Course curriculum
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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
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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
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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
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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
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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
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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