Course curriculum

    1. Introduction: Introduction to Instructor

    2. Introduction: Introduction to Course

    3. Basics of Deep Learning: Problem to Solve Part 1

    4. Basics of Deep Learning: Problem to Solve Part 2

    5. Basics of Deep Learning: Problem to Solve Part 3

    6. Basics of Deep Learning: Linear Equation

    7. Basics of Deep Learning: Linear Equation Vectorized

    8. Basics of Deep Learning: 3D Feature Space

    9. Basics of Deep Learning: N Dimensional Space

    10. Basics of Deep Learning: Theory of Perceptron

    11. Basics of Deep Learning: Implementing Basic Perceptron

    12. Basics of Deep Learning: Logical Gates for Perceptrons

    13. Basics of Deep Learning: Perceptron Training Part 1

    14. Basics of Deep Learning: Perceptron Training Part 2

    15. Basics of Deep Learning: Learning Rate

    16. Basics of Deep Learning: Perceptron Training Part 3

    17. Basics of Deep Learning: Perceptron Algorithm

    18. Basics of Deep Learning: Coading Perceptron Algo (Data Reading & Visualization)

    19. Basics of Deep Learning: Coading Perceptron Algo (Perceptron Step)

    20. Basics of Deep Learning: Coading Perceptron Algo (Training Perceptron)

    21. Basics of Deep Learning: Coading Perceptron Algo (Visualizing the Results)

    22. Basics of Deep Learning: Problem with Linear Solutions

    23. Basics of Deep Learning: Solution to Problem

    24. Basics of Deep Learning: Error Functions

    25. Basics of Deep Learning: Discrete vs Continuous Error Function

    26. Basics of Deep Learning: Sigmoid Function

    27. Basics of Deep Learning: Multi-Class Problem

    28. Basics of Deep Learning: Problem of Negative Scores

    29. Basics of Deep Learning: Need of Softmax

    30. Basics of Deep Learning: Coding Softmax

    31. Basics of Deep Learning: One Hot Encoding

    32. Basics of Deep Learning: Maximum Likelihood Part 1

    33. Basics of Deep Learning: Maximum Likelihood Part 2

    34. Basics of Deep Learning: Cross Entropy

    35. Basics of Deep Learning: Cross Entropy Formulation

    36. Basics of Deep Learning: Multi Class Cross Entropy

    37. Basics of Deep Learning: Cross Entropy Implementation

    38. Basics of Deep Learning: Sigmoid Function Implementation

    39. Basics of Deep Learning: Output Function Implementation

    40. Deep Learning: Introduction to Gradient Decent

    41. Deep Learning: Convex Functions

    42. Deep Learning: Use of Derivatives

    43. Deep Learning: How Gradient Decent Works

    44. Deep Learning: Gradient Step

    45. Deep Learning: Logistic Regression Algorithm

    46. Deep Learning: Data Visualization and Reading

    47. Deep Learning: Updating Weights in Python

    48. Deep Learning: Implementing Logistic Regression

    49. Deep Learning: Visualization and Results

    50. Deep Learning: Gradient Decent vs Perceptron

    51. Deep Learning: Linear to Non Linear Boundaries

    52. Deep Learning: Combining Probabilities

    53. Deep Learning: Weighted Sums

    54. Deep Learning: Neural Network Architecture

    55. Deep Learning: Layers and DEEP Networks

    56. Deep Learning:Multi Class Classification

    57. Deep Learning: Basics of Feed Forward

    58. Deep Learning: Feed Forward for DEEP Net

    59. Deep Learning: Deep Learning Algo Overview

    60. Deep Learning: Basics of Back Propagation

    61. Deep Learning: Updating Weights

    62. Deep Learning: Chain Rule for BackPropagation

    63. Deep Learning: Sigma Prime

    64. Deep Learning: Data Analysis NN Implementation

    65. Deep Learning: One Hot Encoding (NN Implementation)

    66. Deep Learning: Scaling the Data (NN Implementation)

    67. Deep Learning: Splitting the Data (NN Implementation)

    68. Deep Learning: Helper Functions (NN Implementation)

    69. Deep Learning: Training (NN Implementation)

    70. Deep Learning: Testing (NN Implementation)

    71. Optimizations: Underfitting vs Overfitting

    72. Optimizations: Early Stopping

    73. Optimizations: Quiz

    74. Optimizations: Solution & Regularization

    75. Optimizations: L1 & L2 Regularization

    76. Optimizations: Dropout

    77. Optimizations: Local Minima Problem

    78. Optimizations: Random Restart Solution

    79. Optimizations: Vanishing Gradient Problem

    80. Optimizations: Other Activation Functions

    81. Final Project: Final Project Part 1

    82. Final Project: Final Project Part 2

    83. Final Project: Final Project Part 3

    84. Final Project: Final Project Part 4

    85. Final Project: Final Project Part 5

    1. Introduction: Instructor Introduction

    2. Introduction: Why CNN

    3. Introduction: Focus of the Course

    4. Image Processing: Gray Scale Images

    5. Image Processing: Gray Scale Images Quiz

    6. Image Processing: Gray Scale Images Solution

    7. Image Processing: RGB Images

    8. Image Processing: RGB Images Quiz

    9. Image Processing: RGB Images Solution

    10. Image Processing: Reading and Showing Images in Python

    11. Image Processing: Reading and Showing Images in Python Quiz

    12. Image Processing: Reading and Showing Images in Python Solution

    13. Image Processing: Converting an Image to Grayscale in Python

    14. Image Processing: Converting an Image to Grayscale in Python Quiz

    15. Image Processing: Converting an Image to Grayscale in Python Solution

    16. Image Processing: Image Formation

    17. Image Processing: Image Formation Quiz

    18. Image Processing: Image Formation Solution

    19. Image Processing: Image Blurring 1

    20. Image Processing: Image Blurring 1 Quiz

    21. Image Processing: Image Blurring 1 Solution

    22. Image Processing: Image Blurring 2

    23. Image Processing: Image Blurring 2 Quiz

    24. Image Processing: Image Blurring 2 Solution

    25. Image Processing: General Image Filtering

    26. Image Processing: Convolution

    27. Image Processing: Edge Detection

    28. Image Processing: Image Sharpening

    29. Image Processing: Implementation of Image Blurring Edge Detection Image Sharpening in Python

    30. Image Processing: Parameteric Shape Detection

    31. Image Processing: Image Processing Activity

    32. Image Processing: Image Processing Activity Solution

    33. Object Detection: Introduction to Object Detection

    34. Object Detection: Classification PipleLine

    35. Object Detection: Classification PipleLine Quiz

    36. Object Detection: Classification PipleLine Solution

    37. Object Detection: Sliding Window Implementation

    38. Object Detection: Shift Scale Rotation Invariance

    39. Object Detection: Shift Scale Rotation Invariance Exercise

    40. Object Detection: Person Detection

    41. Object Detection: HOG Features

    42. Object Detection: HOG Features Exercise

    43. Object Detection: Hand Engineering vs CNNs

    44. Object Detection: Object Detection Activity

    45. Deep Neural Network Overview: Neuron and Perceptron

    46. Deep Neural Network Overview: DNN Architecture

    47. Deep Neural Network Overview: DNN Architecture Quiz

    48. Deep Neural Network Overview: DNN Architecture Solution

    49. Deep Neural Network Overview: FeedForward FullyConnected MLP

    50. Deep Neural Network Overview: Calculating Number of Weights of DNN

    51. Deep Neural Network Overview: Calculating Number of Weights of DNN Quiz

    52. Deep Neural Network Overview: Calculating Number of Weights of DNN Solution

    53. Deep Neural Network Overview: Number of Nuerons vs Number of Layers

    54. Deep Neural Network Overview: Discriminative vs Generative Learning

    55. Deep Neural Network Overview: Universal Approximation Therorem

    56. Deep Neural Network Overview: Why Depth

    57. Deep Neural Network Overview: Decision Boundary in DNN

    58. Deep Neural Network Overview: Decision Boundary in DNN Quiz

    59. Deep Neural Network Overview: Decision Boundary in DNN Solution

    60. Deep Neural Network Overview: BiasTerm

    61. Deep Neural Network Overview: BiasTerm Quiz

    62. Deep Neural Network Overview: BiasTerm Solution

    63. Deep Neural Network Overview: Activation Function

    64. Deep Neural Network Overview: Activation Function Quiz

    65. Deep Neural Network Overview: Activation Function Solution

    66. Deep Neural Network Overview: DNN Training Parameters

    67. Deep Neural Network Overview: DNN Training Parameters Quiz

    68. Deep Neural Network Overview: DNN Training Parameters Solution

    69. Deep Neural Network Overview: Gradient Descent

    70. Deep Neural Network Overview: BackPropagation

    71. Deep Neural Network Overview: Training DNN Animantion

    72. Deep Neural Network Overview: Weigth Initialization

    73. Deep Neural Network Overview: Weigth Initialization Quiz

    74. Deep Neural Network Overview: Weigth Initialization Solution

    75. Deep Neural Network Overview: Batch miniBatch Stocastic Gradient Descent

    76. Deep Neural Network Overview: Batch Normalization

    77. Deep Neural Network Overview: Rprop and Momentum

    78. Deep Neural Network Overview: Rprop and Momentum Quiz

    79. Deep Neural Network Overview: Rprop and Momentum Solution

    80. Deep Neural Network Overview: Convergence Animation

    81. Deep Neural Network Overview: DropOut, Early Stopping and Hyperparameters

    82. Deep Neural Network Overview: DropOut, Early Stopping and Hyperparameters Quiz

    83. Deep Neural Network Overview: DropOut, Early Stopping and Hyperparameters Solution

    84. Deep Neural Network Architecture: Convolution Revisited

    85. Deep Neural Network Architecture: Implementing Convolution in Python Revisited

    86. Deep Neural Network Architecture: Why Convolution

    87. Deep Neural Network Architecture: Filters Padding Strides

    88. Deep Neural Network Architecture: Padding Image

    89. Deep Neural Network Architecture: Pooling Tensors

    90. Deep Neural Network Architecture: CNN Example

    91. Deep Neural Network Architecture: Convolution and Pooling Details

    92. Deep Neural Network Architecture: Maxpooling Exercise

    93. Deep Neural Network Architecture: NonVectorized Implementations of Conv2d and Pool2d

    94. Deep Neural Network Architecture: Deep Neural Network Architecture Activity

    95. Gradient Descent in CNNs: Example Setup

    96. Gradient Descent in CNNs: Why Derivaties

    97. Gradient Descent in CNNs: Why Derivaties Quiz

    98. Gradient Descent in CNNs: Why Derivaties Solution

    99. Gradient Descent in CNNs: What is Chain Rule

    100. Gradient Descent in CNNs: Applying Chain Rule

    101. Gradient Descent in CNNs: Gradients of MaxPooling Layer

    102. Gradient Descent in CNNs: Gradients of MaxPooling Layer Quiz

    103. Gradient Descent in CNNs: Gradients of MaxPooling Layer Solution

    104. Gradient Descent in CNNs: Gradients of Convolutional Layer

    105. Gradient Descent in CNNs: Extending To Multiple Filters

    106. Gradient Descent in CNNs: Extending to Multiple Layers

    107. Gradient Descent in CNNs: Extending to Multiple Layers Quiz

    108. Gradient Descent in CNNs: Extending to Multiple Layers Solution

    109. Gradient Descent in CNNs: Implementation in Numpy ForwardPass

    110. Gradient Descent in CNNs: Implementation in Numpy BackwardPass 1

    111. Gradient Descent in CNNs: Implementation in Numpy BackwardPass 2

    112. Gradient Descent in CNNs: Implementation in Numpy BackwardPass 3

    113. Gradient Descent in CNNs: Implementation in Numpy BackwardPass 4

    114. Gradient Descent in CNNs: Implementation in Numpy BackwardPass 5

    115. Gradient Descent in CNNs: Gradient Descent in CNNs Activity

    116. Introduction to TensorFlow: Introduction

    117. Introduction to TensorFlow: FashionMNIST Example Plan Neural Network

    118. Introduction to TensorFlow: FashionMNIST Example CNN

    119. Introduction to TensorFlow: Introduction to TensorFlow Activity

    120. Classical CNNs: LeNet

    121. Classical CNNs: LeNet Quiz

    122. Classical CNNs: LeNet Solution

    123. Classical CNNs: AlexNet

    124. Classical CNNs: VGG

    125. Classical CNNs: InceptionNet

    126. Classical CNNs: GoogLeNet

    127. Classical CNNs: Classical CNNs Activity

    128. Transfer Learning: What is Transfer learning

    129. Transfer Learning: Why Transfer Learning

    130. Transfer Learning: ImageNet Challenge

    131. Transfer Learning: Practical Tips

    132. Transfer Learning: Project in TensorFlow

    133. Transfer Learning: Transfer Learning Activity

    134. Yolo: Image Classfication Revisited

    135. Yolo: Sliding Window Object Localization

    136. Yolo: Sliding Window Efficient Implementation

    137. Yolo: Yolo Introduction

    138. Yolo: Yolo Training Data Generation

    139. Yolo: Yolo Anchor Boxes

    140. Yolo: Yolo Algorithm

    141. Yolo: Yolo Non Maxima Supression

    142. Yolo: RCNN

    143. Yolo: Yolo Activity

    144. Face Verification: Problem Setup

    145. Face Verification: Project Implementation

    146. Face Verification: Face Verification Activity

    147. Neural Style Transfer: Problem Setup

    148. Neural Style Transfer: Implementation Tensorflow Hub

    1. Introduction: Introduction to Instructor and Aisciences

    2. Introduction: Introduction To Instructor

    3. Introduction: Focus of the Course

    4. Applications of RNN (Motivation): Human Activity Recognition

    5. Applications of RNN (Motivation): Image Captioning

    6. Applications of RNN (Motivation): Machine Translation

    7. Applications of RNN (Motivation): Speech Recognition

    8. Applications of RNN (Motivation): Stock Price Predictions

    9. Applications of RNN (Motivation): When to Model RNN

    10. Applications of RNN (Motivation): Activity

    11. DNN Overview: Why PyTorch

    12. DNN Overview: PyTorch Installation and Tensors Introduction

    13. DNN Overview: Automatic Diffrenciation Pytorch New

    14. DNN Overview: Why DNNs in Machine Learning

    15. DNN Overview: Representational Power and Data Utilization Capacity of DNN

    16. DNN Overview: Perceptron

    17. DNN Overview: Perceptron Exercise

    18. DNN Overview: Perceptron Exercise Solution

    19. DNN Overview: Perceptron Implementation

    20. DNN Overview: DNN Architecture

    21. DNN Overview: DNN Architecture Exercise

    22. DNN Overview: DNN Architecture Exercise Solution

    23. DNN Overview: DNN ForwardStep Implementation

    24. DNN Overview: DNN Why Activation Function Is Required

    25. DNN Overview: DNN Why Activation Function Is Required Exercise

    26. DNN Overview: DNN Why Activation Function Is Required Exercise Solution

    27. DNN Overview: DNN Properties Of Activation Function

    28. DNN Overview: DNN Activation Functions In Pytorch

    29. DNN Overview: DNN What Is Loss Function

    30. DNN Overview: DNN What Is Loss Function Exercise

    31. DNN Overview: DNN What Is Loss Function Exercise Solution

    32. DNN Overview: DNN What Is Loss Function Exercise 02

    33. DNN Overview: DNN What Is Loss Function Exercise 02 Solution

    34. DNN Overview: DNN Loss Function In Pytorch

    35. DNN Overview: DNN Gradient Descent

    36. DNN Overview: DNN Gradient Descent Exercise

    37. DNN Overview: DNN Gradient Descent Exercise Solution

    38. DNN Overview: DNN Gradient Descent Implementation

    39. DNN Overview: DNN Gradient Descent Stochastic Batch Minibatch

    40. DNN Overview: DNN Gradient Descent Summary

    41. DNN Overview: DNN Implemenation Gradient Step

    42. DNN Overview: DNN Implemenation Stochastic Gradient Descent

    43. DNN Overview: DNN Implemenation Batch Gradient Descent

    44. DNN Overview: DNN Implemenation Minibatch Gradient Descent

    45. DNN Overview: DNN Implemenation In PyTorch

    46. DNN Overview: DNN Weights Initializations

    47. DNN Overview: DNN Learning Rate

    48. DNN Overview: DNN Batch Normalization

    49. DNN Overview: DNN batch Normalization Implementation

    50. DNN Overview: DNN Optimizations

    51. DNN Overview: DNN Dropout

    52. DNN Overview: DNN Dropout In PyTorch

    53. DNN Overview: DNN Early Stopping

    54. DNN Overview: DNN Hyperparameters

    55. DNN Overview: DNN Pytorch CIFAR10 Example

    56. RNN Architecture: Introduction to Module

    57. RNN Architecture: Fixed Length Memory Model

    58. RNN Architecture: Fixed Length Memory Model Exercise

    59. RNN Architecture: Fixed Length Memory Model Exercise Solution Part 01

    60. RNN Architecture: Fixed Length Memory Model Exercise Solution Part 02

    61. RNN Architecture: Infinite Memory Architecture

    62. RNN Architecture: Infinite Memory Architecture Exercise

    63. RNN Architecture: Infinite Memory Architecture Solution

    64. RNN Architecture: Weight Sharing

    65. RNN Architecture: Notations

    66. RNN Architecture: ManyToMany Model

    67. RNN Architecture: ManyToMany Model Exercise 01

    68. RNN Architecture: ManyToMany Model Solution 01

    69. RNN Architecture: ManyToMany Model Exercise 02

    70. RNN Architecture: ManyToMany Model Solution 02

    71. RNN Architecture: ManyToOne Model

    72. RNN Architecture: OneToMany Model Exercise

    73. RNN Architecture: OneToMany Model Solution

    74. RNN Architecture: OneToMany Model

    75. RNN Architecture: ManyToOne Model Exercise

    76. RNN Architecture: ManyToOne Model Solution

    77. RNN Architecture: Activity Many to One

    78. RNN Architecture: Activity Many to One Exercise

    79. RNN Architecture: Activity Many to One Solution

    80. RNN Architecture: ManyToMany Different Sizes Model

    81. RNN Architecture: Activity Many to Many Nmt

    82. RNN Architecture: Models Summary

    83. RNN Architecture: Deep RNNs

    84. RNN Architecture: Deep RNNs Exercise

    85. RNN Architecture: Deep RNNs Solution

    86. Gradient Decsent in RNN: Introduction to Gradient Descent Module

    87. Gradient Decsent in RNN: Example Setup

    88. Gradient Decsent in RNN: Equations

    89. Gradient Decsent in RNN: Equations Exercise

    90. Gradient Decsent in RNN: Equations Solution

    91. Gradient Decsent in RNN: Loss Function

    92. Gradient Decsent in RNN: Why Gradients

    93. Gradient Decsent in RNN: Why Gradients Exercise

    94. Gradient Decsent in RNN: Why Gradients Solution

    95. Gradient Decsent in RNN: Chain Rule

    96. Gradient Decsent in RNN: Chain Rule in Action

    97. Gradient Decsent in RNN: BackPropagation Through Time

    98. Gradient Decsent in RNN: Activity

    99. RNN implementation: Automatic Diffrenciation

    100. RNN implementation: Automatic Diffrenciation Pytorch

    101. RNN implementation: Language Modeling Next Word Prediction Vocabulary Index

    102. RNN implementation: Language Modeling Next Word Prediction Vocabulary Index Embeddings

    103. RNN implementation: Language Modeling Next Word Prediction RNN Architecture

    104. RNN implementation: Language Modeling Next Word Prediction Python 1

    105. RNN implementation: Language Modeling Next Word Prediction Python 2

    106. RNN implementation: Language Modeling Next Word Prediction Python 3

    107. RNN implementation: Language Modeling Next Word Prediction Python 4

    108. RNN implementation: Language Modeling Next Word Prediction Python 5

    109. RNN implementation: Language Modeling Next Word Prediction Python 6

    110. Sentiment Classification using RNN: Vocabulary Implementation

    111. Sentiment Classification using RNN: Vocabulary Implementation Helpers

    112. Sentiment Classification using RNN: Vocabulary Implementation From File

    113. Sentiment Classification using RNN: Vectorizer

    114. Sentiment Classification using RNN: RNN Setup 1

    115. Sentiment Classification using RNN: RNN Setup

    116. Sentiment Classification using RNN: WhatNext

    117. Vanishing Gradients in RNN: Introduction to Better RNNs Module

    118. Vanishing Gradients in RNN: Introduction Vanishing Gradients in RNN

    119. Vanishing Gradients in RNN: GRU

    120. Vanishing Gradients in RNN: GRU Optional

    121. Vanishing Gradients in RNN: LSTM

    122. Vanishing Gradients in RNN: LSTM Optional

    123. Vanishing Gradients in RNN: Bidirectional RNN

    124. Vanishing Gradients in RNN: Attention Model

    125. Vanishing Gradients in RNN: Attention Model Optional

    126. TensorFlow: Introduction to TensorFlow

    127. TensorFlow: TensorFlow Text Classification Example using RNN

    128. Project I: Book Writer: Introduction

    129. Project I: Book Writer: Data Mapping

    130. Project I: Book Writer: Modling RNN Architecture

    131. Project I: Book Writer: Modling RNN Model in TensorFlow

    132. Project I: Book Writer: Modling RNN Model Training

    133. Project I: Book Writer: Modling RNN Model Text Generation

    134. Project I: Book Writer: Activity

    135. Project II: Stock Price Prediction: Problem Statement

    136. Project II: Stock Price Prediction: Data Set

    137. Project II: Stock Price Prediction: Data Prepration

    138. Project II: Stock Price Prediction: RNN Model Training and Evaluation

    139. Project II: Stock Price Prediction: Activity

    140. Further Readings and Resourses: Further Readings and Resourses 1

    1. Introduction: Introduction to Course

    2. Introduction: Introduction to Instructor

    3. Introduction: Introduction to Co-Instructor

    4. Introduction: Course Introduction

    5. Introduction(Regular Expressions): What Is Regular Expression

    6. Introduction(Regular Expressions): Why Regular Expression

    7. Introduction(Regular Expressions): ELIZA Chatbot

    8. Introduction(Regular Expressions): Python Regular Expression Package

    9. Meta Characters(Regular Expressions): Meta Characters

    10. Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise

    11. Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise Solution

    12. Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise 2

    13. Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise 2 Solution

    14. Meta Characters(Regular Expressions): Meta Characters Cap

    15. Meta Characters(Regular Expressions): Meta Characters Cap Exercise 3

    16. Meta Characters(Regular Expressions): Meta Characters Cap Exercise 3 Solution

    17. Meta Characters(Regular Expressions): Backslash

    18. Meta Characters(Regular Expressions): Backslash Continued

    19. Meta Characters(Regular Expressions): Backslash Continued 01

    20. Meta Characters(Regular Expressions): Backslash Squared Brackets Exercise

    21. Meta Characters(Regular Expressions): Backslash Squared Brackets Exercise Solution

    22. Meta Characters(Regular Expressions): Backslash Squared Brackets Exercise Another Solution

    23. Meta Characters(Regular Expressions): Backslash Exercise

    24. Meta Characters(Regular Expressions): Backslash Exercise Solution And Special Sequences Exercise

    25. Meta Characters(Regular Expressions): Solution And Special Sequences Exercise Solution

    26. Meta Characters(Regular Expressions): Meta Character Asterisk

    27. Meta Characters(Regular Expressions): Meta Character Asterisk Exercise

    28. Meta Characters(Regular Expressions): Meta Character Asterisk Exercise Solution

    29. Meta Characters(Regular Expressions): Meta Character Asterisk Homework

    30. Meta Characters(Regular Expressions): Meta Character Asterisk Greedymatching

    31. Meta Characters(Regular Expressions): Meta Character Plus And Questionmark

    32. Meta Characters(Regular Expressions): Meta Character Curly Brackets Exercise

    33. Meta Characters(Regular Expressions): Meta Character Curly Brackets Exercise Solution

    34. Pattern Objects: Pattern Objects

    35. Pattern Objects: Pattern Objects Match Method Exercise

    36. Pattern Objects: Pattern Objects Match Method Exercise Solution

    37. Pattern Objects: Pattern Objects Match Method Vs Search Method

    38. Pattern Objects: Pattern Objects Finditer Method

    39. Pattern Objects: Pattern Objects Finditer Method Exercise Solution

    40. More Meta Characters: Meta Characters Logical Or

    41. More Meta Characters: Meta Characters Beginning And End Patterns

    42. More Meta Characters: Meta Characters Paranthesis

    43. String Modification: String Modification

    44. String Modification: Word Tokenizer Using Split Method

    45. String Modification: Sub Method Exercise

    46. String Modification: Sub Method Exercise Solution

    47. Words and Tokens: What Is A Word

    48. Words and Tokens: Definition Of Word Is Task Dependent

    49. Words and Tokens: Vocabulary And Corpus

    50. Words and Tokens: Tokens

    51. Words and Tokens: Tokenization In Spacy

    52. Sentiment Classification: Yelp Reviews Classification Mini Project Introduction

    53. Sentiment Classification: Yelp Reviews Classification Mini Project Vocabulary Initialization

    54. Sentiment Classification: Yelp Reviews Classification Mini Project Adding Tokens To Vocabulary

    55. Sentiment Classification: Yelp Reviews Classification Mini Project Look Up Functions In Vocabulary

    56. Sentiment Classification: Yelp Reviews Classification Mini Project Building Vocabulary From Data

    57. Sentiment Classification: Yelp Reviews Classification Mini Project One Hot Encoding

    58. Sentiment Classification: Yelp Reviews Classification Mini Project One Hot Encoding Implementation

    59. Sentiment Classification: Yelp Reviews Classification Mini Project Encoding Documents

    60. Sentiment Classification: Yelp Reviews Classification Mini Project Encoding Documents Implementation

    61. Sentiment Classification: Yelp Reviews Classification Mini Project Train Test Splits

    62. Sentiment Classification: Yelp Reviews Classification Mini Project Featurecomputation

    63. Sentiment Classification: Yelp Reviews Classification Mini Project Classification

    64. Language Independent Tokenization: Tokenization In Detial Introduction

    65. Language Independent Tokenization: Tokenization Is Hard

    66. Language Independent Tokenization: Tokenization Byte Pair Encoding

    67. Language Independent Tokenization: Tokenization Byte Pair Encoding Example

    68. Language Independent Tokenization: Tokenization Byte Pair Encoding On Test Data

    69. Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation Getpaircounts

    70. Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation Mergeincorpus

    71. Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE Training

    72. Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE Encoding

    73. Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair

    74. Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair 1

    75. Text Nomalization: Word Normalization Case Folding

    76. Text Nomalization: Word Normalization Lematization

    77. Text Nomalization: Word Normalization Stemming

    78. Text Nomalization: Word Normalization Sentence Segmentation

    79. String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Intro

    80. String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Example

    81. String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Table Filling

    82. String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Dynamic Programming

    83. String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Psudocode

    84. String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Implementation

    85. String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Implementation Bugfixing

    86. String Matching and Spelling Correction: Spelling Correction Implementation

    87. Language Modeling: What Is A Language Model

    88. Language Modeling: Language Model Formal Definition

    89. Language Modeling: Language Model Curse Of Dimensionality

    90. Language Modeling: Language Model Markov Assumption And N-Grams

    91. Language Modeling: Language Model Implementation Setup

    92. Language Modeling: Language Model Implementation Ngrams Function

    93. Language Modeling: Language Model Implementation Update Counts Function

    94. Language Modeling: Language Model Implementation Probability Model Funciton

    95. Language Modeling: Language Model Implementation Reading Corpus

    96. Language Modeling: Language Model Implementation Sampling Text

    97. Topic Modelling with Word and Document Representations: One Hot Vectors

    98. Topic Modelling with Word and Document Representations: One Hot Vectors Implementaton

    99. Topic Modelling with Word and Document Representations: One Hot Vectors Limitations

    100. Topic Modelling with Word and Document Representations: One Hot Vectors Uses As Target Labeling

    101. Topic Modelling with Word and Document Representations: Term Frequency For Document Representations

    102. Topic Modelling with Word and Document Representations: Term Frequency For Document Representations Implementations

    103. Topic Modelling with Word and Document Representations: Term Frequency For Word Representations

    104. Topic Modelling with Word and Document Representations: TFIDF For Document Representations

    105. Topic Modelling with Word and Document Representations: TFIDF For Document Representations Implementation Reading Corpus

    106. Topic Modelling with Word and Document Representations: TFIDF For Document Representations Implementation Computing Document Frequency

    107. Topic Modelling with Word and Document Representations: TFIDF For Document Representations Implementation Computing TFIDF

    108. Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 1

    109. Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 3

    110. Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 4

    111. Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 5

    112. Topic Modelling with Word and Document Representations: Topic Modeling With Gensim

    113. Word Embeddings LSI: Word Co-occurrence Matrix

    114. Word Embeddings LSI: Word Co-occurrence Matrix vs Document-term Matrix

    115. Word Embeddings LSI: Word Co-occurrence Matrix Implementation Preparing Data

    116. Word Embeddings LSI: Word Co-occurrence Matrix Implementation Preparing Data 2

    117. Word Embeddings LSI: Word Co-occurrence Matrix Implementation Preparing Data Getting Vocabulary

    118. Word Embeddings LSI: Word Co-occurrence Matrix Implementation Final Function

    119. Word Embeddings LSI: Word Co-occurrence Matrix Implementation Handling Memory Issues On Large Corpus

    120. Word Embeddings LSI: Word Co-occurrence Matrix Sparsity

    121. Word Embeddings LSI: Word Co-occurrence Matrix Positive Point Wise Mutual Information PPMI

    122. Word Embeddings LSI: PCA For Dense Embeddings

    123. Word Embeddings LSI: Latent Semantic Analysis

    124. Word Embeddings LSI: Latent Semantic Analysis Implementation

    125. Word Semantics: Cosine Similarity

    126. Word Semantics: Cosine Similarity Geting Norms Of Vectors

    127. Word Semantics: Cosine Similarity Normalizing Vectors

    128. Word Semantics: Cosine Similarity With More Than One Vectors

    129. Word Semantics: Cosine Similarity Getting Most Similar Words In The Vocabulary

    130. Word Semantics: Cosine Similarity Getting Most Similar Words In The Vocabulary Fixingbug Of Dimensions

    131. Word Semantics: Cosine Similarity Word2Vec Embeddings

    132. Word Semantics: Words Analogies

    133. Word Semantics: Words Analogies Implemenation 1

    134. Word Semantics: Words Analogies Implemenation 2

    135. Word Semantics: Words Visualizations

    136. Word Semantics: Words Visualizations Implementaion

    137. Word Semantics: Words Visualizations Implementaion 2

    138. Word2vec: Static And Dynamic Embeddings

    139. Word2vec: Self Supervision

    140. Word2vec: Word2Vec Algorithm Abstract

    141. Word2vec: Word2Vec Why Negative Sampling

    142. Word2vec: Word2Vec What Is Skip Gram

    143. Word2vec: Word2Vec How To Define Probability Law

    144. Word2vec: Word2Vec Sigmoid

    145. Word2vec: Word2Vec Formalizing Loss Function

    146. Word2vec: Word2Vec Loss Function

    147. Word2vec: Word2Vec Gradient Descent Step

    148. Word2vec: Word2Vec Implemenation Preparing Data

    149. Word2vec: Word2Vec Implemenation Gradient Step

    150. Word2vec: Word2Vec Implemenation Driver Function

    151. Need of Deep Learning for NLP: Why RNNs For NLP

    152. Need of Deep Learning for NLP: Pytorch Installation And Tensors Introduction

    153. Need of Deep Learning for NLP: Automatic Diffrenciation Pytorch

    154. Introduction(NLP with Deep Learning DNN): Why DNNs In Machine Learning

    155. Introduction(NLP with Deep Learning DNN): Representational Power And Data Utilization Capacity Of DNN

    156. Introduction(NLP with Deep Learning DNN): Perceptron

    157. Introduction(NLP with Deep Learning DNN): Perceptron Implementation

    158. Introduction(NLP with Deep Learning DNN): DNN Architecture

    159. Introduction(NLP with Deep Learning DNN): DNN Forwardstep Implementation

    160. Introduction(NLP with Deep Learning DNN): DNN Why Activation Function Is Require

    161. Introduction(NLP with Deep Learning DNN): DNN Properties Of Activation Function

    162. Introduction(NLP with Deep Learning DNN): DNN Activation Functions In Pytorch

    163. Introduction(NLP with Deep Learning DNN): DNN What Is Loss Function

    164. Introduction(NLP with Deep Learning DNN): DNN Loss Function In Pytorch

    165. Training(NLP with DNN): DNN Gradient Descent

    166. Training(NLP with DNN): DNN Gradient Descent Implementation

    167. Training(NLP with DNN): DNN Gradient Descent Stochastic Batch Minibatch

    168. Training(NLP with DNN): DNN Gradient Descent Summary

    169. Training(NLP with DNN): DNN Implemenation Gradient Step

    170. Training(NLP with DNN): DNN Implemenation Stochastic Gradient Descent

    171. Training(NLP with DNN): DNN Implemenation Batch Gradient Descent

    172. Training(NLP with DNN): DNN Implemenation Minibatch Gradient Descent

    173. Training(NLP with DNN): DNN Implemenation In Pytorch

    174. Hyper parameters(NLP with DNN): DNN Weights Initializations

    175. Hyper parameters(NLP with DNN): DNN Learning Rate

    176. Hyper parameters(NLP with DNN): DNN Batch Normalization

    177. Hyper parameters(NLP with DNN): DNN Batch Normalization Implementation

    178. Hyper parameters(NLP with DNN): DNN Optimizations

    179. Hyper parameters(NLP with DNN): DNN Dropout

    180. Hyper parameters(NLP with DNN): DNN Dropout In Pytorch

    181. Hyper parameters(NLP with DNN): DNN Early Stopping

    182. Hyper parameters(NLP with DNN): DNN Hyperparameters

    183. Hyper parameters(NLP with DNN): DNN Pytorch CIFAR10 Example

    184. Introduction(NLP with Deep Learning RNN): What Is RNN

    185. Introduction(NLP with Deep Learning RNN): Understanding RNN With A Simple Example

    186. Introduction(NLP with Deep Learning RNN): RNN Applications Human Activity Recognition

    187. Introduction(NLP with Deep Learning RNN): RNN Applications Image Captioning

    188. Introduction(NLP with Deep Learning RNN): RNN Applications Machine Translation

    189. Introduction(NLP with Deep Learning RNN): RNN Applications Machine Translation

    190. Introduction(NLP with Deep Learning RNN): RNN Applications Speech Recognition Stock Price Prediction

    191. Introduction(NLP with Deep Learning RNN): RNN Models

    192. Mini-project Language Modelling: Language Modeling Next Word Prediction

    193. Mini-project Language Modelling: Language Modeling Next Word Prediction Vocabulary Index

    194. Mini-project Language Modelling: Language Modeling Next Word Prediction Vocabulary Index Embeddings

    195. Mini-project Language Modelling: Language Modeling Next Word Prediction Rnn Architecture

    196. Mini-project Language Modelling: Language Modeling Next Word Prediction Python 1

    197. Mini-project Language Modelling: Language Modeling Next Word Prediction Python 2

    198. Mini-project Language Modelling: Language Modeling Next Word Prediction Python 3

    199. Mini-project Language Modelling: Language Modeling Next Word Prediction Python 4

    200. Mini-project Language Modelling: Language Modeling Next Word Prediction Python 5

    201. Mini-project Language Modelling: Language Modeling Next Word Prediction Python 6

    202. Mini-project Sentiment Classification: Vocabulary Implementation

    203. Mini-project Sentiment Classification: Vocabulary Implementation Helpers

    204. Mini-project Sentiment Classification: Vocabulary Implementation From File

    205. Mini-project Sentiment Classification: Vectorizer

    206. Mini-project Sentiment Classification: RNN Setup

    207. Mini-project Sentiment Classification: RNN Setup 1

    208. RNN in PyTorch: RNN In Pytorch Introduction

    209. RNN in PyTorch: RNN In Pytorch Embedding Layer

    210. RNN in PyTorch: RNN In Pytorch Nn Rnn

    211. RNN in PyTorch: RNN In Pytorch Output Shapes

    212. RNN in PyTorch: RNN In Pytorch Gatedunits

    213. RNN in PyTorch: RNN In Pytorch Gatedunits GRU LSTM

    214. RNN in PyTorch: RNN In Pytorch Bidirectional RNN

    215. RNN in PyTorch: RNN In Pytorch Bidirectional RNN Output Shapes

    216. RNN in PyTorch: RNN In Pytorch Bidirectional RNN Output Shapes Seperation

    217. RNN in PyTorch: RNN In Pytorch Example

    218. Advanced RNN models: RNN Encoder Decoder

    219. Advanced RNN models: RNN Attention

    220. Neural Machine Translation: Introduction To Dataset And Packages

    221. Neural Machine Translation: Implementing Language Class

    222. Neural Machine Translation: Testing Language Class And Implementing Normalization

    223. Neural Machine Translation: Reading Datafile

    224. Neural Machine Translation: Reading Building Vocabulary

    225. Neural Machine Translation: EncoderRNN

    226. Neural Machine Translation: DecoderRNN

    227. Neural Machine Translation: DecoderRNN Forward Step

    228. Neural Machine Translation: DecoderRNN Helper Functions

    229. Neural Machine Translation: Training Module

    230. Neural Machine Translation: Stochastic Gradient Descent

    231. Neural Machine Translation: NMT Training

    232. Neural Machine Translation: NMT Evaluation

    1. Introduction: Course and Instructor Introduction

    2. Introduction: AI Sciences Introduction

    3. Introduction: Course Description

    4. Fundamentals of Chatbots for Deep Learning: Module Introduction

    5. Fundamentals of Chatbots for Deep Learning: Conventional vs AI Chatbots

    6. Fundamentals of Chatbots for Deep Learning: Geneative vs Retrievel Chatbots

    7. Fundamentals of Chatbots for Deep Learning: Benifits of Deep Learning Chatbots

    8. Fundamentals of Chatbots for Deep Learning: Chatbots in Medical Domain

    9. Fundamentals of Chatbots for Deep Learning: Chatbots in Business

    10. Fundamentals of Chatbots for Deep Learning: Chatbots in E-Commerce

    11. Deep Learning Based Chatbot Architecture and Develpment: Module Introduction

    12. Deep Learning Based Chatbot Architecture and Develpment: Deep Learning Architecture

    13. Deep Learning Based Chatbot Architecture and Develpment: Encoder Decoder

    14. Deep Learning Based Chatbot Architecture and Develpment: Steps Involved

    15. Deep Learning Based Chatbot Architecture and Develpment: Project Overview and Packages

    16. Deep Learning Based Chatbot Architecture and Develpment: Importing Libraries

    17. Deep Learning Based Chatbot Architecture and Develpment: Data Prepration

    18. Deep Learning Based Chatbot Architecture and Develpment: Develop Vocabulary

    19. Deep Learning Based Chatbot Architecture and Develpment: Max Story and Question Length

    20. Deep Learning Based Chatbot Architecture and Develpment: Tokenizer

    21. Deep Learning Based Chatbot Architecture and Develpment: Separation and Sequence

    22. Deep Learning Based Chatbot Architecture and Develpment: Vectorize Stories

    23. Deep Learning Based Chatbot Architecture and Develpment: Vectorizing Train and Test Data

    24. Deep Learning Based Chatbot Architecture and Develpment: Encoding

    25. Deep Learning Based Chatbot Architecture and Develpment: Answer and Response

    26. Deep Learning Based Chatbot Architecture and Develpment: Model Completion

    27. Deep Learning Based Chatbot Architecture and Develpment: Predictions

    1. Introduction: Course Outline

    2. Deep Learning Foundation for Recommender Systems: Module Introduction

    3. Deep Learning Foundation for Recommender Systems: Overview

    4. Deep Learning Foundation for Recommender Systems: Deep Learning in Recommendation Systems

    5. Deep Learning Foundation for Recommender Systems: Inference After Training

    6. Deep Learning Foundation for Recommender Systems: Inference Mechanism

    7. Deep Learning Foundation for Recommender Systems: Embeddings and User Context

    8. Deep Learning Foundation for Recommender Systems: Neutral Collaborative Filterin

    9. Deep Learning Foundation for Recommender Systems: VAE Collaborative Filtering

    10. Deep Learning Foundation for Recommender Systems: Strengths and Weaknesses of DL Models

    11. Deep Learning Foundation for Recommender Systems: Deep Learning Quiz

    12. Deep Learning Foundation for Recommender Systems: Deep Learning Quiz Solution

    13. Project Amazon Product Recommendation System: Module Overview

    14. Project Amazon Product Recommendation System: TensorFlow Recommenders

    15. Project Amazon Product Recommendation System: Two Tower Model

    16. Project Amazon Product Recommendation System: Project Overview

    17. Project Amazon Product Recommendation System: Download Libraries

    18. Project Amazon Product Recommendation System: Data Visualization with WordCloud

    19. Project Amazon Product Recommendation System: Make Tensors from DataFrame

    20. Project Amazon Product Recommendation System: Rating Our Data

    21. Project Amazon Product Recommendation System: Random Train-Test Split

    22. Project Amazon Product Recommendation System: Making the Model and Query Tower

    23. Project Amazon Product Recommendation System: Candidate Tower and Retrieval System

    24. Project Amazon Product Recommendation System: Compute Loss

    25. Project Amazon Product Recommendation System: Train and Validation

    26. Project Amazon Product Recommendation System: Accuracy vs Recommendations

    27. Project Amazon Product Recommendation System: Making Recommendations

About this course

  • $199.99
  • 659 lessons
  • 64.5 hours of video content