Edge Detection Image Sharpening in Python
    • Image Processing: Parameteric Shape Detection
    • Image Processing: Image Processing Activity
    • Image Processing: Image Processing Activity Solution
    • Object Detection: Introduction to Object Detection
    • Object Detection: Classification PipleLine
    • Object Detection: Classification PipleLine Quiz
    • Object Detection: Classification PipleLine Solution
    • Object Detection: Sliding Window Implementation
    • Object Detection: Shift Scale Rotation Invariance
    • Object Detection: Shift Scale Rotation Invariance Exercise
    • Object Detection: Person Detection
    • Object Detection: HOG Features
    • Object Detection: HOG Features Exercise
    • Object Detection: Hand Engineering vs CNNs
    • Object Detection: Object Detection Activity
    • Deep Neural Network Overview: Neuron and Perceptron
    • Deep Neural Network Overview: DNN Architecture
    • Deep Neural Network Overview: DNN Architecture Quiz
    • Deep Neural Network Overview: DNN Architecture Solution
    • Deep Neural Network Overview: FeedForward FullyConnected MLP
    • Deep Neural Network Overview: Calculating Number of Weights of DNN
    • Deep Neural Network Overview: Calculating Number of Weights of DNN Quiz
    • Deep Neural Network Overview: Calculating Number of Weights of DNN Solution
    • Deep Neural Network Overview: Number of Nuerons vs Number of Layers
    • Deep Neural Network Overview: Discriminative vs Generative Learning
    • Deep Neural Network Overview: Universal Approximation Therorem
    • Deep Neural Network Overview: Why Depth
    • Deep Neural Network Overview: Decision Boundary in DNN
    • Deep Neural Network Overview: Decision Boundary in DNN Quiz
    • Deep Neural Network Overview: Decision Boundary in DNN Solution
    • Deep Neural Network Overview: BiasTerm
    • Deep Neural Network Overview: BiasTerm Quiz
    • Deep Neural Network Overview: BiasTerm Solution
    • Deep Neural Network Overview: Activation Function
    • Deep Neural Network Overview: Activation Function Quiz
    • Deep Neural Network Overview: Activation Function Solution
    • Deep Neural Network Overview: DNN Training Parameters
    • Deep Neural Network Overview: DNN Training Parameters Quiz
    • Deep Neural Network Overview: DNN Training Parameters Solution
    • Deep Neural Network Overview: Gradient Descent
    • Deep Neural Network Overview: BackPropagation
    • Deep Neural Network Overview: Training DNN Animantion
    • Deep Neural Network Overview: Weigth Initialization
    • Deep Neural Network Overview: Weigth Initialization Quiz
    • Deep Neural Network Overview: Weigth Initialization Solution
    • Deep Neural Network Overview: Batch miniBatch Stocastic Gradient Descent
    • Deep Neural Network Overview: Batch Normalization
    • Deep Neural Network Overview: Rprop and Momentum
    • Deep Neural Network Overview: Rprop and Momentum Quiz
    • Deep Neural Network Overview: Rprop and Momentum Solution
    • Deep Neural Network Overview: Convergence Animation
    • Deep Neural Network Overview: DropOut, Early Stopping and Hyperparameters
    • Deep Neural Network Overview: DropOut, Early Stopping and Hyperparameters Quiz
    • Deep Neural Network Overview: DropOut, Early Stopping and Hyperparameters Solution
    • Deep Neural Network Architecture: Convolution Revisited
    • Deep Neural Network Architecture: Implementing Convolution in Python Revisited
    • Deep Neural Network Architecture: Why Convolution
    • Deep Neural Network Architecture: Filters Padding Strides
    • Deep Neural Network Architecture: Padding Image
    • Deep Neural Network Architecture: Pooling Tensors
    • Deep Neural Network Architecture: CNN Example
    • Deep Neural Network Architecture: Convolution and Pooling Details
    • Deep Neural Network Architecture: Maxpooling Exercise
    • Deep Neural Network Architecture: NonVectorized Implementations of Conv2d and Pool2d
    • Deep Neural Network Architecture: Deep Neural Network Architecture Activity
    • Gradient Descent in CNNs: Example Setup
    • Gradient Descent in CNNs: Why Derivaties
    • Gradient Descent in CNNs: Why Derivaties Quiz
    • Gradient Descent in CNNs: Why Derivaties Solution
    • Gradient Descent in CNNs: What is Chain Rule
    • Gradient Descent in CNNs: Applying Chain Rule
    • Gradient Descent in CNNs: Gradients of MaxPooling Layer
    • Gradient Descent in CNNs: Gradients of MaxPooling Layer Quiz
    • Gradient Descent in CNNs: Gradients of MaxPooling Layer Solution
    • Gradient Descent in CNNs: Gradients of Convolutional Layer
    • Gradient Descent in CNNs: Extending To Multiple Filters
    • Gradient Descent in CNNs: Extending to Multiple Layers
    • Gradient Descent in CNNs: Extending to Multiple Layers Quiz
    • Gradient Descent in CNNs: Extending to Multiple Layers Solution
    • Gradient Descent in CNNs: Implementation in Numpy ForwardPass
    • Gradient Descent in CNNs: Implementation in Numpy BackwardPass 1
    • Gradient Descent in CNNs: Implementation in Numpy BackwardPass 2
    • Gradient Descent in CNNs: Implementation in Numpy BackwardPass 3
    • Gradient Descent in CNNs: Implementation in Numpy BackwardPass 4
    • Gradient Descent in CNNs: Implementation in Numpy BackwardPass 5
    • Gradient Descent in CNNs: Gradient Descent in CNNs Activity
    • Introduction to TensorFlow: Introduction
    • Introduction to TensorFlow: FashionMNIST Example Plan Neural Network
    • Introduction to TensorFlow: FashionMNIST Example CNN
    • Introduction to TensorFlow: Introduction to TensorFlow Activity
    • Classical CNNs: LeNet
    • Classical CNNs: LeNet Quiz
    • Classical CNNs: LeNet Solution
    • Classical CNNs: AlexNet
    • Classical CNNs: VGG
    • Classical CNNs: InceptionNet
    • Classical CNNs: GoogLeNet
    • Classical CNNs: Classical CNNs Activity
    • Transfer Learning: What is Transfer learning
    • Transfer Learning: Why Transfer Learning
    • Transfer Learning: ImageNet Challenge
    • Transfer Learning: Practical Tips
    • Transfer Learning: Project in TensorFlow
    • Transfer Learning: Transfer Learning Activity
    • Yolo: Image Classfication Revisited
    • Yolo: Sliding Window Object Localization
    • Yolo: Sliding Window Efficient Implementation
    • Yolo: Yolo Introduction
    • Yolo: Yolo Training Data Generation
    • Yolo: Yolo Anchor Boxes
    • Yolo: Yolo Algorithm
    • Yolo: Yolo Non Maxima Supression
    • Yolo: RCNN
    • Yolo: Yolo Activity
    • Face Verification: Problem Setup
    • Face Verification: Project Implementation
    • Face Verification: Face Verification Activity
    • Neural Style Transfer: Problem Setup
    • Neural Style Transfer: Implementation Tensorflow Hub
  • 3

    Deep Learning: Recurrent Neural Networks with Python

    • Introduction: Introduction to Instructor and Aisciences
    • Introduction: Introduction To Instructor
    • Introduction: Focus of the Course
    • Applications of RNN (Motivation): Human Activity Recognition
    • Applications of RNN (Motivation): Image Captioning
    • Applications of RNN (Motivation): Machine Translation
    • Applications of RNN (Motivation): Speech Recognition
    • Applications of RNN (Motivation): Stock Price Predictions
    • Applications of RNN (Motivation): When to Model RNN
    • Applications of RNN (Motivation): Activity
    • DNN Overview: Why PyTorch
    • DNN Overview: PyTorch Installation and Tensors Introduction
    • DNN Overview: Automatic Diffrenciation Pytorch New
    • DNN Overview: Why DNNs in Machine Learning
    • DNN Overview: Representational Power and Data Utilization Capacity of DNN
    • DNN Overview: Perceptron
    • DNN Overview: Perceptron Exercise
    • DNN Overview: Perceptron Exercise Solution
    • DNN Overview: Perceptron Implementation
    • DNN Overview: DNN Architecture
    • DNN Overview: DNN Architecture Exercise
    • DNN Overview: DNN Architecture Exercise Solution
    • DNN Overview: DNN ForwardStep Implementation
    • DNN Overview: DNN Why Activation Function Is Required
    • DNN Overview: DNN Why Activation Function Is Required Exercise
    • DNN Overview: DNN Why Activation Function Is Required Exercise Solution
    • DNN Overview: DNN Properties Of Activation Function
    • DNN Overview: DNN Activation Functions In Pytorch
    • DNN Overview: DNN What Is Loss Function
    • DNN Overview: DNN What Is Loss Function Exercise
    • DNN Overview: DNN What Is Loss Function Exercise Solution
    • DNN Overview: DNN What Is Loss Function Exercise 02
    • DNN Overview: DNN What Is Loss Function Exercise 02 Solution
    • DNN Overview: DNN Loss Function In Pytorch
    • DNN Overview: DNN Gradient Descent
    • DNN Overview: DNN Gradient Descent Exercise
    • DNN Overview: DNN Gradient Descent Exercise Solution
    • DNN Overview: DNN Gradient Descent Implementation
    • DNN Overview: DNN Gradient Descent Stochastic Batch Minibatch
    • DNN Overview: DNN Gradient Descent Summary
    • DNN Overview: DNN Implemenation Gradient Step
    • DNN Overview: DNN Implemenation Stochastic Gradient Descent
    • DNN Overview: DNN Implemenation Batch Gradient Descent
    • DNN Overview: DNN Implemenation Minibatch Gradient Descent
    • DNN Overview: DNN Implemenation In PyTorch
    • DNN Overview: DNN Weights Initializations
    • DNN Overview: DNN Learning Rate
    • DNN Overview: DNN Batch Normalization
    • DNN Overview: DNN batch Normalization Implementation
    • DNN Overview: DNN Optimizations
    • DNN Overview: DNN Dropout
    • DNN Overview: DNN Dropout In PyTorch
    • DNN Overview: DNN Early Stopping
    • DNN Overview: DNN Hyperparameters
    • DNN Overview: DNN Pytorch CIFAR10 Example
    • RNN Architecture: Introduction to Module
    • RNN Architecture: Fixed Length Memory Model
    • RNN Architecture: Fixed Length Memory Model Exercise
    • RNN Architecture: Fixed Length Memory Model Exercise Solution Part 01
    • RNN Architecture: Fixed Length Memory Model Exercise Solution Part 02
    • RNN Architecture: Infinite Memory Architecture
    • RNN Architecture: Infinite Memory Architecture Exercise
    • RNN Architecture: Infinite Memory Architecture Solution
    • RNN Architecture: Weight Sharing
    • RNN Architecture: Notations
    • RNN Architecture: ManyToMany Model
    • RNN Architecture: ManyToMany Model Exercise 01
    • RNN Architecture: ManyToMany Model Solution 01
    • RNN Architecture: ManyToMany Model Exercise 02
    • RNN Architecture: ManyToMany Model Solution 02
    • RNN Architecture: ManyToOne Model
    • RNN Architecture: OneToMany Model Exercise
    • RNN Architecture: OneToMany Model Solution
    • RNN Architecture: OneToMany Model
    • RNN Architecture: ManyToOne Model Exercise
    • RNN Architecture: ManyToOne Model Solution
    • RNN Architecture: Activity Many to One
    • RNN Architecture: Activity Many to One Exercise
    • RNN Architecture: Activity Many to One Solution
    • RNN Architecture: ManyToMany Different Sizes Model
    • RNN Architecture: Activity Many to Many Nmt
    • RNN Architecture: Models Summary
    • RNN Architecture: Deep RNNs
    • RNN Architecture: Deep RNNs Exercise
    • RNN Architecture: Deep RNNs Solution
    • Gradient Decsent in RNN: Introduction to Gradient Descent Module
    • Gradient Decsent in RNN: Example Setup
    • Gradient Decsent in RNN: Equations
    • Gradient Decsent in RNN: Equations Exercise
    • Gradient Decsent in RNN: Equations Solution
    • Gradient Decsent in RNN: Loss Function
    • Gradient Decsent in RNN: Why Gradients
    • Gradient Decsent in RNN: Why Gradients Exercise
    • Gradient Decsent in RNN: Why Gradients Solution
    • Gradient Decsent in RNN: Chain Rule
    • Gradient Decsent in RNN: Chain Rule in Action
    • Gradient Decsent in RNN: BackPropagation Through Time
    • Gradient Decsent in RNN: Activity
    • RNN implementation: Automatic Diffrenciation
    • RNN implementation: Automatic Diffrenciation Pytorch
    • RNN implementation: Language Modeling Next Word Prediction Vocabulary Index
    • RNN implementation: Language Modeling Next Word Prediction Vocabulary Index Embeddings
    • RNN implementation: Language Modeling Next Word Prediction RNN Architecture
    • RNN implementation: Language Modeling Next Word Prediction Python 1
    • RNN implementation: Language Modeling Next Word Prediction Python 2
    • RNN implementation: Language Modeling Next Word Prediction Python 3
    • RNN implementation: Language Modeling Next Word Prediction Python 4
    • RNN implementation: Language Modeling Next Word Prediction Python 5
    • RNN implementation: Language Modeling Next Word Prediction Python 6
    • Sentiment Classification using RNN: Vocabulary Implementation
    • Sentiment Classification using RNN: Vocabulary Implementation Helpers
    • Sentiment Classification using RNN: Vocabulary Implementation From File
    • Sentiment Classification using RNN: Vectorizer
    • Sentiment Classification using RNN: RNN Setup 1
    • Sentiment Classification using RNN: RNN Setup
    • Sentiment Classification using RNN: WhatNext
    • Vanishing Gradients in RNN: Introduction to Better RNNs Module
    • Vanishing Gradients in RNN: Introduction Vanishing Gradients in RNN
    • Vanishing Gradients in RNN: GRU
    • Vanishing Gradients in RNN: GRU Optional
    • Vanishing Gradients in RNN: LSTM
    • Vanishing Gradients in RNN: LSTM Optional
    • Vanishing Gradients in RNN: Bidirectional RNN
    • Vanishing Gradients in RNN: Attention Model
    • Vanishing Gradients in RNN: Attention Model Optional
    • TensorFlow: Introduction to TensorFlow
    • TensorFlow: TensorFlow Text Classification Example using RNN
    • Project I: Book Writer: Introduction
    • Project I: Book Writer: Data Mapping
    • Project I: Book Writer: Modling RNN Architecture
    • Project I: Book Writer: Modling RNN Model in TensorFlow
    • Project I: Book Writer: Modling RNN Model Training
    • Project I: Book Writer: Modling RNN Model Text Generation
    • Project I: Book Writer: Activity
    • Project II: Stock Price Prediction: Problem Statement
    • Project II: Stock Price Prediction: Data Set
    • Project II: Stock Price Prediction: Data Prepration
    • Project II: Stock Price Prediction: RNN Model Training and Evaluation
    • Project II: Stock Price Prediction: Activity
    • Further Readings and Resourses: Further Readings and Resourses 1
  • 4

    NLP-Natural Language Processing in Python(Theory & Projects)

    • Introduction: Introduction to Course
    • Introduction: Introduction to Instructor
    • Introduction: Introduction to Co-Instructor
    • Introduction: Course Introduction
    • Introduction(Regular Expressions): What Is Regular Expression
    • Introduction(Regular Expressions): Why Regular Expression
    • Introduction(Regular Expressions): ELIZA Chatbot
    • Introduction(Regular Expressions): Python Regular Expression Package
    • Meta Characters(Regular Expressions): Meta Characters
    • Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise
    • Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise Solution
    • Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise 2
    • Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise 2 Solution
    • Meta Characters(Regular Expressions): Meta Characters Cap
    • Meta Characters(Regular Expressions): Meta Characters Cap Exercise 3
    • Meta Characters(Regular Expressions): Meta Characters Cap Exercise 3 Solution
    • Meta Characters(Regular Expressions): Backslash
    • Meta Characters(Regular Expressions): Backslash Continued
    • Meta Characters(Regular Expressions): Backslash Continued 01
    • Meta Characters(Regular Expressions): Backslash Squared Brackets Exercise
    • Meta Characters(Regular Expressions): Backslash Squared Brackets Exercise Solution
    • Meta Characters(Regular Expressions): Backslash Squared Brackets Exercise Another Solution
    • Meta Characters(Regular Expressions): Backslash Exercise
    • Meta Characters(Regular Expressions): Backslash Exercise Solution And Special Sequences Exercise
    • Meta Characters(Regular Expressions): Solution And Special Sequences Exercise Solution
    • Meta Characters(Regular Expressions): Meta Character Asterisk
    • Meta Characters(Regular Expressions): Meta Character Asterisk Exercise
    • Meta Characters(Regular Expressions): Meta Character Asterisk Exercise Solution
    • Meta Characters(Regular Expressions): Meta Character Asterisk Homework
    • Meta Characters(Regular Expressions): Meta Character Asterisk Greedymatching
    • Meta Characters(Regular Expressions): Meta Character Plus And Questionmark
    • Meta Characters(Regular Expressions): Meta Character Curly Brackets Exercise
    • Meta Characters(Regular Expressions): Meta Character Curly Brackets Exercise Solution
    • Pattern Objects: Pattern Objects
    • Pattern Objects: Pattern Objects Match Method Exercise
    • Pattern Objects: Pattern Objects Match Method Exercise Solution
    • Pattern Objects: Pattern Objects Match Method Vs Search Method
    • Pattern Objects: Pattern Objects Finditer Method
    • Pattern Objects: Pattern Objects Finditer Method Exercise Solution
    • More Meta Characters: Meta Characters Logical Or
    • More Meta Characters: Meta Characters Beginning And End Patterns
    • More Meta Characters: Meta Characters Paranthesis
    • String Modification: String Modification
    • String Modification: Word Tokenizer Using Split Method
    • String Modification: Sub Method Exercise
    • String Modification: Sub Method Exercise Solution
    • Words and Tokens: What Is A Word
    • Words and Tokens: Definition Of Word Is Task Dependent
    • Words and Tokens: Vocabulary And Corpus
    • Words and Tokens: Tokens
    • Words and Tokens: Tokenization In Spacy
    • Sentiment Classification: Yelp Reviews Classification Mini Project Introduction
    • Sentiment Classification: Yelp Reviews Classification Mini Project Vocabulary Initialization
    • Sentiment Classification: Yelp Reviews Classification Mini Project Adding Tokens To Vocabulary
    • Sentiment Classification: Yelp Reviews Classification Mini Project Look Up Functions In Vocabulary
    • Sentiment Classification: Yelp Reviews Classification Mini Project Building Vocabulary From Data
    • Sentiment Classification: Yelp Reviews Classification Mini Project One Hot Encoding
    • Sentiment Classification: Yelp Reviews Classification Mini Project One Hot Encoding Implementation
    • Sentiment Classification: Yelp Reviews Classification Mini Project Encoding Documents
    • Sentiment Classification: Yelp Reviews Classification Mini Project Encoding Documents Implementation
    • Sentiment Classification: Yelp Reviews Classification Mini Project Train Test Splits
    • Sentiment Classification: Yelp Reviews Classification Mini Project Featurecomputation
    • Sentiment Classification: Yelp Reviews Classification Mini Project Classification
    • Language Independent Tokenization: Tokenization In Detial Introduction
    • Language Independent Tokenization: Tokenization Is Hard
    • Language Independent Tokenization: Tokenization Byte Pair Encoding
    • Language Independent Tokenization: Tokenization Byte Pair Encoding Example
    • Language Independent Tokenization: Tokenization Byte Pair Encoding On Test Data
    • Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation Getpaircounts
    • Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation Mergeincorpus
    • Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE Training
    • Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE Encoding
    • Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair
    • Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair 1
    • Text Nomalization: Word Normalization Case Folding
    • Text Nomalization: Word Normalization Lematization
    • Text Nomalization: Word Normalization Stemming
    • Text Nomalization: Word Normalization Sentence Segmentation
    • String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Intro
    • String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Example
    • String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Table Filling
    • String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Dynamic Programming
    • String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Psudocode
    • String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Implementation
    • String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Implementation Bugfixing
    • String Matching and Spelling Correction: Spelling Correction Implementation
    • Language Modeling: What Is A Language Model
    • Language Modeling: Language Model Formal Definition
    • Language Modeling: Language Model Curse Of Dimensionality
    • Language Modeling: Language Model Markov Assumption And N-Grams
    • Language Modeling: Language Model Implementation Setup
    • Language Modeling: Language Model Implementation Ngrams Function
    • Language Modeling: Language Model Implementation Update Counts Function
    • Language Modeling: Language Model Implementation Probability Model Funciton
    • Language Modeling: Language Model Implementation Reading Corpus
    • Language Modeling: Language Model Implementation Sampling Text
    • Topic Modelling with Word and Document Representations: One Hot Vectors
    • Topic Modelling with Word and Document Representations: One Hot Vectors Implementaton
    • Topic Modelling with Word and Document Representations: One Hot Vectors Limitations
    • Topic Modelling with Word and Document Representations: One Hot Vectors Uses As Target Labeling
    • Topic Modelling with Word and Document Representations: Term Frequency For Document Representations
    • Topic Modelling with Word and Document Representations: Term Frequency For Document Representations Implementations
    • Topic Modelling with Word and Document Representations: Term Frequency For Word Representations
    • Topic Modelling with Word and Document Representations: TFIDF For Document Representations
    • Topic Modelling with Word and Document Representations: TFIDF For Document Representations Implementation Reading Corpus
    • Topic Modelling with Word and Document Representations: TFIDF For Document Representations Implementation Computing Document Frequency
    • Topic Modelling with Word and Document Representations: TFIDF For Document Representations Implementation Computing TFIDF
    • Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 1
    • Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 3
    • Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 4
    • Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 5
    • Topic Modelling with Word and Document Representations: Topic Modeling With Gensim
    • Word Embeddings LSI: Word Co-occurrence Matrix
    • Word Embeddings LSI: Word Co-occurrence Matrix vs Document-term Matrix
    • Word Embeddings LSI: Word Co-occurrence Matrix Implementation Preparing Data
    • Word Embeddings LSI: Word Co-occurrence Matrix Implementation Preparing Data 2
    • Word Embeddings LSI: Word Co-occurrence Matrix Implementation Preparing Data Getting Vocabulary
    • Word Embeddings LSI: Word Co-occurrence Matrix Implementation Final Function
    • Word Embeddings LSI: Word Co-occurrence Matrix Implementation Handling Memory Issues On Large Corpus
    • Word Embeddings LSI: Word Co-occurrence Matrix Sparsity
    • Word Embeddings LSI: Word Co-occurrence Matrix Positive Point Wise Mutual Information PPMI
    • Word Embeddings LSI: PCA For Dense Embeddings
    • Word Embeddings LSI: Latent Semantic Analysis
    • Word Embeddings LSI: Latent Semantic Analysis Implementation
    • Word Semantics: Cosine Similarity
    • Word Semantics: Cosine Similarity Geting Norms Of Vectors
    • Word Semantics: Cosine Similarity Normalizing Vectors
    • Word Semantics: Cosine Similarity With More Than One Vectors
    • Word Semantics: Cosine Similarity Getting Most Similar Words In The Vocabulary
    • Word Semantics: Cosine Similarity Getting Most Similar Words In The Vocabulary Fixingbug Of Dimensions
    • Word Semantics: Cosine Similarity Word2Vec Embeddings
    • Word Semantics: Words Analogies
    • Word Semantics: Words Analogies Implemenation 1
    • Word Semantics: Words Analogies Implemenation 2
    • Word Semantics: Words Visualizations
    • Word Semantics: Words Visualizations Implementaion
    • Word Semantics: Words Visualizations Implementaion 2
    • Word2vec: Static And Dynamic Embeddings
    • Word2vec: Self Supervision
    • Word2vec: Word2Vec Algorithm Abstract
    • Word2vec: Word2Vec Why Negative Sampling
    • Word2vec: Word2Vec What Is Skip Gram
    • Word2vec: Word2Vec How To Define Probability Law
    • Word2vec: Word2Vec Sigmoid
    • Word2vec: Word2Vec Formalizing Loss Function
    • Word2vec: Word2Vec Loss Function
    • Word2vec: Word2Vec Gradient Descent Step
    • Word2vec: Word2Vec Implemenation Preparing Data
    • Word2vec: Word2Vec Implemenation Gradient Step
    • Word2vec: Word2Vec Implemenation Driver Function
    • Need of Deep Learning for NLP: Why RNNs For NLP
    • Need of Deep Learning for NLP: Pytorch Installation And Tensors Introduction
    • Need of Deep Learning for NLP: Automatic Diffrenciation Pytorch
    • Introduction(NLP with Deep Learning DNN): Why DNNs In Machine Learning
    • Introduction(NLP with Deep Learning DNN): Representational Power And Data Utilization Capacity Of DNN
    • Introduction(NLP with Deep Learning DNN): Perceptron
    • Introduction(NLP with Deep Learning DNN): Perceptron Implementation
    • Introduction(NLP with Deep Learning DNN): DNN Architecture
    • Introduction(NLP with Deep Learning DNN): DNN Forwardstep Implementation
    • Introduction(NLP with Deep Learning DNN): DNN Why Activation Function Is Require
    • Introduction(NLP with Deep Learning DNN): DNN Properties Of Activation Function
    • Introduction(NLP with Deep Learning DNN): DNN Activation Functions In Pytorch
    • Introduction(NLP with Deep Learning DNN): DNN What Is Loss Function
    • Introduction(NLP with Deep Learning DNN): DNN Loss Function In Pytorch
    • Training(NLP with DNN): DNN Gradient Descent
    • Training(NLP with DNN): DNN Gradient Descent Implementation
    • Training(NLP with DNN): DNN Gradient Descent Stochastic Batch Minibatch
    • Training(NLP with DNN): DNN Gradient Descent Summary
    • Training(NLP with DNN): DNN Implemenation Gradient Step
    • Training(NLP with DNN): DNN Implemenation Stochastic Gradient Descent
    • Training(NLP with DNN): DNN Implemenation Batch Gradient Descent
    • Training(NLP with DNN): DNN Implemenation Minibatch Gradient Descent
    • Training(NLP with DNN): DNN Implemenation In Pytorch
    • Hyper parameters(NLP with DNN): DNN Weights Initializations
    • Hyper parameters(NLP with DNN): DNN Learning Rate
    • Hyper parameters(NLP with DNN): DNN Batch Normalization
    • Hyper parameters(NLP with DNN): DNN Batch Normalization Implementation
    • Hyper parameters(NLP with DNN): DNN Optimizations
    • Hyper parameters(NLP with DNN): DNN Dropout
    • Hyper parameters(NLP with DNN): DNN Dropout In Pytorch
    • Hyper parameters(NLP with DNN): DNN Early Stopping
    • Hyper parameters(NLP with DNN): DNN Hyperparameters
    • Hyper parameters(NLP with DNN): DNN Pytorch CIFAR10 Example
    • Introduction(NLP with Deep Learning RNN): What Is RNN
    • Introduction(NLP with Deep Learning RNN): Understanding RNN With A Simple Example
    • Introduction(NLP with Deep Learning RNN): RNN Applications Human Activity Recognition
    • Introduction(NLP with Deep Learning RNN): RNN Applications Image Captioning
    • Introduction(NLP with Deep Learning RNN): RNN Applications Machine Translation
    • Introduction(NLP with Deep Learning RNN): RNN Applications Machine Translation
    • Introduction(NLP with Deep Learning RNN): RNN Applications Speech Recognition Stock Price Prediction
    • Introduction(NLP with Deep Learning RNN): RNN Models
    • Mini-project Language Modelling: Language Modeling Next Word Prediction
    • Mini-project Language Modelling: Language Modeling Next Word Prediction Vocabulary Index
    • Mini-project Language Modelling: Language Modeling Next Word Prediction Vocabulary Index Embeddings
    • Mini-project Language Modelling: Language Modeling Next Word Prediction Rnn Architecture
    • Mini-project Language Modelling: Language Modeling Next Word Prediction Python 1
    • Mini-project Language Modelling: Language Modeling Next Word Prediction Python 2
    • Mini-project Language Modelling: Language Modeling Next Word Prediction Python 3
    • Mini-project Language Modelling: Language Modeling Next Word Prediction Python 4
    • Mini-project Language Modelling: Language Modeling Next Word Prediction Python 5
    • Mini-project Language Modelling: Language Modeling Next Word Prediction Python 6
    • Mini-project Sentiment Classification: Vocabulary Implementation
    • Mini-project Sentiment Classification: Vocabulary Implementation Helpers
    • Mini-project Sentiment Classification: Vocabulary Implementation From File
    • Mini-project Sentiment Classification: Vectorizer
    • Mini-project Sentiment Classification: RNN Setup
    • Mini-project Sentiment Classification: RNN Setup 1
    • RNN in PyTorch: RNN In Pytorch Introduction
    • RNN in PyTorch: RNN In Pytorch Embedding Layer
    • RNN in PyTorch: RNN In Pytorch Nn Rnn
    • RNN in PyTorch: RNN In Pytorch Output Shapes
    • RNN in PyTorch: RNN In Pytorch Gatedunits
    • RNN in PyTorch: RNN In Pytorch Gatedunits GRU LSTM
    • RNN in PyTorch: RNN In Pytorch Bidirectional RNN
    • RNN in PyTorch: RNN In Pytorch Bidirectional RNN Output Shapes
    • RNN in PyTorch: RNN In Pytorch Bidirectional RNN Output Shapes Seperation
    • RNN in PyTorch: RNN In Pytorch Example
    • Advanced RNN models: RNN Encoder Decoder
    • Advanced RNN models: RNN Attention
    • Neural Machine Translation: Introduction To Dataset And Packages
    • Neural Machine Translation: Implementing Language Class
    • Neural Machine Translation: Testing Language Class And Implementing Normalization
    • Neural Machine Translation: Reading Datafile
    • Neural Machine Translation: Reading Building Vocabulary
    • Neural Machine Translation: EncoderRNN
    • Neural Machine Translation: DecoderRNN
    • Neural Machine Translation: DecoderRNN Forward Step
    • Neural Machine Translation: DecoderRNN Helper Functions
    • Neural Machine Translation: Training Module
    • Neural Machine Translation: Stochastic Gradient Descent
    • Neural Machine Translation: NMT Training
    • Neural Machine Translation: NMT Evaluation
  • 5

    Advanced Chatbots with Deep Learning & Python

    • Introduction: Course and Instructor Introduction
    • Introduction: AI Sciences Introduction
    • Introduction: Course Description
    • Fundamentals of Chatbots for Deep Learning: Module Introduction
    • Fundamentals of Chatbots for Deep Learning: Conventional vs AI Chatbots
    • Fundamentals of Chatbots for Deep Learning: Geneative vs Retrievel Chatbots
    • Fundamentals of Chatbots for Deep Learning: Benifits of Deep Learning Chatbots
    • Fundamentals of Chatbots for Deep Learning: Chatbots in Medical Domain
    • Fundamentals of Chatbots for Deep Learning: Chatbots in Business
    • Fundamentals of Chatbots for Deep Learning: Chatbots in E-Commerce
    • Deep Learning Based Chatbot Architecture and Develpment: Module Introduction
    • Deep Learning Based Chatbot Architecture and Develpment: Deep Learning Architecture
    • Deep Learning Based Chatbot Architecture and Develpment: Encoder Decoder
    • Deep Learning Based Chatbot Architecture and Develpment: Steps Involved
    • Deep Learning Based Chatbot Architecture and Develpment: Project Overview and Packages
    • Deep Learning Based Chatbot Architecture and Develpment: Importing Libraries
    • Deep Learning Based Chatbot Architecture and Develpment: Data Prepration
    • Deep Learning Based Chatbot Architecture and Develpment: Develop Vocabulary
    • Deep Learning Based Chatbot Architecture and Develpment: Max Story and Question Length
    • Deep Learning Based Chatbot Architecture and Develpment: Tokenizer
    • Deep Learning Based Chatbot Architecture and Develpment: Separation and Sequence
    • Deep Learning Based Chatbot Architecture and Develpment: Vectorize Stories
    • Deep Learning Based Chatbot Architecture and Develpment: Vectorizing Train and Test Data
    • Deep Learning Based Chatbot Architecture and Develpment: Encoding
    • Deep Learning Based Chatbot Architecture and Develpment: Answer and Response
    • Deep Learning Based Chatbot Architecture and Develpment: Model Completion
    • Deep Learning Based Chatbot Architecture and Develpment: Predictions
  • 6

    Recommender Systems: An Applied Approach using Deep Learning

    • Introduction: Course Outline
    • Deep Learning Foundation for Recommender Systems: Module Introduction
    • Deep Learning Foundation for Recommender Systems: Overview
    • Deep Learning Foundation for Recommender Systems: Deep Learning in Recommendation Systems
    • Deep Learning Foundation for Recommender Systems: Inference After Training
    • Deep Learning Foundation for Recommender Systems: Inference Mechanism
    • Deep Learning Foundation for Recommender Systems: Embeddings and User Context
    • Deep Learning Foundation for Recommender Systems: Neutral Collaborative Filterin
    • Deep Learning Foundation for Recommender Systems: VAE Collaborative Filtering
    • Deep Learning Foundation for Recommender Systems: Strengths and Weaknesses of DL Models
    • Deep Learning Foundation for Recommender Systems: Deep Learning Quiz
    • Deep Learning Foundation for Recommender Systems: Deep Learning Quiz Solution
    • Project Amazon Product Recommendation System: Module Overview
    • Project Amazon Product Recommendation System: TensorFlow Recommenders
    • Project Amazon Product Recommendation System: Two Tower Model
    • Project Amazon Product Recommendation System: Project Overview
    • Project Amazon Product Recommendation System: Download Libraries
    • Project Amazon Product Recommendation System: Data Visualization with WordCloud
    • Project Amazon Product Recommendation System: Make Tensors from DataFrame
    • Project Amazon Product Recommendation System: Rating Our Data
    • Project Amazon Product Recommendation System: Random Train-Test Split
    • Project Amazon Product Recommendation System: Making the Model and Query Tower
    • Project Amazon Product Recommendation System: Candidate Tower and Retrieval System
    • Project Amazon Product Recommendation System: Compute Loss
    • Project Amazon Product Recommendation System: Train and Validation
    • Project Amazon Product Recommendation System: Accuracy vs Recommendations
    • Project Amazon Product Recommendation System: Making Recommendations