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Deep Learning: Python Deep Learning Masterclass - BaDshaH - 11-26-2023
![[Image: UYWLIu-Eb-Vb4gbgbd-KLRsx-UQUVd-Kmw2-GU.jpg]](https://i.postimg.cc/9z3P19gq/UYWLIu-Eb-Vb4gbgbd-KLRsx-UQUVd-Kmw2-GU.jpg)
Published 11/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 25.29 GB | Duration: 63h 51m
Unlock the Secrets of Deep Learning: Dive Deep into CNNs, RNNs, NLP, Chatbots, and Recommender Systems - Deep Learning
[b]What you'll learn[/b]
Hands-on Projects: Engage in practical projects spanning image analysis, language translation, chatbot creation, and recommendation systems.
Deep Learning Fundamentals: Understand the core principles of deep learning and its applications across various domains.
Convolutional Neural Networks (CNNs): Master image processing, object detection, and advanced CNN architectures like LeNet, AlexNet, and ResNet.
Recurrent Neural Networks (RNNs) and Sequence Modeling: Explore sequence processing, language understanding, and modern RNN variants such as LSTM.
Natural Language Processing (NLP) Essentials: Dive into text preprocessing, word embeddings, and deep learning applications in language understanding.
Integration and Application: Combine knowledge from different modules to develop comprehensive deep learning solutions through a capstone project.
[b]Requirements[/b]
Understanding Python fundamentals is recommended for implementing deep learning concepts covered in the course.
[b]Description[/b]
Welcome to the ultimate Deep Learning masterclass! This comprehensive course integrates six modules, each providing a deep dive into different aspects of Deep Learning using Python. Whether you're a beginner looking to build a strong foundation or an intermediate learner seeking to advance your skills, this course offers practical insights, theoretical knowledge, and hands-on projects to cater to your needs. Who Should Take This Course?Beginners interested in diving into the world of Deep Learning with PythonIntermediate learners looking to enhance their Deep Learning skillsAnyone aspiring to understand and apply Deep Learning concepts in real-world projectsWhy This Course?This course offers an all-encompassing resource that covers a wide range of Deep Learning topics, making it suitable for learners at different levels. From fundamentals to advanced concepts, you will gain a comprehensive understanding of Deep Learning using Python through practical applications. What You Will Learn:Module 1: Deep Learning Fundamentals with PythonIntroduction to Deep LearningPython basics for Deep LearningData preprocessing for Deep Learning algorithmsGeneral machine learning conceptsModule 2: Convolutional Neural Networks (CNNs) in DepthIn-depth understanding of CNNsClassical computer vision techniquesBasics of Deep Neural NetworksArchitectures like LeNet, AlexNet, InceptionNet, ResNetTransfer Learning and YOLO Case StudyModule 3: Recurrent Neural Networks (RNNs) and Sequence ModelingExploration of RNNsApplications and importance of RNNsAddressing vanishing gradients in RNNsModern RNNs: LSTM, Bi-Directional RNNs, Attention ModelsImplementation of RNNs using TensorFlowModule 4: Natural Language Processing (NLP) FundamentalsMastery of NLPNLP foundations and significanceText preprocessing techniquesWord embeddings: Word2Vec, GloVe, BERTDeep Learning in NLP: Neural Networks, RNNs, and Advanced ModelsModule 5: Developing Chatbots using Deep LearningBuilding Chatbot systemsDeep Learning fundamentals for ChatbotsComparison of conventional vs. Deep Learning-based ChatbotsPractical implementation of RNN-based ChatbotsComprehensive package: Projects and advanced modelsModule 6: Recommender Systems using Deep LearningApplication of Recommender SystemsDeep Learning's role in Recommender SystemsBenefits and challengesDeveloping Recommender Systems with TensorFlowReal-world project: Amazon Product Recommendation SystemFinal Capstone ProjectIntegration and applicationHands-on project: Developing a comprehensive Deep Learning solutionFinal assessment and evaluationThis comprehensive course merges the essentials of Deep Learning, covering CNNs, RNNs, NLP, Chatbots, and Recommender Systems, offering a thorough understanding of Python-based implementations. Enroll now to gain expertise in various domains of Deep Learning through hands-on projects and theoretical foundations. Keywords and Skills
eep Learning MasteryPython Deep Learning CourseCNNs and RNNs TrainingNLP Fundamentals TutorialChatbot Development WorkshopRecommender Systems with TensorFlowAI Course for BeginnersHands-on Deep Learning ProjectsPython Programming for AIComprehensive Deep Learning CurriculumOverview
Section 1: Introduction
Lecture 1 Links for the Course's Materials and Codes
Section 2: Deep Learning
eep Neural Network for Beginners Using PythonLecture 2 Introduction: Introduction to Instructor
Lecture 3 Introduction: Introduction to Course
Lecture 4 Basics of Deep Learning: Problem to Solve Part 1
Lecture 5 Basics of Deep Learning: Problem to Solve Part 2
Lecture 6 Basics of Deep Learning: Problem to Solve Part 3
Lecture 7 Basics of Deep Learning: Linear Equation
Lecture 8 Basics of Deep Learning: Linear Equation Vectorized
Lecture 9 Basics of Deep Learning: 3D Feature Space
Lecture 10 Basics of Deep Learning: N Dimensional Space
Lecture 11 Basics of Deep Learning: Theory of Perceptron
Lecture 12 Basics of Deep Learning: Implementing Basic Perceptron
Lecture 13 Basics of Deep Learning: Logical Gates for Perceptrons
Lecture 14 Basics of Deep Learning: Perceptron Training Part 1
Lecture 15 Basics of Deep Learning: Perceptron Training Part 2
Lecture 16 Basics of Deep Learning: Learning Rate
Lecture 17 Basics of Deep Learning: Perceptron Training Part 3
Lecture 18 Basics of Deep Learning: Perceptron Algorithm
Lecture 19 Basics of Deep Learning: Coading Perceptron Algo (Data Reading & Visualization)
Lecture 20 Basics of Deep Learning: Coading Perceptron Algo (Perceptron Step)
Lecture 21 Basics of Deep Learning: Coading Perceptron Algo (Training Perceptron)
Lecture 22 Basics of Deep Learning: Coading Perceptron Algo (Visualizing the Results)
Lecture 23 Basics of Deep Learning: Problem with Linear Solutions
Lecture 24 Basics of Deep Learning: Solution to Problem
Lecture 25 Basics of Deep Learning: Error Functions
Lecture 26 Basics of Deep Learning: Discrete vs Continuous Error Function
Lecture 27 Basics of Deep Learning: Sigmoid Function
Lecture 28 Basics of Deep Learning: Multi-Class Problem
Lecture 29 Basics of Deep Learning: Problem of Negative Scores
Lecture 30 Basics of Deep Learning: Need of Softmax
Lecture 31 Basics of Deep Learning: Coding Softmax
Lecture 32 Basics of Deep Learning: One Hot Encoding
Lecture 33 Basics of Deep Learning: Maximum Likelihood Part 1
Lecture 34 Basics of Deep Learning: Maximum Likelihood Part 2
Lecture 35 Basics of Deep Learning: Cross Entropy
Lecture 36 Basics of Deep Learning: Cross Entropy Formulation
Lecture 37 Basics of Deep Learning: Multi Class Cross Entropy
Lecture 38 Basics of Deep Learning: Cross Entropy Implementation
Lecture 39 Basics of Deep Learning: Sigmoid Function Implementation
Lecture 40 Basics of Deep Learning: Output Function Implementation
Lecture 41 Deep Learning: Introduction to Gradient Decent
Lecture 42 Deep Learning: Convex Functions
Lecture 43 Deep Learning: Use of Derivatives
Lecture 44 Deep Learning: How Gradient Decent Works
Lecture 45 Deep Learning: Gradient Step
Lecture 46 Deep Learning: Logistic Regression Algorithm
Lecture 47 Deep Learning: Data Visualization and Reading
Lecture 48 Deep Learning: Updating Weights in Python
Lecture 49 Deep Learning: Implementing Logistic Regression
Lecture 50 Deep Learning: Visualization and Results
Lecture 51 Deep Learning: Gradient Decent vs Perceptron
Lecture 52 Deep Learning: Linear to Non Linear Boundaries
Lecture 53 Deep Learning: Combining Probabilities
Lecture 54 Deep Learning: Weighted Sums
Lecture 55 Deep Learning: Neural Network Architecture
Lecture 56 Deep Learning: Layers and DEEP Networks
Lecture 57 Deep Learning:Multi Class Classification
Lecture 58 Deep Learning: Basics of Feed Forward
Lecture 59 Deep Learning: Feed Forward for DEEP Net
Lecture 60 Deep Learning: Deep Learning Algo Overview
Lecture 61 Deep Learning: Basics of Back Propagation
Lecture 62 Deep Learning: Updating Weights
Lecture 63 Deep Learning: Chain Rule for BackPropagation
Lecture 64 Deep Learning: Sigma Prime
Lecture 65 Deep Learning: Data Analysis NN Implementation
Lecture 66 Deep Learning: One Hot Encoding (NN Implementation)
Lecture 67 Deep Learning: Scaling the Data (NN Implementation)
Lecture 68 Deep Learning: Splitting the Data (NN Implementation)
Lecture 69 Deep Learning: Helper Functions (NN Implementation)
Lecture 70 Deep Learning: Training (NN Implementation)
Lecture 71 Deep Learning: Testing (NN Implementation)
Lecture 72 Optimizations: Underfitting vs Overfitting
Lecture 73 Optimizations: Early Stopping
Lecture 74 Optimizations: Quiz
Lecture 75 Optimizations: Solution & Regularization
Lecture 76 Optimizations: L1 & L2 Regularization
Lecture 77 Optimizations: Dropout
Lecture 78 Optimizations: Local Minima Problem
Lecture 79 Optimizations: Random Restart Solution
Lecture 80 Optimizations: Vanishing Gradient Problem
Lecture 81 Optimizations: Other Activation Functions
Lecture 82 Final Project: Final Project Part 1
Lecture 83 Final Project: Final Project Part 2
Lecture 84 Final Project: Final Project Part 3
Lecture 85 Final Project: Final Project Part 4
Lecture 86 Final Project: Final Project Part 5
Section 3: Deep Learning CNN: Convolutional Neural Networks with Python
Lecture 87 Link to Github to get the Python Notebooks
Lecture 88 Introduction: Instructor Introduction
Lecture 89 Introduction: Why CNN
Lecture 90 Introduction: Focus of the Course
Lecture 91 Image Processing: Gray Scale Images
Lecture 92 Image Processing: Gray Scale Images Quiz
Lecture 93 Image Processing: Gray Scale Images Solution
Lecture 94 Image Processing: RGB Images
Lecture 95 Image Processing: RGB Images Quiz
Lecture 96 Image Processing: RGB Images Solution
Lecture 97 Image Processing: Reading and Showing Images in Python
Lecture 98 Image Processing: Reading and Showing Images in Python Quiz
Lecture 99 Image Processing: Reading and Showing Images in Python Solution
Lecture 100 Image Processing: Converting an Image to Grayscale in Python
Lecture 101 Image Processing: Converting an Image to Grayscale in Python Quiz
Lecture 102 Image Processing: Converting an Image to Grayscale in Python Solution
Lecture 103 Image Processing: Image Formation
Lecture 104 Image Processing: Image Formation Quiz
Lecture 105 Image Processing: Image Formation Solution
Lecture 106 Image Processing: Image Blurring 1
Lecture 107 Image Processing: Image Blurring 1 Quiz
Lecture 108 Image Processing: Image Blurring 1 Solution
Lecture 109 Image Processing: Image Blurring 2
Lecture 110 Image Processing: Image Blurring 2 Quiz
Lecture 111 Image Processing: Image Blurring 2 Solution
Lecture 112 Image Processing: General Image Filtering
Lecture 113 Image Processing: Convolution
Lecture 114 Image Processing: Edge Detection
Lecture 115 Image Processing: Image Sharpening
Lecture 116 Image Processing: Implementation of Image Blurring Edge Detection Image Sharpening in Python
Lecture 117 Image Processing: Parameteric Shape Detection
Lecture 118 Image Processing: Image Processing Activity
Lecture 119 Image Processing: Image Processing Activity Solution
Lecture 120 Object Detection: Introduction to Object Detection
Lecture 121 Object Detection: Classification PipleLine
Lecture 122 Object Detection: Classification PipleLine Quiz
Lecture 123 Object Detection: Classification PipleLine Solution
Lecture 124 Object Detection: Sliding Window Implementation
Lecture 125 Object Detection: Shift Scale Rotation Invariance
Lecture 126 Object Detection: Shift Scale Rotation Invariance Exercise
Lecture 127 Object Detection: Person Detection
Lecture 128 Object Detection: HOG Features
Lecture 129 Object Detection: HOG Features Exercise
Lecture 130 Object Detection: Hand Engineering vs CNNs
Lecture 131 Object Detection: Object Detection Activity
Lecture 132 Deep Neural Network Overview: Neuron and Perceptron
Lecture 133 Deep Neural Network Overview: DNN Architecture
Lecture 134 Deep Neural Network Overview: DNN Architecture Quiz
Lecture 135 Deep Neural Network Overview: DNN Architecture Solution
Lecture 136 Deep Neural Network Overview: FeedForward FullyConnected MLP
Lecture 137 Deep Neural Network Overview: Calculating Number of Weights of DNN
Lecture 138 Deep Neural Network Overview: Calculating Number of Weights of DNN Quiz
Lecture 139 Deep Neural Network Overview: Calculating Number of Weights of DNN Solution
Lecture 140 Deep Neural Network Overview: Number of Nuerons vs Number of Layers
Lecture 141 Deep Neural Network Overview: Discriminative vs Generative Learning
Lecture 142 Deep Neural Network Overview: Universal Approximation Therorem
Lecture 143 Deep Neural Network Overview: Why Depth
Lecture 144 Deep Neural Network Overview: Decision Boundary in DNN
Lecture 145 Deep Neural Network Overview: Decision Boundary in DNN Quiz
Lecture 146 Deep Neural Network Overview: Decision Boundary in DNN Solution
Lecture 147 Deep Neural Network Overview: BiasTerm
Lecture 148 Deep Neural Network Overview: BiasTerm Quiz
Lecture 149 Deep Neural Network Overview: BiasTerm Solution
Lecture 150 Deep Neural Network Overview: Activation Function
Lecture 151 Deep Neural Network Overview: Activation Function Quiz
Lecture 152 Deep Neural Network Overview: Activation Function Solution
Lecture 153 Deep Neural Network Overview: DNN Training Parameters
Lecture 154 Deep Neural Network Overview: DNN Training Parameters Quiz
Lecture 155 Deep Neural Network Overview: DNN Training Parameters Solution
Lecture 156 Deep Neural Network Overview: Gradient Descent
Lecture 157 Deep Neural Network Overview: BackPropagation
Lecture 158 Deep Neural Network Overview: Training DNN Animantion
Lecture 159 Deep Neural Network Overview: Weigth Initialization
Lecture 160 Deep Neural Network Overview: Weigth Initialization Quiz
Lecture 161 Deep Neural Network Overview: Weigth Initialization Solution
Lecture 162 Deep Neural Network Overview: Batch miniBatch Stocastic Gradient Descent
Lecture 163 Deep Neural Network Overview: Batch Normalization
Lecture 164 Deep Neural Network Overview: Rprop and Momentum
Lecture 165 Deep Neural Network Overview: Rprop and Momentum Quiz
Lecture 166 Deep Neural Network Overview: Rprop and Momentum Solution
Lecture 167 Deep Neural Network Overview: Convergence Animation
Lecture 168 Deep Neural Network Overview: DropOut, Early Stopping and Hyperparameters
Lecture 169 Deep Neural Network Overview: DropOut, Early Stopping and Hyperparameters Quiz
Lecture 170 Deep Neural Network Overview: DropOut, Early Stopping and Hyperparameters Solution
Lecture 171 Deep Neural Network Architecture: Convolution Revisited
Lecture 172 Deep Neural Network Architecture: Implementing Convolution in Python Revisited
Lecture 173 Deep Neural Network Architecture: Why Convolution
Lecture 174 Deep Neural Network Architecture: Filters Padding Strides
Lecture 175 Deep Neural Network Architecture: Padding Image
Lecture 176 Deep Neural Network Architecture: Pooling Tensors
Lecture 177 Deep Neural Network Architecture: CNN Example
Lecture 178 Deep Neural Network Architecture: Convolution and Pooling Details
Lecture 179 Deep Neural Network Architecture: Maxpooling Exercise
Lecture 180 Deep Neural Network Architecture: NonVectorized Implementations of Conv2d and Pool2d
Lecture 181 Deep Neural Network Architecture: Deep Neural Network Architecture Activity
Lecture 182 Gradient Descent in CNNs: Example Setup
Lecture 183 Gradient Descent in CNNs: Why Derivaties
Lecture 184 Gradient Descent in CNNs: Why Derivaties Quiz
Lecture 185 Gradient Descent in CNNs: Why Derivaties Solution
Lecture 186 Gradient Descent in CNNs: What is Chain Rule
Lecture 187 Gradient Descent in CNNs: Applying Chain Rule
Lecture 188 Gradient Descent in CNNs: Gradients of MaxPooling Layer
Lecture 189 Gradient Descent in CNNs: Gradients of MaxPooling Layer Quiz
Lecture 190 Gradient Descent in CNNs: Gradients of MaxPooling Layer Solution
Lecture 191 Gradient Descent in CNNs: Gradients of Convolutional Layer
Lecture 192 Gradient Descent in CNNs: Extending To Multiple Filters
Lecture 193 Gradient Descent in CNNs: Extending to Multiple Layers
Lecture 194 Gradient Descent in CNNs: Extending to Multiple Layers Quiz
Lecture 195 Gradient Descent in CNNs: Extending to Multiple Layers Solution
Lecture 196 Gradient Descent in CNNs: Implementation in Numpy ForwardPass
Lecture 197 Gradient Descent in CNNs: Implementation in Numpy BackwardPass 1
Lecture 198 Gradient Descent in CNNs: Implementation in Numpy BackwardPass 2
Lecture 199 Gradient Descent in CNNs: Implementation in Numpy BackwardPass 3
Lecture 200 Gradient Descent in CNNs: Implementation in Numpy BackwardPass 4
Lecture 201 Gradient Descent in CNNs: Implementation in Numpy BackwardPass 5
Lecture 202 Gradient Descent in CNNs: Gradient Descent in CNNs Activity
Lecture 203 Introduction to TensorFlow: Introduction
Lecture 204 Introduction to TensorFlow: FashionMNIST Example Plan Neural Network
Lecture 205 Introduction to TensorFlow: FashionMNIST Example CNN
Lecture 206 Introduction to TensorFlow: Introduction to TensorFlow Activity
Lecture 207 Classical CNNs: LeNet
Lecture 208 Classical CNNs: LeNet Quiz
Lecture 209 Classical CNNs: LeNet Solution
Lecture 210 Classical CNNs: AlexNet
Lecture 211 Classical CNNs: VGG
Lecture 212 Classical CNNs: InceptionNet
Lecture 213 Classical CNNs: GoogLeNet
Lecture 214 Classical CNNs: Resnet
Lecture 215 Classical CNNs: Classical CNNs Activity
Lecture 216 Transfer Learning: What is Transfer learning
Lecture 217 Transfer Learning: Why Transfer Learning
Lecture 218 Transfer Learning: Practical Tips
Lecture 219 Transfer Learning: Project in TensorFlow
Lecture 220 Transfer Learning: ImageNet Challenge
Lecture 221 Transfer Learning: Transfer Learning Activity
Lecture 222 Yolo: Image Classfication Revisited
Lecture 223 Yolo: Sliding Window Object Localization
Lecture 224 Yolo: Sliding Window Efficient Implementation
Lecture 225 Yolo: Yolo Introduction
Lecture 226 Yolo: Yolo Training Data Generation
Lecture 227 Yolo: Yolo Anchor Boxes
Lecture 228 Yolo: Yolo Algorithm
Lecture 229 Yolo: Yolo Non Maxima Supression
Lecture 230 Yolo: RCNN
Lecture 231 Yolo: Yolo Activity
Lecture 232 Face Verification: Problem Setup
Lecture 233 Face Verification: Project Implementation
Lecture 234 Face Verification: Face Verification Activity
Lecture 235 Neural Style Transfer: Problem Setup
Lecture 236 Neural Style Transfer: Implementation Tensorflow Hub
Section 4: Deep Learning: Recurrent Neural Networks with Python
Lecture 237 Link to oneDrive and Github to get the Python Notebooks
Lecture 238 Introduction: Introduction to Instructor and Aisciences
Lecture 239 Introduction: Introduction To Instructor
Lecture 240 Introduction: Focus of the Course
Lecture 241 Applications of RNN (Motivation): Human Activity Recognition
Lecture 242 Applications of RNN (Motivation): Image Captioning
Lecture 243 Applications of RNN (Motivation): Machine Translation
Lecture 244 Applications of RNN (Motivation): Speech Recognition
Lecture 245 Applications of RNN (Motivation): Stock Price Predictions
Lecture 246 Applications of RNN (Motivation): When to Model RNN
Lecture 247 Applications of RNN (Motivation): Activity
Lecture 248 DNN Overview: Why PyTorch
Lecture 249 DNN Overview: PyTorch Installation and Tensors Introduction
Lecture 250 DNN Overview: Automatic Diffrenciation Pytorch New
Lecture 251 DNN Overview: Why DNNs in Machine Learning
Lecture 252 DNN Overview: Representational Power and Data Utilization Capacity of DNN
Lecture 253 DNN Overview: Perceptron
Lecture 254 DNN Overview: Perceptron Exercise
Lecture 255 DNN Overview: Perceptron Exercise Solution
Lecture 256 DNN Overview: Perceptron Implementation
Lecture 257 DNN Overview: DNN Architecture
Lecture 258 DNN Overview: DNN Architecture Exercise
Lecture 259 DNN Overview: DNN Architecture Exercise Solution
Lecture 260 DNN Overview: DNN ForwardStep Implementation
Lecture 261 DNN Overview: DNN Why Activation Function Is Required
Lecture 262 DNN Overview: DNN Why Activation Function Is Required Exercise
Lecture 263 DNN Overview: DNN Why Activation Function Is Required Exercise Solution
Lecture 264 DNN Overview: DNN Properties Of Activation Function
Lecture 265 DNN Overview: DNN Activation Functions In Pytorch
Lecture 266 DNN Overview: DNN What Is Loss Function
Lecture 267 DNN Overview: DNN What Is Loss Function Exercise
Lecture 268 DNN Overview: DNN What Is Loss Function Exercise Solution
Lecture 269 DNN Overview: DNN What Is Loss Function Exercise 02
Lecture 270 DNN Overview: DNN What Is Loss Function Exercise 02 Solution
Lecture 271 DNN Overview: DNN Loss Function In Pytorch
Lecture 272 DNN Overview: DNN Gradient Descent
Lecture 273 DNN Overview: DNN Gradient Descent Exercise
Lecture 274 DNN Overview: DNN Gradient Descent Exercise Solution
Lecture 275 DNN Overview: DNN Gradient Descent Implementation
Lecture 276 DNN Overview: DNN Gradient Descent Stochastic Batch Minibatch
Lecture 277 DNN Overview: DNN Implemenation Gradient Step
Lecture 278 DNN Overview: DNN Implemenation Stochastic Gradient Descent
Lecture 279 DNN Overview: DNN Gradient Descent Summary
Lecture 280 DNN Overview: DNN Implemenation Batch Gradient Descent
Lecture 281 DNN Overview: DNN Implemenation Minibatch Gradient Descent
Lecture 282 DNN Overview: DNN Implemenation In PyTorch
Lecture 283 DNN Overview: DNN Weights Initializations
Lecture 284 DNN Overview: DNN Learning Rate
Lecture 285 DNN Overview: DNN Batch Normalization
Lecture 286 DNN Overview: DNN batch Normalization Implementation
Lecture 287 DNN Overview: DNN Optimizations
Lecture 288 DNN Overview: DNN Dropout
Lecture 289 DNN Overview: DNN Dropout In PyTorch
Lecture 290 DNN Overview: DNN Early Stopping
Lecture 291 DNN Overview: DNN Hyperparameters
Lecture 292 DNN Overview: DNN Pytorch CIFAR10 Example
Lecture 293 RNN Architecture: Introduction to Module
Lecture 294 RNN Architecture: Fixed Length Memory Model
Lecture 295 RNN Architecture: Fixed Length Memory Model Exercise
Lecture 296 RNN Architecture: Fixed Length Memory Model Exercise Solution Part 01
Lecture 297 RNN Architecture: Fixed Length Memory Model Exercise Solution Part 02
Lecture 298 RNN Architecture: Infinite Memory Architecture
Lecture 299 RNN Architecture: Infinite Memory Architecture Exercise
Lecture 300 RNN Architecture: Infinite Memory Architecture Solution
Lecture 301 RNN Architecture: Weight Sharing
Lecture 302 RNN Architecture: Notations
Lecture 303 RNN Architecture: ManyToMany Model
Lecture 304 RNN Architecture: ManyToMany Model Exercise 01
Lecture 305 RNN Architecture: ManyToMany Model Solution 01
Lecture 306 RNN Architecture: ManyToMany Model Exercise 02
Lecture 307 RNN Architecture: ManyToMany Model Solution 02
Lecture 308 RNN Architecture: ManyToOne Model
Lecture 309 RNN Architecture: OneToMany Model Exercise
Lecture 310 RNN Architecture: OneToMany Model Solution
Lecture 311 RNN Architecture: OneToMany Model
Lecture 312 RNN Architecture: ManyToOne Model Exercise
Lecture 313 RNN Architecture: ManyToOne Model Solution
Lecture 314 RNN Architecture: Activity Many to One
Lecture 315 RNN Architecture: Activity Many to One Exercise
Lecture 316 RNN Architecture: Activity Many to One Solution
Lecture 317 RNN Architecture: ManyToMany Different Sizes Model
Lecture 318 RNN Architecture: Activity Many to Many Nmt
Lecture 319 RNN Architecture: Models Summary
Lecture 320 RNN Architecture: Deep RNNs
Lecture 321 RNN Architecture: Deep RNNs Exercise
Lecture 322 RNN Architecture: Deep RNNs Solution
Lecture 323 Gradient Decsent in RNN: Introduction to Gradient Descent Module
Lecture 324 Gradient Decsent in RNN: Example Setup
Lecture 325 Gradient Decsent in RNN: Equations
Lecture 326 Gradient Decsent in RNN: Equations Exercise
Lecture 327 Gradient Decsent in RNN: Equations Solution
Lecture 328 Gradient Decsent in RNN: Loss Function
Lecture 329 Gradient Decsent in RNN: Why Gradients
Lecture 330 Gradient Decsent in RNN: Why Gradients Exercise
Lecture 331 Gradient Decsent in RNN: Why Gradients Solution
Lecture 332 Gradient Decsent in RNN: Chain Rule
Lecture 333 Gradient Decsent in RNN: Chain Rule in Action
Lecture 334 Gradient Decsent in RNN: BackPropagation Through Time
Lecture 335 Gradient Decsent in RNN: Activity
Lecture 336 RNN implementation: Automatic Diffrenciation
Lecture 337 RNN implementation: Automatic Diffrenciation Pytorch
Lecture 338 RNN implementation: Language Modeling Next Word Prediction Vocabulary Index
Lecture 339 RNN implementation: Language Modeling Next Word Prediction Vocabulary Index Embeddings
Lecture 340 RNN implementation: Language Modeling Next Word Prediction RNN Architecture
Lecture 341 RNN implementation: Language Modeling Next Word Prediction Python 1
Lecture 342 RNN implementation: Language Modeling Next Word Prediction Python 2
Lecture 343 RNN implementation: Language Modeling Next Word Prediction Python 3
Lecture 344 RNN implementation: Language Modeling Next Word Prediction Python 4
Lecture 345 RNN implementation: Language Modeling Next Word Prediction Python 5
Lecture 346 RNN implementation: Language Modeling Next Word Prediction Python 6
Lecture 347 Sentiment Classification using RNN: Vocabulary Implementation
Lecture 348 Sentiment Classification using RNN: Vocabulary Implementation Helpers
Lecture 349 Sentiment Classification using RNN: Vocabulary Implementation From File
Lecture 350 Sentiment Classification using RNN: Vectorizer
Lecture 351 Sentiment Classification using RNN: RNN Setup 1
Lecture 352 Sentiment Classification using RNN: RNN Setup 2
Lecture 353 Sentiment Classification using RNN: WhatNext
Lecture 354 Vanishing Gradients in RNN: Introduction to Better RNNs Module
Lecture 355 Vanishing Gradients in RNN: Introduction Vanishing Gradients in RNN
Lecture 356 Vanishing Gradients in RNN: GRU
Lecture 357 Vanishing Gradients in RNN: GRU Optional
Lecture 358 Vanishing Gradients in RNN: LSTM
Lecture 359 Vanishing Gradients in RNN: LSTM Optional
Lecture 360 Vanishing Gradients in RNN: Bidirectional RNN
Lecture 361 Vanishing Gradients in RNN: Attention Model
Lecture 362 Vanishing Gradients in RNN: Attention Model Optional
Lecture 363 TensorFlow: Introduction to TensorFlow
Lecture 364 TensorFlow: TensorFlow Text Classification Example using RNN
Lecture 365 Project I: Book Writer: Introduction
Lecture 366 Project I: Book Writer: Data Mapping
Lecture 367 Project I: Book Writer: Modling RNN Architecture
Lecture 368 Project I: Book Writer: Modling RNN Model in TensorFlow
Lecture 369 Project I: Book Writer: Modling RNN Model Training
Lecture 370 Project I: Book Writer: Modling RNN Model Text Generation
Lecture 371 Project I: Book Writer: Activity
Lecture 372 Project II: Stock Price Prediction: Problem Statement
Lecture 373 Project II: Stock Price Prediction: Data Set
Lecture 374 Project II: Stock Price Prediction: Data Prepration
Lecture 375 Project II: Stock Price Prediction: RNN Model Training and Evaluation
Lecture 376 Project II: Stock Price Prediction: Activity
Lecture 377 Further Readings and Resourses: Further Readings and Resourses 1
Section 5: NLP-Natural Language Processing in Python(Theory & Projects)
Lecture 378 Links for the Course's Materials and Codes
Lecture 379 Introduction: Introduction to Course
Lecture 380 Introduction: Introduction to Instructor
Lecture 381 Introduction: Introduction to Co-Instructor
Lecture 382 Introduction: Course Introduction
Lecture 383 Introduction(Regular Expressions): What Is Regular Expression
Lecture 384 Introduction(Regular Expressions): Why Regular Expression
Lecture 385 Introduction(Regular Expressions): ELIZA Chatbot
Lecture 386 Introduction(Regular Expressions): Python Regular Expression Package
Lecture 387 Meta Characters(Regular Expressions): Meta Characters
Lecture 388 Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise
Lecture 389 Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise Solution
Lecture 390 Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise 2
Lecture 391 Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise 2 Solution
Lecture 392 Meta Characters(Regular Expressions): Meta Characters Cap
Lecture 393 Meta Characters(Regular Expressions): Meta Characters Cap Exercise 3
Lecture 394 Meta Characters(Regular Expressions): Meta Characters Cap Exercise 3 Solution
Lecture 395 Meta Characters(Regular Expressions): Backslash
Lecture 396 Meta Characters(Regular Expressions): Backslash Continued
Lecture 397 Meta Characters(Regular Expressions): Backslash Continued 01
Lecture 398 Meta Characters(Regular Expressions): Backslash Squared Brackets Exercise
Lecture 399 Meta Characters(Regular Expressions): Backslash Squared Brackets Exercise Solution
Lecture 400 Meta Characters(Regular Expressions): Backslash Squared Brackets Exercise Another Solution
Lecture 401 Meta Characters(Regular Expressions): Backslash Exercise
Lecture 402 Meta Characters(Regular Expressions): Backslash Exercise Solution And Special Sequences Exercise
Lecture 403 Meta Characters(Regular Expressions): Solution And Special Sequences Exercise Solution
Lecture 404 Meta Characters(Regular Expressions): Meta Character Asterisk
Lecture 405 Meta Characters(Regular Expressions): Meta Character Asterisk Exercise
Lecture 406 Meta Characters(Regular Expressions): Meta Character Asterisk Exercise Solution
Lecture 407 Meta Characters(Regular Expressions): Meta Character Asterisk Homework
Lecture 408 Meta Characters(Regular Expressions): Meta Character Asterisk Greedymatching
Lecture 409 Meta Characters(Regular Expressions): Meta Character Plus And Questionmark
Lecture 410 Meta Characters(Regular Expressions): Meta Character Curly Brackets Exercise
Lecture 411 Meta Characters(Regular Expressions): Meta Character Curly Brackets Exercise Solution
Lecture 412 Pattern Objects: Pattern Objects
Lecture 413 Pattern Objects: Pattern Objects Match Method Exersize
Lecture 414 Pattern Objects: Pattern Objects Match Method Exersize Solution
Lecture 415 Pattern Objects: Pattern Objects Match Method Vs Search Method
Lecture 416 Pattern Objects: Pattern Objects Finditer Method
Lecture 417 Pattern Objects: Pattern Objects Finditer Method Exersize Solution
Lecture 418 More Meta Characters: Meta Characters Logical Or
Lecture 419 More Meta Characters: Meta Characters Beginning And End Patterns
Lecture 420 More Meta Characters: Meta Characters Paranthesis
Lecture 421 String Modification: String Modification
Lecture 422 String Modification: Word Tokenizer Using Split Method
Lecture 423 String Modification: Sub Method Exercise
Lecture 424 String Modification: Sub Method Exercise Solution
Lecture 425 Words and Tokens: What Is A Word
Lecture 426 Words and Tokens: Definition Of Word Is Task Dependent
Lecture 427 Words and Tokens: Vocabulary And Corpus
Lecture 428 Words and Tokens: Tokens
Lecture 429 Words and Tokens: Tokenization In Spacy
Lecture 430 Sentiment Classification: Yelp Reviews Classification Mini Project Introduction
Lecture 431 Sentiment Classification: Yelp Reviews Classification Mini Project Vocabulary Initialization
Lecture 432 Sentiment Classification: Yelp Reviews Classification Mini Project Adding Tokens To Vocabulary
Lecture 433 Sentiment Classification: Yelp Reviews Classification Mini Project Look Up Functions In Vocabulary
Lecture 434 Sentiment Classification: Yelp Reviews Classification Mini Project Building Vocabulary From Data
Lecture 435 Sentiment Classification: Yelp Reviews Classification Mini Project One Hot Encoding
Lecture 436 Sentiment Classification: Yelp Reviews Classification Mini Project One Hot Encoding Implementation
Lecture 437 Sentiment Classification: Yelp Reviews Classification Mini Project Encoding Documents
Lecture 438 Sentiment Classification: Yelp Reviews Classification Mini Project Encoding Documents Implementation
Lecture 439 Sentiment Classification: Yelp Reviews Classification Mini Project Train Test Splits
Lecture 440 Sentiment Classification: Yelp Reviews Classification Mini Project Featurecomputation
Lecture 441 Sentiment Classification: Yelp Reviews Classification Mini Project Classification
Lecture 442 Language Independent Tokenization: Tokenization In Detial Introduction
Lecture 443 Language Independent Tokenization: Tokenization Is Hard
Lecture 444 Language Independent Tokenization: Tokenization Byte Pair Encoding
Lecture 445 Language Independent Tokenization: Tokenization Byte Pair Encoding Example
Lecture 446 Language Independent Tokenization: Tokenization Byte Pair Encoding On Test Data
Lecture 447 Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation Getpaircounts
Lecture 448 Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation Mergeincorpus
Lecture 449 Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE Training
Lecture 450 Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE Encoding
Lecture 451 Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair
Lecture 452 Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair 1
Lecture 453 Text Nomalization: Word Normalization Case Folding
Lecture 454 Text Nomalization: Word Normalization Lematization
Lecture 455 Text Nomalization: Word Normalization Stemming
Lecture 456 Text Nomalization: Word Normalization Sentence Segmentation
Lecture 457 String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Intro
Lecture 458 String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Example
Lecture 459 String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Table Filling
Lecture 460 String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Dynamic Programming
Lecture 461 String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Psudocode
Lecture 462 String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Implementation
Lecture 463 String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Implementation Bugfixing
Lecture 464 String Matching and Spelling Correction: Spelling Correction Implementation
Lecture 465 Language Modeling: What Is A Language Model
Lecture 466 Language Modeling: Language Model Formal Definition
Lecture 467 Language Modeling: Language Model Curse Of Dimensionality
Lecture 468 Language Modeling: Language Model Markov Assumption And N-Grams
Lecture 469 Language Modeling: Language Model Implementation Setup
Lecture 470 Language Modeling: Language Model Implementation Ngrams Function
Lecture 471 Language Modeling: Language Model Implementation Update Counts Function
Lecture 472 Language Modeling: Language Model Implementation Probability Model Funciton
Lecture 473 Language Modeling: Language Model Implementation Reading Corpus
Lecture 474 Language Modeling: Language Model Implementation Sampling Text
Lecture 475 Topic Modelling with Word and Document Representations: One Hot Vectors
Lecture 476 Topic Modelling with Word and Document Representations: One Hot Vectors Implementaton
Lecture 477 Topic Modelling with Word and Document Representations: One Hot Vectors Limitations
Lecture 478 Topic Modelling with Word and Document Representations: One Hot Vectors Uses As Target Labeling
Lecture 479 Topic Modelling with Word and Document Representations: Term Frequency For Document Representations
Lecture 480 Topic Modelling with Word and Document Representations: Term Frequency For Document Representations Implementations
Lecture 481 Topic Modelling with Word and Document Representations: Term Frequency For Word Representations
Lecture 482 Topic Modelling with Word and Document Representations: TFIDF For Document Representations
Lecture 483 Topic Modelling with Word and Document Representations: TFIDF For Document Representations Implementation Reading Corpus
Lecture 484 Topic Modelling with Word and Document Representations: TFIDF For Document Representations Implementation Computing Document Frequency
Lecture 485 Topic Modelling with Word and Document Representations: TFIDF For Document Representations Implementation Computing TFIDF
Lecture 486 Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 1
Lecture 487 Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 3
Lecture 488 Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 4
Lecture 489 Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 5
Lecture 490 Topic Modelling with Word and Document Representations: Topic Modeling With Gensim
Lecture 491 Word Embeddings LSI: Word Co-occurrence Matrix
Lecture 492 Word Embeddings LSI: Word Co-occurrence Matrix vs Document-term Matrix
Lecture 493 Word Embeddings LSI: Word Co-occurrence Matrix Implementation Preparing Data
Lecture 494 Word Embeddings LSI: Word Co-occurrence Matrix Implementation Preparing Data 2
Lecture 495 Word Embeddings LSI: Word Co-occurrence Matrix Implementation Preparing Data Getting Vocabulary
Lecture 496 Word Embeddings LSI: Word Co-occurrence Matrix Implementation Final Function
Lecture 497 Word Embeddings LSI: Word Co-occurrence Matrix Implementation Handling Memory Issues On Large Corp
Lecture 498 Word Embeddings LSI: Word Co-occurrence Matrix Sparsity
Lecture 499 Word Embeddings LSI: Word Co-occurrence Matrix Positive Point Wise Mutual Information PPMI
Lecture 500 Word Embeddings LSI: PCA For Dense Embeddings
Lecture 501 Word Embeddings LSI: Latent Semantic Analysis
Lecture 502 Word Embeddings LSI: Latent Semantic Analysis Implementation
Lecture 503 Word Semantics: Cosine Similarity
Lecture 504 Word Semantics: Cosine Similarity Geting Norms Of Vectors
Lecture 505 Word Semantics: Cosine Similarity Normalizing Vectors
Lecture 506 Word Semantics: Cosine Similarity With More Than One Vectors
Lecture 507 Word Semantics: Cosine Similarity Getting Most Similar Words In The Vocabulary
Lecture 508 Word Semantics: Cosine Similarity Getting Most Similar Words In The Vocabulary Fixingbug Of D
Lecture 509 Word Semantics: Cosine Similarity Word2Vec Embeddings
Lecture 510 Word Semantics: Words Analogies
Lecture 511 Word Semantics: Words Analogies Implemenation 1
Lecture 512 Word Semantics: Words Analogies Implemenation 2
Lecture 513 Word Semantics: Words Visualizations
Lecture 514 Word Semantics: Words Visualizations Implementaion
Lecture 515 Word Semantics: Words Visualizations Implementaion 2
Lecture 516 Word2vec: Static And Dynamic Embeddings
Lecture 517 Word2vec: Self Supervision
Lecture 518 Word2vec: Word2Vec Algorithm Abstract
Lecture 519 Word2vec: Word2Vec Why Negative Sampling
Lecture 520 Word2vec: Word2Vec What Is Skip Gram
Lecture 521 Word2vec: Word2Vec How To Define Probability Law
Lecture 522 Word2vec: Word2Vec Sigmoid
Lecture 523 Word2vec: Word2Vec Formalizing Loss Function
Lecture 524 Word2vec: Word2Vec Loss Function
Lecture 525 Word2vec: Word2Vec Gradient Descent Step
Lecture 526 Word2vec: Word2Vec Implemenation Preparing Data
Lecture 527 Word2vec: Word2Vec Implemenation Gradient Step
Lecture 528 Word2vec: Word2Vec Implemenation Driver Function
Lecture 529 Need of Deep Learning for NLP: Why RNNs For NLP
Lecture 530 Need of Deep Learning for NLP: Pytorch Installation And Tensors Introduction
Lecture 531 Need of Deep Learning for NLP: Automatic Diffrenciation Pytorch
Lecture 532 Introduction(NLP with Deep Learning DNN): Why DNNs In Machine Learning
Lecture 533 Introduction(NLP with Deep Learning DNN): Representational Power And Data Utilization Capacity Of DNN
Lecture 534 Introduction(NLP with Deep Learning DNN): Perceptron
Lecture 535 Introduction(NLP with Deep Learning DNN): Perceptron Implementation
Lecture 536 Introduction(NLP with Deep Learning DNN): DNN Architecture
Lecture 537 Introduction(NLP with Deep Learning DNN): DNN Forwardstep Implementation
Lecture 538 Introduction(NLP with Deep Learning DNN): DNN Why Activation Function Is Require
Lecture 539 Introduction(NLP with Deep Learning DNN): DNN Properties Of Activation Function
Lecture 540 Introduction(NLP with Deep Learning DNN): DNN Activation Functions In Pytorch
Lecture 541 Introduction(NLP with Deep Learning DNN): DNN What Is Loss Function
Lecture 542 Introduction(NLP with Deep Learning DNN): DNN Loss Function In Pytorch
Lecture 543 Training(NLP with DNN): DNN Gradient Descent
Lecture 544 Training(NLP with DNN): DNN Gradient Descent Implementation
Lecture 545 Training(NLP with DNN): DNN Gradient Descent Stochastic Batch Minibatch
Lecture 546 Training(NLP with DNN): DNN Gradient Descent Summary
Lecture 547 Training(NLP with DNN): DNN Implemenation Gradient Step
Lecture 548 Training(NLP with DNN): DNN Implemenation Stochastic Gradient Descent
Lecture 549 Training(NLP with DNN): DNN Implemenation Batch Gradient Descent
Lecture 550 Training(NLP with DNN): DNN Implemenation Minibatch Gradient Descent
Lecture 551 Training(NLP with DNN): DNN Implemenation In Pytorch
Lecture 552 Hyper parameters(NLP with DNN): DNN Weights Initializations
Lecture 553 Hyper parameters(NLP with DNN): DNN Learning Rate
Lecture 554 Hyper parameters(NLP with DNN): DNN Batch Normalization
Lecture 555 Hyper parameters(NLP with DNN): DNN Batch Normalization Implementation
Lecture 556 Hyper parameters(NLP with DNN): DNN Optimizations
Lecture 557 Hyper parameters(NLP with DNN): DNN Dropout
Lecture 558 Hyper parameters(NLP with DNN): DNN Dropout In Pytorch
Lecture 559 Hyper parameters(NLP with DNN): DNN Early Stopping
Lecture 560 Hyper parameters(NLP with DNN): DNN Hyperparameters
Lecture 561 Hyper parameters(NLP with DNN): DNN Pytorch CIFAR10 Example
Lecture 562 Introduction(NLP with Deep Learning RNN): What Is RNN
Lecture 563 Introduction(NLP with Deep Learning RNN): Understanding RNN With A Simple Example
Lecture 564 Introduction(NLP with Deep Learning RNN): RNN Applications Human Activity Recognition
Lecture 565 Introduction(NLP with Deep Learning RNN): RNN Applications Image Captioning
Lecture 566 Introduction(NLP with Deep Learning RNN): RNN Applications Machine Translation
Lecture 567 Introduction(NLP with Deep Learning RNN): RNN Applications Speech Recognition Stock Price Prediction
Lecture 568 Introduction(NLP with Deep Learning RNN): RNN Models
Lecture 569 Mini-project Language Modelling: Language Modeling Next Word Prediction
Lecture 570 Mini-project Language Modelling: Language Modeling Next Word Prediction Vocabulary Index
Lecture 571 Mini-project Language Modelling: Language Modeling Next Word Prediction Vocabulary Index Embeddings
Lecture 572 Mini-project Language Modelling: Language Modeling Next Word Prediction Rnn Architecture
Lecture 573 Mini-project Language Modelling: Language Modeling Next Word Prediction Python 1
Lecture 574 Mini-project Language Modelling: Language Modeling Next Word Prediction Python 2
Lecture 575 Mini-project Language Modelling: Language Modeling Next Word Prediction Python 3
Lecture 576 Mini-project Language Modelling: Language Modeling Next Word Prediction Python 4
Lecture 577 Mini-project Language Modelling: Language Modeling Next Word Prediction Python 5
Lecture 578 Mini-project Language Modelling: Language Modeling Next Word Prediction Python 6
Lecture 579 Mini-project Sentiment Classification: Vocabulary Implementation
Lecture 580 Mini-project Sentiment Classification: Vocabulary Implementation Helpers
Lecture 581 Mini-project Sentiment Classification: Vocabulary Implementation From File
Lecture 582 Mini-project Sentiment Classification: Vectorizer
Lecture 583 Mini-project Sentiment Classification: RNN Setup
Lecture 584 Mini-project Sentiment Classification: RNN Setup 1
Lecture 585 RNN in PyTorch: RNN In Pytorch Introduction
Lecture 586 RNN in PyTorch: RNN In Pytorch Embedding Layer
Lecture 587 RNN in PyTorch: RNN In Pytorch Nn Rnn
Lecture 588 RNN in PyTorch: RNN In Pytorch Output Shapes
Lecture 589 RNN in PyTorch: RNN In Pytorch Gatedunits
Lecture 590 RNN in PyTorch: RNN In Pytorch Gatedunits GRU LSTM
Lecture 591 RNN in PyTorch: RNN In Pytorch Bidirectional RNN
Lecture 592 RNN in PyTorch: RNN In Pytorch Bidirectional RNN Output Shapes
Lecture 593 RNN in PyTorch: RNN In Pytorch Bidirectional RNN Output Shapes Seperation
Lecture 594 RNN in PyTorch: RNN In Pytorch Example
Lecture 595 Advanced RNN models: RNN Encoder Decoder
Lecture 596 Advanced RNN models: RNN Attention
Lecture 597 Neural Machine Translation: Introduction To Dataset And Packages
Lecture 598 Neural Machine Translation: Implementing Language Class
Lecture 599 Neural Machine Translation: Testing Language Class And Implementing Normalization
Lecture 600 Neural Machine Translation: Reading Datafile
Lecture 601 Neural Machine Translation: Reading Building Vocabulary
Lecture 602 Neural Machine Translation: EncoderRNN
Lecture 603 Neural Machine Translation: DecoderRNN
Lecture 604 Neural Machine Translation: DecoderRNN Forward Step
Lecture 605 Neural Machine Translation: DecoderRNN Helper Functions
Lecture 606 Neural Machine Translation: Training Module
Lecture 607 Neural Machine Translation: Stochastic Gradient Descent
Lecture 608 Neural Machine Translation: NMT Training
Lecture 609 Neural Machine Translation: NMT Evaluation
Section 6: Advanced Chatbots with Deep Learning & Python
Lecture 610 Links for the Course's Materials and Codes
Lecture 611 Introduction: Course and Instructor Introduction
Lecture 612 Introduction: AI Sciences Introduction
Lecture 613 Introduction: Course Description
Lecture 614 Fundamentals of Chatbots for Deep Learning: Module Introduction
Lecture 615 Fundamentals of Chatbots for Deep Learning: Conventional vs AI Chatbots
Lecture 616 Fundamentals of Chatbots for Deep Learning: Geneative vs Retrievel Chatbots
Lecture 617 Fundamentals of Chatbots for Deep Learning: Benifits of Deep Learning Chatbots
Lecture 618 Fundamentals of Chatbots for Deep Learning: Chatbots in Medical Domain
Lecture 619 Fundamentals of Chatbots for Deep Learning: Chatbots in Business
Lecture 620 Fundamentals of Chatbots for Deep Learning: Chatbots in E-Commerce
Lecture 621 Deep Learning Based Chatbot Architecture and Develpment: Module Introduction
Lecture 622 Deep Learning Based Chatbot Architecture and Develpment: Deep Learning Architect
Lecture 623 Deep Learning Based Chatbot Architecture and Develpment: Encoder Decoder
Lecture 624 Deep Learning Based Chatbot Architecture and Develpment: Steps Involved
Lecture 625 Deep Learning Based Chatbot Architecture and Develpment: Project Overview and Packages
Lecture 626 Deep Learning Based Chatbot Architecture and Develpment: Importing Libraries
Lecture 627 Deep Learning Based Chatbot Architecture and Develpment: Data Prepration
Lecture 628 Deep Learning Based Chatbot Architecture and Develpment: Develop Vocabulary
Lecture 629 Deep Learning Based Chatbot Architecture and Develpment: Max Story and Question Length
Lecture 630 Deep Learning Based Chatbot Architecture and Develpment: Tokenizer
Lecture 631 Deep Learning Based Chatbot Architecture and Develpment: Separation and Sequence
Lecture 632 Deep Learning Based Chatbot Architecture and Develpment: Vectorize Stories
Lecture 633 Deep Learning Based Chatbot Architecture and Develpment: Vectorizing Train and Test Data
Lecture 634 Deep Learning Based Chatbot Architecture and Develpment: Encoding
Lecture 635 Deep Learning Based Chatbot Architecture and Develpment: Answer and Response
Lecture 636 Deep Learning Based Chatbot Architecture and Develpment: Model Completion
Lecture 637 Deep Learning Based Chatbot Architecture and Develpment: Predictions
Section 7: Recommender Systems: An Applied Approach using Deep Learning
Lecture 638 Links for the Course's Materials and Codes
Lecture 639 Introduction: Course Outline
Lecture 640 Deep Learning Foundation for Recommender Systems: Module Introduction
Lecture 641 Deep Learning Foundation for Recommender Systems: Overview
Lecture 642 Deep Learning Foundation for Recommender Systems: Deep Learning in Recommendation Systems
Lecture 643 Deep Learning Foundation for Recommender Systems: Inference After Training
Lecture 644 Deep Learning Foundation for Recommender Systems: Inference Mechanism
Lecture 645 Deep Learning Foundation for Recommender Systems: Embeddings and User Context
Lecture 646 Deep Learning Foundation for Recommender Systems: Neutral Collaborative Filterin
Lecture 647 Deep Learning Foundation for Recommender Systems: VAE Collaborative Filtering
Lecture 648 Deep Learning Foundation for Recommender Systems: Strengths and Weaknesses of DL Models
Lecture 649 Deep Learning Foundation for Recommender Systems: Deep Learning Quiz
Lecture 650 Deep Learning Foundation for Recommender Systems: Deep Learning Quiz Solution
Lecture 651 Project Amazon Product Recommendation System: Module Overview
Lecture 652 Project Amazon Product Recommendation System: TensorFlow Recommenders
Lecture 653 Project Amazon Product Recommendation System: Two Tower Model
Lecture 654 Project Amazon Product Recommendation System: Project Overview
Lecture 655 Project Amazon Product Recommendation System: Download Libraries
Lecture 656 Project Amazon Product Recommendation System: Data Visualization with WordCloud
Lecture 657 Project Amazon Product Recommendation System: Make Tensors from DataFrame
Lecture 658 Project Amazon Product Recommendation System: Rating Our Data
Lecture 659 Project Amazon Product Recommendation System: Random Train-Test Split
Lecture 660 Project Amazon Product Recommendation System: Making the Model and Query Tower
Lecture 661 Project Amazon Product Recommendation System: Candidate Tower and Retrieval System
Lecture 662 Project Amazon Product Recommendation System: Compute Loss
Lecture 663 Project Amazon Product Recommendation System: Train and Validation
Lecture 664 Project Amazon Product Recommendation System: Accuracy vs Recommendations
Lecture 665 Project Amazon Product Recommendation System: Making Recommendations
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