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Deep Learning: Python Deep Learning Masterclass

سرفصل های دوره

Unlock the Secrets of Deep Learning: Dive Deep into CNNs, RNNs, NLP, Chatbots, and Recommender Systems - Deep Learning


1 - Introduction
  • 1 - Links for the Courses Materials and Codes.html

  • 2 - Deep LearningDeep Neural Network for Beginners Using Python
  • 2 - Introduction Introduction to Instructor
  • 3 - Introduction Introduction to Course
  • 4 - Basics of Deep Learning Problem to Solve Part 1
  • 5 - Basics of Deep Learning Problem to Solve Part 2
  • 6 - Basics of Deep Learning Problem to Solve Part 3
  • 7 - Basics of Deep Learning Linear Equation
  • 8 - Basics of Deep Learning Linear Equation Vectorized
  • 9 - Basics of Deep Learning 3D Feature Space
  • 10 - Basics of Deep Learning N Dimensional Space
  • 11 - Basics of Deep Learning Theory of Perceptron
  • 12 - Basics of Deep Learning Implementing Basic Perceptron
  • 13 - Basics of Deep Learning Logical Gates for Perceptrons
  • 14 - Basics of Deep Learning Perceptron Training Part 1
  • 15 - Basics of Deep Learning Perceptron Training Part 2
  • 16 - Basics of Deep Learning Learning Rate
  • 17 - Basics of Deep Learning Perceptron Training Part 3
  • 18 - Basics of Deep Learning Perceptron Algorithm
  • 19 - Basics of Deep Learning Coading Perceptron Algo Data Reading Visualization
  • 20 - Basics of Deep Learning Coading Perceptron Algo Perceptron Step
  • 21 - Basics of Deep Learning Coading Perceptron Algo Training Perceptron
  • 22 - Basics of Deep Learning Coading Perceptron Algo Visualizing the Results
  • 23 - Basics of Deep Learning Problem with Linear Solutions
  • 24 - Basics of Deep Learning Solution to Problem
  • 25 - Basics of Deep Learning Error Functions
  • 26 - Basics of Deep Learning Discrete vs Continuous Error Function
  • 27 - Basics of Deep Learning Sigmoid Function
  • 28 - Basics of Deep Learning MultiClass Problem
  • 29 - Basics of Deep Learning Problem of Negative Scores
  • 30 - Basics of Deep Learning Need of Softmax
  • 31 - Basics of Deep Learning Coding Softmax
  • 32 - Basics of Deep Learning One Hot Encoding
  • 33 - Basics of Deep Learning Maximum Likelihood Part 1
  • 34 - Basics of Deep Learning Maximum Likelihood Part 2
  • 35 - Basics of Deep Learning Cross Entropy
  • 36 - Basics of Deep Learning Cross Entropy Formulation
  • 37 - Basics of Deep Learning Multi Class Cross Entropy
  • 38 - Basics of Deep Learning Cross Entropy Implementation
  • 39 - Basics of Deep Learning Sigmoid Function Implementation
  • 40 - Basics of Deep Learning Output Function Implementation
  • 41 - Deep Learning Introduction to Gradient Decent
  • 42 - Deep Learning Convex Functions
  • 43 - Deep Learning Use of Derivatives
  • 44 - Deep Learning How Gradient Decent Works
  • 45 - Deep Learning Gradient Step
  • 46 - Deep Learning Logistic Regression Algorithm
  • 47 - Deep Learning Data Visualization and Reading
  • 48 - Deep Learning Updating Weights in Python
  • 49 - Deep Learning Implementing Logistic Regression
  • 50 - Deep Learning Visualization and Results
  • 51 - Deep Learning Gradient Decent vs Perceptron
  • 52 - Deep Learning Linear to Non Linear Boundaries
  • 53 - Deep Learning Combining Probabilities
  • 54 - Deep Learning Weighted Sums
  • 55 - Deep Learning Neural Network Architecture
  • 56 - Deep Learning Layers and DEEP Networks
  • 57 - Deep LearningMulti Class Classification
  • 58 - Deep Learning Basics of Feed Forward
  • 59 - Deep Learning Feed Forward for DEEP Net
  • 60 - Deep Learning Deep Learning Algo Overview
  • 61 - Deep Learning Basics of Back Propagation
  • 62 - Deep Learning Updating Weights
  • 63 - Deep Learning Chain Rule for BackPropagation
  • 64 - Deep Learning Sigma Prime
  • 65 - Deep Learning Data Analysis NN Implementation
  • 66 - Deep Learning One Hot Encoding NN Implementation
  • 67 - Deep Learning Scaling the Data NN Implementation
  • 68 - Deep Learning Splitting the Data NN Implementation
  • 69 - Deep Learning Helper Functions NN Implementation
  • 70 - Deep Learning Training NN Implementation
  • 71 - Deep Learning Testing NN Implementation
  • 72 - Optimizations Underfitting vs Overfitting
  • 73 - Optimizations Early Stopping
  • 74 - Optimizations Quiz
  • 75 - Optimizations Solution Regularization
  • 76 - Optimizations L1 L2 Regularization
  • 77 - Optimizations Dropout
  • 78 - Optimizations Local Minima Problem
  • 79 - Optimizations Random Restart Solution
  • 81 - Optimizations Other Activation Functions
  • 82 - Final Project Final Project Part 1
  • 83 - Final Project Final Project Part 2
  • 84 - Final Project Final Project Part 3
  • 85 - Final Project Final Project Part 4
  • 86 - Final Project Final Project Part 5

  • 3 - Deep Learning CNN Convolutional Neural Networks with Python
  • 87 - Link to Github to get the Python Notebooks.html
  • 88 - Introduction Instructor Introduction
  • 89 - Introduction Why CNN
  • 90 - Introduction Focus of the Course
  • 91 - Image Processing Gray Scale Images
  • 92 - Image Processing Gray Scale Images Quiz
  • 93 - Image Processing Gray Scale Images Solution
  • 94 - Image Processing RGB Images
  • 95 - Image Processing RGB Images Quiz
  • 96 - Image Processing RGB Images Solution
  • 97 - Image Processing Reading and Showing Images in Python
  • 98 - Image Processing Reading and Showing Images in Python Quiz
  • 99 - Image Processing Reading and Showing Images in Python Solution
  • 100 - Image Processing Converting an Image to Grayscale in Python
  • 101 - Image Processing Converting an Image to Grayscale in Python Quiz
  • 102 - Image Processing Converting an Image to Grayscale in Python Solution
  • 103 - Image Processing Image Formation
  • 104 - Image Processing Image Formation Quiz
  • 105 - Image Processing Image Formation Solution
  • 106 - Image Processing Image Blurring 1
  • 107 - Image Processing Image Blurring 1 Quiz
  • 108 - Image Processing Image Blurring 1 Solution
  • 109 - Image Processing Image Blurring 2
  • 110 - Image Processing Image Blurring 2 Quiz
  • 111 - Image Processing Image Blurring 2 Solution
  • 112 - Image Processing General Image Filtering
  • 113 - Image Processing Convolution
  • 114 - Image Processing Edge Detection
  • 115 - Image Processing Image Sharpening
  • 116 - Image Processing Implementation of Image Blurring Edge Detection Image Sharpening in Python
  • 117 - Image Processing Parameteric Shape Detection
  • 118 - Image Processing Image Processing Activity
  • 119 - Image Processing Image Processing Activity Solution
  • 120 - Object Detection Introduction to Object Detection
  • 121 - Object Detection Classification PipleLine
  • 122 - Object Detection Classification PipleLine Quiz
  • 123 - Object Detection Classification PipleLine Solution
  • 124 - Object Detection Sliding Window Implementation
  • 125 - Object Detection Shift Scale Rotation Invariance
  • 126 - Object Detection Shift Scale Rotation Invariance Exercise
  • 127 - Object Detection Person Detection
  • 128 - Object Detection HOG Features
  • 129 - Object Detection HOG Features Exercise
  • 130 - Object Detection Hand Engineering vs CNNs
  • 131 - Object Detection Object Detection Activity
  • 132 - Deep Neural Network Overview Neuron and Perceptron
  • 133 - Deep Neural Network Overview DNN Architecture
  • 134 - Deep Neural Network Overview DNN Architecture Quiz
  • 135 - Deep Neural Network Overview DNN Architecture Solution
  • 136 - Deep Neural Network Overview FeedForward FullyConnected MLP
  • 137 - Deep Neural Network Overview Calculating Number of Weights of DNN
  • 138 - Deep Neural Network Overview Calculating Number of Weights of DNN Quiz
  • 139 - Deep Neural Network Overview Calculating Number of Weights of DNN Solution
  • 140 - Deep Neural Network Overview Number of Nuerons vs Number of Layers
  • 141 - Deep Neural Network Overview Discriminative vs Generative Learning
  • 142 - Deep Neural Network Overview Universal Approximation Therorem
  • 143 - Deep Neural Network Overview Why Depth
  • 144 - Deep Neural Network Overview Decision Boundary in DNN
  • 145 - Deep Neural Network Overview Decision Boundary in DNN Quiz
  • 146 - Deep Neural Network Overview Decision Boundary in DNN Solution
  • 147 - Deep Neural Network Overview BiasTerm
  • 148 - Deep Neural Network Overview BiasTerm Quiz
  • 149 - Deep Neural Network Overview BiasTerm Solution
  • 150 - Deep Neural Network Overview Activation Function
  • 151 - Deep Neural Network Overview Activation Function Quiz
  • 152 - Deep Neural Network Overview Activation Function Solution
  • 153 - Deep Neural Network Overview DNN Training Parameters
  • 154 - Deep Neural Network Overview DNN Training Parameters Quiz
  • 155 - Deep Neural Network Overview DNN Training Parameters Solution
  • 156 - Deep Neural Network Overview Gradient Descent
  • 157 - Deep Neural Network Overview BackPropagation
  • 158 - Deep Neural Network Overview Training DNN Animantion
  • 159 - Deep Neural Network Overview Weigth Initialization
  • 160 - Deep Neural Network Overview Weigth Initialization Quiz
  • 161 - Deep Neural Network Overview Weigth Initialization Solution
  • 162 - Deep Neural Network Overview Batch miniBatch Stocastic Gradient Descent
  • 163 - Deep Neural Network Overview Batch Normalization
  • 164 - Deep Neural Network Overview Rprop and Momentum
  • 165 - Deep Neural Network Overview Rprop and Momentum Quiz
  • 166 - Deep Neural Network Overview Rprop and Momentum Solution
  • 167 - Deep Neural Network Overview Convergence Animation
  • 168 - Deep Neural Network Overview DropOut Early Stopping and Hyperparameters
  • 169 - Deep Neural Network Overview DropOut Early Stopping and Hyperparameters Quiz
  • 170 - Deep Neural Network Overview DropOut Early Stopping and Hyperparameters Solution
  • 171 - Deep Neural Network Architecture Convolution Revisited
  • 172 - Deep Neural Network Architecture Implementing Convolution in Python Revisited
  • 173 - Deep Neural Network Architecture Why Convolution
  • 174 - Deep Neural Network Architecture Filters Padding Strides
  • 175 - Deep Neural Network Architecture Padding Image
  • 176 - Deep Neural Network Architecture Pooling Tensors
  • 177 - Deep Neural Network Architecture CNN Example
  • 178 - Deep Neural Network Architecture Convolution and Pooling Details
  • 179 - Deep Neural Network Architecture Maxpooling Exercise
  • 180 - Deep Neural Network Architecture NonVectorized Implementations of Conv2d and Pool2d
  • 181 - Deep Neural Network Architecture Deep Neural Network Architecture Activity
  • 182 - Gradient Descent in CNNs Example Setup
  • 183 - Gradient Descent in CNNs Why Derivaties
  • 184 - Gradient Descent in CNNs Why Derivaties Quiz
  • 185 - Gradient Descent in CNNs Why Derivaties Solution
  • 186 - Gradient Descent in CNNs What is Chain Rule
  • 187 - Gradient Descent in CNNs Applying Chain Rule
  • 188 - Gradient Descent in CNNs Gradients of MaxPooling Layer
  • 189 - Gradient Descent in CNNs Gradients of MaxPooling Layer Quiz
  • 190 - Gradient Descent in CNNs Gradients of MaxPooling Layer Solution
  • 191 - Gradient Descent in CNNs Gradients of Convolutional Layer
  • 192 - Gradient Descent in CNNs Extending To Multiple Filters
  • 193 - Gradient Descent in CNNs Extending to Multiple Layers
  • 194 - Gradient Descent in CNNs Extending to Multiple Layers Quiz
  • 195 - Gradient Descent in CNNs Extending to Multiple Layers Solution
  • 196 - Gradient Descent in CNNs Implementation in Numpy ForwardPass
  • 197 - Gradient Descent in CNNs Implementation in Numpy BackwardPass 1
  • 198 - Gradient Descent in CNNs Implementation in Numpy BackwardPass 2
  • 199 - Gradient Descent in CNNs Implementation in Numpy BackwardPass 3
  • 200 - Gradient Descent in CNNs Implementation in Numpy BackwardPass 4
  • 201 - Gradient Descent in CNNs Implementation in Numpy BackwardPass 5
  • 202 - Gradient Descent in CNNs Gradient Descent in CNNs Activity
  • 203 - Introduction to TensorFlow Introduction
  • 204 - Introduction to TensorFlow FashionMNIST Example Plan Neural Network
  • 205 - Introduction to TensorFlow FashionMNIST Example CNN
  • 206 - Introduction to TensorFlow Introduction to TensorFlow Activity
  • 207 - Classical CNNs LeNet
  • 208 - Classical CNNs LeNet Quiz
  • 209 - Classical CNNs LeNet Solution
  • 210 - Classical CNNs AlexNet
  • 211 - Classical CNNs VGG
  • 212 - Classical CNNs InceptionNet
  • 213 - Classical CNNs GoogLeNet
  • 214 - Classical CNNs Resnet
  • 215 - Classical CNNs Classical CNNs Activity
  • 216 - Transfer Learning What is Transfer learning
  • 217 - Transfer Learning Why Transfer Learning
  • 218 - Transfer Learning Practical Tips
  • 219 - Transfer Learning Project in TensorFlow
  • 220 - Transfer Learning ImageNet Challenge
  • 221 - Transfer Learning Transfer Learning Activity
  • 222 - Yolo Image Classfication Revisited
  • 223 - Yolo Sliding Window Object Localization
  • 224 - Yolo Sliding Window Efficient Implementation
  • 225 - Yolo Yolo Introduction
  • 226 - Yolo Yolo Training Data Generation
  • 227 - Yolo Yolo Anchor Boxes
  • 228 - Yolo Yolo Algorithm
  • 229 - Yolo Yolo Non Maxima Supression
  • 230 - Yolo RCNN
  • 231 - Yolo Yolo Activity
  • 232 - Face Verification Problem Setup
  • 233 - Face Verification Project Implementation
  • 234 - Face Verification Face Verification Activity
  • 235 - Neural Style Transfer Problem Setup
  • 236 - Neural Style Transfer Implementation Tensorflow Hub

  • 4 - Deep Learning Recurrent Neural Networks with Python
  • 237 - Link to oneDrive and Github to get the Python Notebooks.html
  • 238 - Introduction Introduction to Instructor and Aisciences
  • 239 - Introduction Introduction To Instructor
  • 240 - Introduction Focus of the Course
  • 241 - Applications of RNN Motivation Human Activity Recognition
  • 242 - Applications of RNN Motivation Image Captioning
  • 243 - Applications of RNN Motivation Machine Translation
  • 244 - Applications of RNN Motivation Speech Recognition
  • 245 - Applications of RNN Motivation Stock Price Predictions
  • 246 - Applications of RNN Motivation When to Model RNN
  • 247 - Applications of RNN Motivation Activity
  • 248 - DNN Overview Why PyTorch
  • 249 - DNN Overview PyTorch Installation and Tensors Introduction
  • 250 - DNN Overview Automatic Diffrenciation Pytorch New
  • 251 - DNN Overview Why DNNs in Machine Learning
  • 252 - DNN Overview Representational Power and Data Utilization Capacity of DNN
  • 253 - DNN Overview Perceptron
  • 254 - DNN Overview Perceptron Exercise
  • 255 - DNN Overview Perceptron Exercise Solution
  • 256 - DNN Overview Perceptron Implementation
  • 257 - DNN Overview DNN Architecture
  • 258 - DNN Overview DNN Architecture Exercise
  • 259 - DNN Overview DNN Architecture Exercise Solution
  • 260 - DNN Overview DNN ForwardStep Implementation
  • 261 - DNN Overview DNN Why Activation Function Is Required
  • 262 - DNN Overview DNN Why Activation Function Is Required Exercise
  • 263 - DNN Overview DNN Why Activation Function Is Required Exercise Solution
  • 264 - DNN Overview DNN Properties Of Activation Function
  • 265 - DNN Overview DNN Activation Functions In Pytorch
  • 266 - DNN Overview DNN What Is Loss Function
  • 267 - DNN Overview DNN What Is Loss Function Exercise
  • 268 - DNN Overview DNN What Is Loss Function Exercise Solution
  • 269 - DNN Overview DNN What Is Loss Function Exercise 02
  • 270 - DNN Overview DNN What Is Loss Function Exercise 02 Solution
  • 271 - DNN Overview DNN Loss Function In Pytorch
  • 272 - DNN Overview DNN Gradient Descent
  • 273 - DNN Overview DNN Gradient Descent Exercise
  • 274 - DNN Overview DNN Gradient Descent Exercise Solution
  • 275 - DNN Overview DNN Gradient Descent Implementation
  • 276 - DNN Overview DNN Gradient Descent Stochastic Batch Minibatch
  • 277 - DNN Overview DNN Implemenation Gradient Step
  • 278 - DNN Overview DNN Implemenation Stochastic Gradient Descent
  • 279 - DNN Overview DNN Gradient Descent Summary
  • 280 - DNN Overview DNN Implemenation Batch Gradient Descent
  • 281 - DNN Overview DNN Implemenation Minibatch Gradient Descent
  • 282 - DNN Overview DNN Implemenation In PyTorch
  • 283 - DNN Overview DNN Weights Initializations
  • 284 - DNN Overview DNN Learning Rate
  • 285 - DNN Overview DNN Batch Normalization
  • 286 - DNN Overview DNN batch Normalization Implementation
  • 287 - DNN Overview DNN Optimizations
  • 288 - DNN Overview DNN Dropout
  • 289 - DNN Overview DNN Dropout In PyTorch
  • 290 - DNN Overview DNN Early Stopping
  • 291 - DNN Overview DNN Hyperparameters
  • 292 - DNN Overview DNN Pytorch CIFAR10 Example
  • 293 - RNN Architecture Introduction to Module
  • 294 - RNN Architecture Fixed Length Memory Model
  • 295 - RNN Architecture Fixed Length Memory Model Exercise
  • 296 - RNN Architecture Fixed Length Memory Model Exercise Solution Part 01
  • 297 - RNN Architecture Fixed Length Memory Model Exercise Solution Part 02
  • 298 - RNN Architecture Infinite Memory Architecture
  • 299 - RNN Architecture Infinite Memory Architecture Exercise
  • 300 - RNN Architecture Infinite Memory Architecture Solution
  • 301 - RNN Architecture Weight Sharing
  • 302 - RNN Architecture Notations
  • 303 - RNN Architecture ManyToMany Model
  • 304 - RNN Architecture ManyToMany Model Exercise 01
  • 305 - RNN Architecture ManyToMany Model Solution 01
  • 306 - RNN Architecture ManyToMany Model Exercise 02
  • 307 - RNN Architecture ManyToMany Model Solution 02
  • 308 - RNN Architecture ManyToOne Model
  • 309 - RNN Architecture OneToMany Model Exercise
  • 310 - RNN Architecture OneToMany Model Solution
  • 311 - RNN Architecture OneToMany Model
  • 312 - RNN Architecture ManyToOne Model Exercise
  • 313 - RNN Architecture ManyToOne Model Solution
  • 314 - RNN Architecture Activity Many to One
  • 315 - RNN Architecture Activity Many to One Exercise
  • 316 - RNN Architecture Activity Many to One Solution
  • 317 - RNN Architecture ManyToMany Different Sizes Model
  • 318 - RNN Architecture Activity Many to Many Nmt
  • 319 - RNN Architecture Models Summary
  • 320 - RNN Architecture Deep RNNs
  • 321 - RNN Architecture Deep RNNs Exercise
  • 322 - RNN Architecture Deep RNNs Solution
  • 323 - Gradient Decsent in RNN Introduction to Gradient Descent Module
  • 324 - Gradient Decsent in RNN Example Setup
  • 325 - Gradient Decsent in RNN Equations
  • 326 - Gradient Decsent in RNN Equations Exercise
  • 327 - Gradient Decsent in RNN Equations Solution
  • 328 - Gradient Decsent in RNN Loss Function
  • 329 - Gradient Decsent in RNN Why Gradients
  • 330 - Gradient Decsent in RNN Why Gradients Exercise
  • 331 - Gradient Decsent in RNN Why Gradients Solution
  • 332 - Gradient Decsent in RNN Chain Rule
  • 333 - Gradient Decsent in RNN Chain Rule in Action
  • 334 - Gradient Decsent in RNN BackPropagation Through Time
  • 335 - Gradient Decsent in RNN Activity
  • 336 - RNN implementation Automatic Diffrenciation
  • 337 - RNN implementation Automatic Diffrenciation Pytorch
  • 338 - RNN implementation Language Modeling Next Word Prediction Vocabulary Index
  • 339 - RNN implementation Language Modeling Next Word Prediction Vocabulary Index Embeddings
  • 340 - RNN implementation Language Modeling Next Word Prediction RNN Architecture
  • 341 - RNN implementation Language Modeling Next Word Prediction Python 1
  • 342 - RNN implementation Language Modeling Next Word Prediction Python 2
  • 343 - RNN implementation Language Modeling Next Word Prediction Python 3
  • 344 - RNN implementation Language Modeling Next Word Prediction Python 4
  • 345 - RNN implementation Language Modeling Next Word Prediction Python 5
  • 346 - RNN implementation Language Modeling Next Word Prediction Python 6
  • 347 - Sentiment Classification using RNN Vocabulary Implementation
  • 348 - Sentiment Classification using RNN Vocabulary Implementation Helpers
  • 349 - Sentiment Classification using RNN Vocabulary Implementation From File
  • 350 - Sentiment Classification using RNN Vectorizer
  • 351 - Sentiment Classification using RNN RNN Setup 1
  • 352 - Sentiment Classification using RNN RNN Setup 2
  • 353 - Sentiment Classification using RNN WhatNext
  • 354 - Vanishing Gradients in RNN Introduction to Better RNNs Module
  • 355 - Vanishing Gradients in RNN Introduction Vanishing Gradients in RNN
  • 356 - Vanishing Gradients in RNN GRU
  • 357 - Vanishing Gradients in RNN GRU Optional
  • 358 - Vanishing Gradients in RNN LSTM
  • 359 - Vanishing Gradients in RNN LSTM Optional
  • 360 - Vanishing Gradients in RNN Bidirectional RNN
  • 361 - Vanishing Gradients in RNN Attention Model
  • 362 - Vanishing Gradients in RNN Attention Model Optional
  • 363 - TensorFlow Introduction to TensorFlow
  • 364 - TensorFlow TensorFlow Text Classification Example using RNN
  • 365 - Project I Book Writer Introduction
  • 366 - Project I Book Writer Data Mapping
  • 367 - Project I Book Writer Modling RNN Architecture
  • 368 - Project I Book Writer Modling RNN Model in TensorFlow
  • 369 - Project I Book Writer Modling RNN Model Training
  • 370 - Project I Book Writer Modling RNN Model Text Generation
  • 371 - Project I Book Writer Activity
  • 372 - Project II Stock Price Prediction Problem Statement
  • 373 - Project II Stock Price Prediction Data Set
  • 374 - Project II Stock Price Prediction Data Prepration
  • 375 - Project II Stock Price Prediction RNN Model Training and Evaluation
  • 376 - Project II Stock Price Prediction Activity
  • 377 - Further Readings and Resourses Further Readings and Resourses 1

  • 5 - NLPNatural Language Processing in PythonTheory Projects
  • 378 - Links for the Courses Materials and Codes.html
  • 379 - Introduction Introduction to Course
  • 380 - Introduction Introduction to Instructor
  • 381 - Introduction Introduction to CoInstructor
  • 382 - Introduction Course Introduction
  • 383 - IntroductionRegular Expressions What Is Regular Expression
  • 384 - IntroductionRegular Expressions Why Regular Expression
  • 385 - IntroductionRegular Expressions ELIZA Chatbot
  • 386 - IntroductionRegular Expressions Python Regular Expression Package
  • 387 - Meta CharactersRegular Expressions Meta Characters
  • 388 - Meta CharactersRegular Expressions Meta Characters Bigbrackets Exercise
  • 389 - Meta CharactersRegular Expressions Meta Characters Bigbrackets Exercise Solution
  • 390 - Meta CharactersRegular Expressions Meta Characters Bigbrackets Exercise 2
  • 391 - Meta CharactersRegular Expressions Meta Characters Bigbrackets Exercise 2 Solution
  • 392 - Meta CharactersRegular Expressions Meta Characters Cap
  • 393 - Meta CharactersRegular Expressions Meta Characters Cap Exercise 3
  • 394 - Meta CharactersRegular Expressions Meta Characters Cap Exercise 3 Solution
  • 395 - Meta CharactersRegular Expressions Backslash
  • 396 - Meta CharactersRegular Expressions Backslash Continued
  • 397 - Meta CharactersRegular Expressions Backslash Continued 01
  • 398 - Meta CharactersRegular Expressions Backslash Squared Brackets Exercise
  • 399 - Meta CharactersRegular Expressions Backslash Squared Brackets Exercise Solution
  • 400 - Meta CharactersRegular Expressions Backslash Squared Brackets Exercise Another Solution
  • 401 - Meta CharactersRegular Expressions Backslash Exercise
  • 402 - Meta CharactersRegular Expressions Backslash Exercise Solution And Special Sequences Exercise
  • 403 - Meta CharactersRegular Expressions Solution And Special Sequences Exercise Solution
  • 404 - Meta CharactersRegular Expressions Meta Character Asterisk
  • 405 - Meta CharactersRegular Expressions Meta Character Asterisk Exercise
  • 406 - Meta CharactersRegular Expressions Meta Character Asterisk Exercise Solution
  • 407 - Meta CharactersRegular Expressions Meta Character Asterisk Homework
  • 408 - Meta CharactersRegular Expressions Meta Character Asterisk Greedymatching
  • 409 - Meta CharactersRegular Expressions Meta Character Plus And Questionmark
  • 410 - Meta CharactersRegular Expressions Meta Character Curly Brackets Exercise
  • 411 - Meta CharactersRegular Expressions Meta Character Curly Brackets Exercise Solution
  • 412 - Pattern Objects Pattern Objects
  • 413 - Pattern Objects Pattern Objects Match Method Exersize
  • 414 - Pattern Objects Pattern Objects Match Method Exersize Solution
  • 415 - Pattern Objects Pattern Objects Match Method Vs Search Method
  • 416 - Pattern Objects Pattern Objects Finditer Method
  • 417 - Pattern Objects Pattern Objects Finditer Method Exersize Solution
  • 418 - More Meta Characters Meta Characters Logical Or
  • 419 - More Meta Characters Meta Characters Beginning And End Patterns
  • 420 - More Meta Characters Meta Characters Paranthesis
  • 421 - String Modification String Modification
  • 422 - String Modification Word Tokenizer Using Split Method
  • 423 - String Modification Sub Method Exercise
  • 424 - String Modification Sub Method Exercise Solution
  • 425 - Words and Tokens What Is A Word
  • 426 - Words and Tokens Definition Of Word Is Task Dependent
  • 427 - Words and Tokens Vocabulary And Corpus
  • 428 - Words and Tokens Tokens
  • 429 - Words and Tokens Tokenization In Spacy
  • 430 - Sentiment Classification Yelp Reviews Classification Mini Project Introduction
  • 431 - Sentiment Classification Yelp Reviews Classification Mini Project Vocabulary Initialization
  • 432 - Sentiment Classification Yelp Reviews Classification Mini Project Adding Tokens To Vocabulary
  • 433 - Sentiment Classification Yelp Reviews Classification Mini Project Look Up Functions In Vocabulary
  • 434 - Sentiment Classification Yelp Reviews Classification Mini Project Building Vocabulary From Data
  • 435 - Sentiment Classification Yelp Reviews Classification Mini Project One Hot Encoding
  • 436 - Sentiment Classification Yelp Reviews Classification Mini Project One Hot Encoding Implementation
  • 437 - Sentiment Classification Yelp Reviews Classification Mini Project Encoding Documents
  • 438 - Sentiment Classification Yelp Reviews Classification Mini Project Encoding Documents Implementation
  • 439 - Sentiment Classification Yelp Reviews Classification Mini Project Train Test Splits
  • 440 - Sentiment Classification Yelp Reviews Classification Mini Project Featurecomputation
  • 441 - Sentiment Classification Yelp Reviews Classification Mini Project Classification
  • 442 - Language Independent Tokenization Tokenization In Detial Introduction
  • 443 - Language Independent Tokenization Tokenization Is Hard
  • 444 - Language Independent Tokenization Tokenization Byte Pair Encoding
  • 445 - Language Independent Tokenization Tokenization Byte Pair Encoding Example
  • 446 - Language Independent Tokenization Tokenization Byte Pair Encoding On Test Data
  • 447 - Language Independent Tokenization Tokenization Byte Pair Encoding Implementation Getpaircounts
  • 448 - Language Independent Tokenization Tokenization Byte Pair Encoding Implementation Mergeincorpus
  • 449 - Language Independent Tokenization Tokenization Byte Pair Encoding Implementation BFE Training
  • 450 - Language Independent Tokenization Tokenization Byte Pair Encoding Implementation BFE Encoding
  • 451 - Language Independent Tokenization Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair
  • 452 - Language Independent Tokenization Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair 1
  • 453 - Text Nomalization Word Normalization Case Folding
  • 454 - Text Nomalization Word Normalization Lematization
  • 455 - Text Nomalization Word Normalization Stemming
  • 456 - Text Nomalization Word Normalization Sentence Segmentation
  • 457 - String Matching and Spelling Correction Spelling Correction Minimum Edit Distance Intro
  • 458 - String Matching and Spelling Correction Spelling Correction Minimum Edit Distance Example
  • 459 - String Matching and Spelling Correction Spelling Correction Minimum Edit Distance Table Filling
  • 460 - String Matching and Spelling Correction Spelling Correction Minimum Edit Distance Dynamic Programming
  • 461 - String Matching and Spelling Correction Spelling Correction Minimum Edit Distance Psudocode
  • 462 - String Matching and Spelling Correction Spelling Correction Minimum Edit Distance Implementation
  • 463 - String Matching and Spelling Correction Spelling Correction Minimum Edit Distance Implementation Bugfixing
  • 464 - String Matching and Spelling Correction Spelling Correction Implementation
  • 465 - Language Modeling What Is A Language Model
  • 466 - Language Modeling Language Model Formal Definition
  • 467 - Language Modeling Language Model Curse Of Dimensionality
  • 468 - Language Modeling Language Model Markov Assumption And NGrams
  • 469 - Language Modeling Language Model Implementation Setup
  • 470 - Language Modeling Language Model Implementation Ngrams Function
  • 471 - Language Modeling Language Model Implementation Update Counts Function
  • 472 - Language Modeling Language Model Implementation Probability Model Funciton
  • 473 - Language Modeling Language Model Implementation Reading Corpus
  • 474 - Language Modeling Language Model Implementation Sampling Text
  • 475 - Topic Modelling with Word and Document Representations One Hot Vectors
  • 476 - Topic Modelling with Word and Document Representations One Hot Vectors Implementaton
  • 477 - Topic Modelling with Word and Document Representations One Hot Vectors Limitations
  • 478 - Topic Modelling with Word and Document Representations One Hot Vectors Uses As Target Labeling
  • 479 - Topic Modelling with Word and Document Representations Term Frequency For Document Representations
  • 480 - Topic Modelling with Word and Document Representations Term Frequency For Document Representations Implementations
  • 481 - Topic Modelling with Word and Document Representations Term Frequency For Word Representations
  • 482 - Topic Modelling with Word and Document Representations TFIDF For Document Representations
  • 483 - Topic Modelling with Word and Document Representations TFIDF For Document Representations Implementation Reading Corpus
  • 484 - Topic Modelling with Word and Document Representations TFIDF For Document Representations Implementation Computing Document Frequency
  • 485 - Topic Modelling with Word and Document Representations TFIDF For Document Representations Implementation Computing TFIDF
  • 486 - Topic Modelling with Word and Document Representations Topic Modeling With TFIDF 1
  • 487 - Topic Modelling with Word and Document Representations Topic Modeling With TFIDF 3
  • 488 - Topic Modelling with Word and Document Representations Topic Modeling With TFIDF 4
  • 489 - Topic Modelling with Word and Document Representations Topic Modeling With TFIDF 5
  • 490 - Topic Modelling with Word and Document Representations Topic Modeling With Gensim
  • 491 - Word Embeddings LSI Word Cooccurrence Matrix
  • 492 - Word Embeddings LSI Word Cooccurrence Matrix vs Documentterm Matrix
  • 493 - Word Embeddings LSI Word Cooccurrence Matrix Implementation Preparing Data
  • 494 - Word Embeddings LSI Word Cooccurrence Matrix Implementation Preparing Data 2
  • 495 - Word Embeddings LSI Word Cooccurrence Matrix Implementation Preparing Data Getting Vocabulary
  • 496 - Word Embeddings LSI Word Cooccurrence Matrix Implementation Final Function
  • 497 - Word Embeddings LSI Word Cooccurrence Matrix Implementation Handling Memory Issues On Large Corp
  • 498 - Word Embeddings LSI Word Cooccurrence Matrix Sparsity
  • 499 - Word Embeddings LSI Word Cooccurrence Matrix Positive Point Wise Mutual Information PPMI
  • 500 - Word Embeddings LSI PCA For Dense Embeddings
  • 501 - Word Embeddings LSI Latent Semantic Analysis
  • 502 - Word Embeddings LSI Latent Semantic Analysis Implementation
  • 503 - Word Semantics Cosine Similarity
  • 504 - Word Semantics Cosine Similarity Geting Norms Of Vectors
  • 505 - Word Semantics Cosine Similarity Normalizing Vectors
  • 506 - Word Semantics Cosine Similarity With More Than One Vectors
  • 507 - Word Semantics Cosine Similarity Getting Most Similar Words In The Vocabulary
  • 508 - Word Semantics Cosine Similarity Getting Most Similar Words In The Vocabulary Fixingbug Of D
  • 509 - Word Semantics Cosine Similarity Word2Vec Embeddings
  • 510 - Word Semantics Words Analogies
  • 511 - Word Semantics Words Analogies Implemenation 1
  • 512 - Word Semantics Words Analogies Implemenation 2
  • 513 - Word Semantics Words Visualizations
  • 514 - Word Semantics Words Visualizations Implementaion
  • 515 - Word Semantics Words Visualizations Implementaion 2
  • 516 - Word2vec Static And Dynamic Embeddings
  • 517 - Word2vec Self Supervision
  • 518 - Word2vec Word2Vec Algorithm Abstract
  • 519 - Word2vec Word2Vec Why Negative Sampling
  • 520 - Word2vec Word2Vec What Is Skip Gram
  • 521 - Word2vec Word2Vec How To Define Probability Law
  • 522 - Word2vec Word2Vec Sigmoid
  • 523 - Word2vec Word2Vec Formalizing Loss Function
  • 524 - Word2vec Word2Vec Loss Function
  • 525 - Word2vec Word2Vec Gradient Descent Step
  • 526 - Word2vec Word2Vec Implemenation Preparing Data
  • 527 - Word2vec Word2Vec Implemenation Gradient Step
  • 528 - Word2vec Word2Vec Implemenation Driver Function
  • 529 - Need of Deep Learning for NLP Why RNNs For NLP
  • 530 - Need of Deep Learning for NLP Pytorch Installation And Tensors Introduction
  • 531 - Need of Deep Learning for NLP Automatic Diffrenciation Pytorch
  • 532 - IntroductionNLP with Deep Learning DNN Why DNNs In Machine Learning
  • 533 - IntroductionNLP with Deep Learning DNN Representational Power And Data Utilization Capacity Of DNN
  • 534 - IntroductionNLP with Deep Learning DNN Perceptron
  • 535 - IntroductionNLP with Deep Learning DNN Perceptron Implementation
  • 536 - IntroductionNLP with Deep Learning DNN DNN Architecture
  • 537 - IntroductionNLP with Deep Learning DNN DNN Forwardstep Implementation
  • 538 - IntroductionNLP with Deep Learning DNN DNN Why Activation Function Is Require
  • 539 - IntroductionNLP with Deep Learning DNN DNN Properties Of Activation Function
  • 540 - IntroductionNLP with Deep Learning DNN DNN Activation Functions In Pytorch
  • 541 - IntroductionNLP with Deep Learning DNN DNN What Is Loss Function
  • 542 - IntroductionNLP with Deep Learning DNN DNN Loss Function In Pytorch
  • 543 - TrainingNLP with DNN DNN Gradient Descent
  • 544 - TrainingNLP with DNN DNN Gradient Descent Implementation
  • 545 - TrainingNLP with DNN DNN Gradient Descent Stochastic Batch Minibatch
  • 546 - TrainingNLP with DNN DNN Gradient Descent Summary
  • 547 - TrainingNLP with DNN DNN Implemenation Gradient Step
  • 548 - TrainingNLP with DNN DNN Implemenation Stochastic Gradient Descent
  • 549 - TrainingNLP with DNN DNN Implemenation Batch Gradient Descent
  • 550 - TrainingNLP with DNN DNN Implemenation Minibatch Gradient Descent
  • 551 - TrainingNLP with DNN DNN Implemenation In Pytorch
  • 552 - Hyper parametersNLP with DNN DNN Weights Initializations
  • 553 - Hyper parametersNLP with DNN DNN Learning Rate
  • 554 - Hyper parametersNLP with DNN DNN Batch Normalization
  • 555 - Hyper parametersNLP with DNN DNN Batch Normalization Implementation
  • 556 - Hyper parametersNLP with DNN DNN Optimizations
  • 557 - Hyper parametersNLP with DNN DNN Dropout
  • 558 - Hyper parametersNLP with DNN DNN Dropout In Pytorch
  • 559 - Hyper parametersNLP with DNN DNN Early Stopping
  • 560 - Hyper parametersNLP with DNN DNN Hyperparameters
  • 561 - Hyper parametersNLP with DNN DNN Pytorch CIFAR10 Example
  • 562 - IntroductionNLP with Deep Learning RNN What Is RNN
  • 563 - IntroductionNLP with Deep Learning RNN Understanding RNN With A Simple Example
  • 564 - IntroductionNLP with Deep Learning RNN RNN Applications Human Activity Recognition
  • 565 - IntroductionNLP with Deep Learning RNN RNN Applications Image Captioning
  • 566 - IntroductionNLP with Deep Learning RNN RNN Applications Machine Translation
  • 567 - IntroductionNLP with Deep Learning RNN RNN Applications Speech Recognition Stock Price Prediction
  • 568 - IntroductionNLP with Deep Learning RNN RNN Models
  • 569 - Miniproject Language Modelling Language Modeling Next Word Prediction
  • 570 - Miniproject Language Modelling Language Modeling Next Word Prediction Vocabulary Index
  • 571 - Miniproject Language Modelling Language Modeling Next Word Prediction Vocabulary Index Embeddings
  • 572 - Miniproject Language Modelling Language Modeling Next Word Prediction Rnn Architecture
  • 573 - Miniproject Language Modelling Language Modeling Next Word Prediction Python 1
  • 574 - Miniproject Language Modelling Language Modeling Next Word Prediction Python 2
  • 575 - Miniproject Language Modelling Language Modeling Next Word Prediction Python 3
  • 576 - Miniproject Language Modelling Language Modeling Next Word Prediction Python 4
  • 577 - Miniproject Language Modelling Language Modeling Next Word Prediction Python 5
  • 578 - Miniproject Language Modelling Language Modeling Next Word Prediction Python 6
  • 579 - Miniproject Sentiment Classification Vocabulary Implementation
  • 580 - Miniproject Sentiment Classification Vocabulary Implementation Helpers
  • 581 - Miniproject Sentiment Classification Vocabulary Implementation From File
  • 582 - Miniproject Sentiment Classification Vectorizer
  • 583 - Miniproject Sentiment Classification RNN Setup
  • 584 - Miniproject Sentiment Classification RNN Setup 1
  • 585 - RNN in PyTorch RNN In Pytorch Introduction
  • 586 - RNN in PyTorch RNN In Pytorch Embedding Layer
  • 587 - RNN in PyTorch RNN In Pytorch Nn Rnn
  • 588 - RNN in PyTorch RNN In Pytorch Output Shapes
  • 589 - RNN in PyTorch RNN In Pytorch Gatedunits
  • 590 - RNN in PyTorch RNN In Pytorch Gatedunits GRU LSTM
  • 591 - RNN in PyTorch RNN In Pytorch Bidirectional RNN
  • 592 - RNN in PyTorch RNN In Pytorch Bidirectional RNN Output Shapes
  • 593 - RNN in PyTorch RNN In Pytorch Bidirectional RNN Output Shapes Seperation
  • 594 - RNN in PyTorch RNN In Pytorch Example
  • 595 - Advanced RNN models RNN Encoder Decoder
  • 596 - Advanced RNN models RNN Attention
  • 597 - Neural Machine Translation Introduction To Dataset And Packages
  • 598 - Neural Machine Translation Implementing Language Class
  • 599 - Neural Machine Translation Testing Language Class And Implementing Normalization
  • 600 - Neural Machine Translation Reading Datafile
  • 601 - Neural Machine Translation Reading Building Vocabulary
  • 602 - Neural Machine Translation EncoderRNN
  • 603 - Neural Machine Translation DecoderRNN
  • 604 - Neural Machine Translation DecoderRNN Forward Step
  • 605 - Neural Machine Translation DecoderRNN Helper Functions
  • 606 - Neural Machine Translation Training Module
  • 607 - Neural Machine Translation Stochastic Gradient Descent
  • 608 - Neural Machine Translation NMT Training
  • 609 - Neural Machine Translation NMT Evaluation

  • 6 - Advanced Chatbots with Deep Learning Python
  • 610 - Links for the Courses Materials and Codes.html
  • 611 - Introduction Course and Instructor Introduction
  • 612 - Introduction AI Sciences Introduction
  • 613 - Introduction Course Description
  • 614 - Fundamentals of Chatbots for Deep Learning Module Introduction
  • 615 - Fundamentals of Chatbots for Deep Learning Conventional vs AI Chatbots
  • 616 - Fundamentals of Chatbots for Deep Learning Geneative vs Retrievel Chatbots
  • 617 - Fundamentals of Chatbots for Deep Learning Benifits of Deep Learning Chatbots
  • 618 - Fundamentals of Chatbots for Deep Learning Chatbots in Medical Domain
  • 619 - Fundamentals of Chatbots for Deep Learning Chatbots in Business
  • 620 - Fundamentals of Chatbots for Deep Learning Chatbots in ECommerce
  • 621 - Deep Learning Based Chatbot Architecture and Develpment Module Introduction
  • 622 - Deep Learning Based Chatbot Architecture and Develpment Deep Learning Architect
  • 623 - Deep Learning Based Chatbot Architecture and Develpment Encoder Decoder
  • 624 - Deep Learning Based Chatbot Architecture and Develpment Steps Involved
  • 625 - Deep Learning Based Chatbot Architecture and Develpment Project Overview and Packages
  • 626 - Deep Learning Based Chatbot Architecture and Develpment Importing Libraries
  • 627 - Deep Learning Based Chatbot Architecture and Develpment Data Prepration
  • 628 - Deep Learning Based Chatbot Architecture and Develpment Develop Vocabulary
  • 629 - Deep Learning Based Chatbot Architecture and Develpment Max Story and Question Length
  • 630 - Deep Learning Based Chatbot Architecture and Develpment Tokenizer
  • 631 - Deep Learning Based Chatbot Architecture and Develpment Separation and Sequence
  • 632 - Deep Learning Based Chatbot Architecture and Develpment Vectorize Stories
  • 633 - Deep Learning Based Chatbot Architecture and Develpment Vectorizing Train and Test Data
  • 634 - Deep Learning Based Chatbot Architecture and Develpment Encoding
  • 635 - Deep Learning Based Chatbot Architecture and Develpment Answer and Response
  • 636 - Deep Learning Based Chatbot Architecture and Develpment Model Completion
  • 637 - Deep Learning Based Chatbot Architecture and Develpment Predictions

  • 7 - Recommender Systems An Applied Approach using Deep Learning
  • 638 - Links for the Courses Materials and Codes.html
  • 639 - Introduction Course Outline
  • 640 - Deep Learning Foundation for Recommender Systems Module Introduction
  • 641 - Deep Learning Foundation for Recommender Systems Overview
  • 642 - Deep Learning Foundation for Recommender Systems Deep Learning in Recommendation Systems
  • 643 - Deep Learning Foundation for Recommender Systems Inference After Training
  • 644 - Deep Learning Foundation for Recommender Systems Inference Mechanism
  • 645 - Deep Learning Foundation for Recommender Systems Embeddings and User Context
  • 646 - Deep Learning Foundation for Recommender Systems Neutral Collaborative Filterin
  • 647 - Deep Learning Foundation for Recommender Systems VAE Collaborative Filtering
  • 648 - Deep Learning Foundation for Recommender Systems Strengths and Weaknesses of DL Models
  • 649 - Deep Learning Foundation for Recommender Systems Deep Learning Quiz
  • 650 - Deep Learning Foundation for Recommender Systems Deep Learning Quiz Solution
  • 651 - Project Amazon Product Recommendation System Module Overview
  • 652 - Project Amazon Product Recommendation System TensorFlow Recommenders
  • 653 - Project Amazon Product Recommendation System Two Tower Model
  • 654 - Project Amazon Product Recommendation System Project Overview
  • 655 - Project Amazon Product Recommendation System Download Libraries
  • 656 - Project Amazon Product Recommendation System Data Visualization with WordCloud
  • 657 - Project Amazon Product Recommendation System Make Tensors from DataFrame
  • 658 - Project Amazon Product Recommendation System Rating Our Data
  • 659 - Project Amazon Product Recommendation System Random TrainTest Split
  • 660 - Project Amazon Product Recommendation System Making the Model and Query Tower
  • 661 - Project Amazon Product Recommendation System Candidate Tower and Retrieval System
  • 662 - Project Amazon Product Recommendation System Compute Loss
  • 663 - Project Amazon Product Recommendation System Train and Validation
  • 664 - Project Amazon Product Recommendation System Accuracy vs Recommendations
  • 665 - Project Amazon Product Recommendation System Making Recommendations
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