Nov 26 2023 Deep Learning: Python Deep Learning Masterclass BaDshaH LEARNING / e-learning - Tutorials 11:20 0 Published 11/2023MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHzLanguage: English | Size: 25.29 GB | Duration: 63h 51mUnlock the Secrets of Deep Learning: Dive Deep into CNNs, RNNs, NLP, Chatbots, and Recommender Systems - Deep Learning What you'll learnHands-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.RequirementsUnderstanding Python fundamentals is recommended for implementing deep learning concepts covered in the course.DescriptionWelcome 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:Deep 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 CurriculumOverviewSection 1: IntroductionLecture 1 Links for the Course's Materials and CodesSection 2: Deep Learning:Deep Neural Network for Beginners Using PythonLecture 2 Introduction: Introduction to InstructorLecture 3 Introduction: Introduction to CourseLecture 4 Basics of Deep Learning: Problem to Solve Part 1Lecture 5 Basics of Deep Learning: Problem to Solve Part 2Lecture 6 Basics of Deep Learning: Problem to Solve Part 3Lecture 7 Basics of Deep Learning: Linear EquationLecture 8 Basics of Deep Learning: Linear Equation VectorizedLecture 9 Basics of Deep Learning: 3D Feature SpaceLecture 10 Basics of Deep Learning: N Dimensional SpaceLecture 11 Basics of Deep Learning: Theory of PerceptronLecture 12 Basics of Deep Learning: Implementing Basic PerceptronLecture 13 Basics of Deep Learning: Logical Gates for PerceptronsLecture 14 Basics of Deep Learning: Perceptron Training Part 1Lecture 15 Basics of Deep Learning: Perceptron Training Part 2Lecture 16 Basics of Deep Learning: Learning RateLecture 17 Basics of Deep Learning: Perceptron Training Part 3Lecture 18 Basics of Deep Learning: Perceptron AlgorithmLecture 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 SolutionsLecture 24 Basics of Deep Learning: Solution to ProblemLecture 25 Basics of Deep Learning: Error FunctionsLecture 26 Basics of Deep Learning: Discrete vs Continuous Error FunctionLecture 27 Basics of Deep Learning: Sigmoid FunctionLecture 28 Basics of Deep Learning: Multi-Class ProblemLecture 29 Basics of Deep Learning: Problem of Negative ScoresLecture 30 Basics of Deep Learning: Need of SoftmaxLecture 31 Basics of Deep Learning: Coding SoftmaxLecture 32 Basics of Deep Learning: One Hot EncodingLecture 33 Basics of Deep Learning: Maximum Likelihood Part 1Lecture 34 Basics of Deep Learning: Maximum Likelihood Part 2Lecture 35 Basics of Deep Learning: Cross EntropyLecture 36 Basics of Deep Learning: Cross Entropy FormulationLecture 37 Basics of Deep Learning: Multi Class Cross EntropyLecture 38 Basics of Deep Learning: Cross Entropy ImplementationLecture 39 Basics of Deep Learning: Sigmoid Function ImplementationLecture 40 Basics of Deep Learning: Output Function ImplementationLecture 41 Deep Learning: Introduction to Gradient DecentLecture 42 Deep Learning: Convex FunctionsLecture 43 Deep Learning: Use of DerivativesLecture 44 Deep Learning: How Gradient Decent WorksLecture 45 Deep Learning: Gradient StepLecture 46 Deep Learning: Logistic Regression AlgorithmLecture 47 Deep Learning: Data Visualization and ReadingLecture 48 Deep Learning: Updating Weights in PythonLecture 49 Deep Learning: Implementing Logistic RegressionLecture 50 Deep Learning: Visualization and ResultsLecture 51 Deep Learning: Gradient Decent vs PerceptronLecture 52 Deep Learning: Linear to Non Linear BoundariesLecture 53 Deep Learning: Combining ProbabilitiesLecture 54 Deep Learning: Weighted SumsLecture 55 Deep Learning: Neural Network ArchitectureLecture 56 Deep Learning: Layers and DEEP NetworksLecture 57 Deep Learning:Multi Class ClassificationLecture 58 Deep Learning: Basics of Feed ForwardLecture 59 Deep Learning: Feed Forward for DEEP NetLecture 60 Deep Learning: Deep Learning Algo OverviewLecture 61 Deep Learning: Basics of Back PropagationLecture 62 Deep Learning: Updating WeightsLecture 63 Deep Learning: Chain Rule for BackPropagationLecture 64 Deep Learning: Sigma PrimeLecture 65 Deep Learning: Data Analysis NN ImplementationLecture 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 OverfittingLecture 73 Optimizations: Early StoppingLecture 74 Optimizations: QuizLecture 75 Optimizations: Solution & RegularizationLecture 76 Optimizations: L1 & L2 RegularizationLecture 77 Optimizations: DropoutLecture 78 Optimizations: Local Minima ProblemLecture 79 Optimizations: Random Restart SolutionLecture 80 Optimizations: Vanishing Gradient ProblemLecture 81 Optimizations: Other Activation FunctionsLecture 82 Final Project: Final Project Part 1Lecture 83 Final Project: Final Project Part 2Lecture 84 Final Project: Final Project Part 3Lecture 85 Final Project: Final Project Part 4Lecture 86 Final Project: Final Project Part 5Section 3: Deep Learning CNN: Convolutional Neural Networks with PythonLecture 87 Link to Github to get the Python NotebooksLecture 88 Introduction: Instructor IntroductionLecture 89 Introduction: Why CNNLecture 90 Introduction: Focus of the CourseLecture 91 Image Processing: Gray Scale ImagesLecture 92 Image Processing: Gray Scale Images QuizLecture 93 Image Processing: Gray Scale Images SolutionLecture 94 Image Processing: RGB ImagesLecture 95 Image Processing: RGB Images QuizLecture 96 Image Processing: RGB Images SolutionLecture 97 Image Processing: Reading and Showing Images in PythonLecture 98 Image Processing: Reading and Showing Images in Python QuizLecture 99 Image Processing: Reading and Showing Images in Python SolutionLecture 100 Image Processing: Converting an Image to Grayscale in PythonLecture 101 Image Processing: Converting an Image to Grayscale in Python QuizLecture 102 Image Processing: Converting an Image to Grayscale in Python SolutionLecture 103 Image Processing: Image FormationLecture 104 Image Processing: Image Formation QuizLecture 105 Image Processing: Image Formation SolutionLecture 106 Image Processing: Image Blurring 1Lecture 107 Image Processing: Image Blurring 1 QuizLecture 108 Image Processing: Image Blurring 1 SolutionLecture 109 Image Processing: Image Blurring 2Lecture 110 Image Processing: Image Blurring 2 QuizLecture 111 Image Processing: Image Blurring 2 SolutionLecture 112 Image Processing: General Image FilteringLecture 113 Image Processing: ConvolutionLecture 114 Image Processing: Edge DetectionLecture 115 Image Processing: Image SharpeningLecture 116 Image Processing: Implementation of Image Blurring Edge Detection Image Sharpening in PythonLecture 117 Image Processing: Parameteric Shape DetectionLecture 118 Image Processing: Image Processing ActivityLecture 119 Image Processing: Image Processing Activity SolutionLecture 120 Object Detection: Introduction to Object DetectionLecture 121 Object Detection: Classification PipleLineLecture 122 Object Detection: Classification PipleLine QuizLecture 123 Object Detection: Classification PipleLine SolutionLecture 124 Object Detection: Sliding Window ImplementationLecture 125 Object Detection: Shift Scale Rotation InvarianceLecture 126 Object Detection: Shift Scale Rotation Invariance ExerciseLecture 127 Object Detection: Person DetectionLecture 128 Object Detection: HOG FeaturesLecture 129 Object Detection: HOG Features ExerciseLecture 130 Object Detection: Hand Engineering vs CNNsLecture 131 Object Detection: Object Detection ActivityLecture 132 Deep Neural Network Overview: Neuron and PerceptronLecture 133 Deep Neural Network Overview: DNN ArchitectureLecture 134 Deep Neural Network Overview: DNN Architecture QuizLecture 135 Deep Neural Network Overview: DNN Architecture SolutionLecture 136 Deep Neural Network Overview: FeedForward FullyConnected MLPLecture 137 Deep Neural Network Overview: Calculating Number of Weights of DNNLecture 138 Deep Neural Network Overview: Calculating Number of Weights of DNN QuizLecture 139 Deep Neural Network Overview: Calculating Number of Weights of DNN SolutionLecture 140 Deep Neural Network Overview: Number of Nuerons vs Number of LayersLecture 141 Deep Neural Network Overview: Discriminative vs Generative LearningLecture 142 Deep Neural Network Overview: Universal Approximation TheroremLecture 143 Deep Neural Network Overview: Why DepthLecture 144 Deep Neural Network Overview: Decision Boundary in DNNLecture 145 Deep Neural Network Overview: Decision Boundary in DNN QuizLecture 146 Deep Neural Network Overview: Decision Boundary in DNN SolutionLecture 147 Deep Neural Network Overview: BiasTermLecture 148 Deep Neural Network Overview: BiasTerm QuizLecture 149 Deep Neural Network Overview: BiasTerm SolutionLecture 150 Deep Neural Network Overview: Activation FunctionLecture 151 Deep Neural Network Overview: Activation Function QuizLecture 152 Deep Neural Network Overview: Activation Function SolutionLecture 153 Deep Neural Network Overview: DNN Training ParametersLecture 154 Deep Neural Network Overview: DNN Training Parameters QuizLecture 155 Deep Neural Network Overview: DNN Training Parameters SolutionLecture 156 Deep Neural Network Overview: Gradient DescentLecture 157 Deep Neural Network Overview: BackPropagationLecture 158 Deep Neural Network Overview: Training DNN AnimantionLecture 159 Deep Neural Network Overview: Weigth InitializationLecture 160 Deep Neural Network Overview: Weigth Initialization QuizLecture 161 Deep Neural Network Overview: Weigth Initialization SolutionLecture 162 Deep Neural Network Overview: Batch miniBatch Stocastic Gradient DescentLecture 163 Deep Neural Network Overview: Batch NormalizationLecture 164 Deep Neural Network Overview: Rprop and MomentumLecture 165 Deep Neural Network Overview: Rprop and Momentum QuizLecture 166 Deep Neural Network Overview: Rprop and Momentum SolutionLecture 167 Deep Neural Network Overview: Convergence AnimationLecture 168 Deep Neural Network Overview: DropOut, Early Stopping and HyperparametersLecture 169 Deep Neural Network Overview: DropOut, Early Stopping and Hyperparameters QuizLecture 170 Deep Neural Network Overview: DropOut, Early Stopping and Hyperparameters SolutionLecture 171 Deep Neural Network Architecture: Convolution RevisitedLecture 172 Deep Neural Network Architecture: Implementing Convolution in Python RevisitedLecture 173 Deep Neural Network Architecture: Why ConvolutionLecture 174 Deep Neural Network Architecture: Filters Padding StridesLecture 175 Deep Neural Network Architecture: Padding ImageLecture 176 Deep Neural Network Architecture: Pooling TensorsLecture 177 Deep Neural Network Architecture: CNN ExampleLecture 178 Deep Neural Network Architecture: Convolution and Pooling DetailsLecture 179 Deep Neural Network Architecture: Maxpooling ExerciseLecture 180 Deep Neural Network Architecture: NonVectorized Implementations of Conv2d and Pool2dLecture 181 Deep Neural Network Architecture: Deep Neural Network Architecture ActivityLecture 182 Gradient Descent in CNNs: Example SetupLecture 183 Gradient Descent in CNNs: Why DerivatiesLecture 184 Gradient Descent in CNNs: Why Derivaties QuizLecture 185 Gradient Descent in CNNs: Why Derivaties SolutionLecture 186 Gradient Descent in CNNs: What is Chain RuleLecture 187 Gradient Descent in CNNs: Applying Chain RuleLecture 188 Gradient Descent in CNNs: Gradients of MaxPooling LayerLecture 189 Gradient Descent in CNNs: Gradients of MaxPooling Layer QuizLecture 190 Gradient Descent in CNNs: Gradients of MaxPooling Layer SolutionLecture 191 Gradient Descent in CNNs: Gradients of Convolutional LayerLecture 192 Gradient Descent in CNNs: Extending To Multiple FiltersLecture 193 Gradient Descent in CNNs: Extending to Multiple LayersLecture 194 Gradient Descent in CNNs: Extending to Multiple Layers QuizLecture 195 Gradient Descent in CNNs: Extending to Multiple Layers SolutionLecture 196 Gradient Descent in CNNs: Implementation in Numpy ForwardPassLecture 197 Gradient Descent in CNNs: Implementation in Numpy BackwardPass 1Lecture 198 Gradient Descent in CNNs: Implementation in Numpy BackwardPass 2Lecture 199 Gradient Descent in CNNs: Implementation in Numpy BackwardPass 3Lecture 200 Gradient Descent in CNNs: Implementation in Numpy BackwardPass 4Lecture 201 Gradient Descent in CNNs: Implementation in Numpy BackwardPass 5Lecture 202 Gradient Descent in CNNs: Gradient Descent in CNNs ActivityLecture 203 Introduction to TensorFlow: IntroductionLecture 204 Introduction to TensorFlow: FashionMNIST Example Plan Neural NetworkLecture 205 Introduction to TensorFlow: FashionMNIST Example CNNLecture 206 Introduction to TensorFlow: Introduction to TensorFlow ActivityLecture 207 Classical CNNs: LeNetLecture 208 Classical CNNs: LeNet QuizLecture 209 Classical CNNs: LeNet SolutionLecture 210 Classical CNNs: AlexNetLecture 211 Classical CNNs: VGGLecture 212 Classical CNNs: InceptionNetLecture 213 Classical CNNs: GoogLeNetLecture 214 Classical CNNs: ResnetLecture 215 Classical CNNs: Classical CNNs ActivityLecture 216 Transfer Learning: What is Transfer learningLecture 217 Transfer Learning: Why Transfer LearningLecture 218 Transfer Learning: Practical TipsLecture 219 Transfer Learning: Project in TensorFlowLecture 220 Transfer Learning: ImageNet ChallengeLecture 221 Transfer Learning: Transfer Learning ActivityLecture 222 Yolo: Image Classfication RevisitedLecture 223 Yolo: Sliding Window Object LocalizationLecture 224 Yolo: Sliding Window Efficient ImplementationLecture 225 Yolo: Yolo IntroductionLecture 226 Yolo: Yolo Training Data GenerationLecture 227 Yolo: Yolo Anchor BoxesLecture 228 Yolo: Yolo AlgorithmLecture 229 Yolo: Yolo Non Maxima SupressionLecture 230 Yolo: RCNNLecture 231 Yolo: Yolo ActivityLecture 232 Face Verification: Problem SetupLecture 233 Face Verification: Project ImplementationLecture 234 Face Verification: Face Verification ActivityLecture 235 Neural Style Transfer: Problem SetupLecture 236 Neural Style Transfer: Implementation Tensorflow HubSection 4: Deep Learning: Recurrent Neural Networks with PythonLecture 237 Link to oneDrive and Github to get the Python NotebooksLecture 238 Introduction: Introduction to Instructor and AisciencesLecture 239 Introduction: Introduction To InstructorLecture 240 Introduction: Focus of the CourseLecture 241 Applications of RNN (Motivation): Human Activity RecognitionLecture 242 Applications of RNN (Motivation): Image CaptioningLecture 243 Applications of RNN (Motivation): Machine TranslationLecture 244 Applications of RNN (Motivation): Speech RecognitionLecture 245 Applications of RNN (Motivation): Stock Price PredictionsLecture 246 Applications of RNN (Motivation): When to Model RNNLecture 247 Applications of RNN (Motivation): ActivityLecture 248 DNN Overview: Why PyTorchLecture 249 DNN Overview: PyTorch Installation and Tensors IntroductionLecture 250 DNN Overview: Automatic Diffrenciation Pytorch NewLecture 251 DNN Overview: Why DNNs in Machine LearningLecture 252 DNN Overview: Representational Power and Data Utilization Capacity of DNNLecture 253 DNN Overview: PerceptronLecture 254 DNN Overview: Perceptron ExerciseLecture 255 DNN Overview: Perceptron Exercise SolutionLecture 256 DNN Overview: Perceptron ImplementationLecture 257 DNN Overview: DNN ArchitectureLecture 258 DNN Overview: DNN Architecture ExerciseLecture 259 DNN Overview: DNN Architecture Exercise SolutionLecture 260 DNN Overview: DNN ForwardStep ImplementationLecture 261 DNN Overview: DNN Why Activation Function Is RequiredLecture 262 DNN Overview: DNN Why Activation Function Is Required ExerciseLecture 263 DNN Overview: DNN Why Activation Function Is Required Exercise SolutionLecture 264 DNN Overview: DNN Properties Of Activation FunctionLecture 265 DNN Overview: DNN Activation Functions In PytorchLecture 266 DNN Overview: DNN What Is Loss FunctionLecture 267 DNN Overview: DNN What Is Loss Function ExerciseLecture 268 DNN Overview: DNN What Is Loss Function Exercise SolutionLecture 269 DNN Overview: DNN What Is Loss Function Exercise 02Lecture 270 DNN Overview: DNN What Is Loss Function Exercise 02 SolutionLecture 271 DNN Overview: DNN Loss Function In PytorchLecture 272 DNN Overview: DNN Gradient DescentLecture 273 DNN Overview: DNN Gradient Descent ExerciseLecture 274 DNN Overview: DNN Gradient Descent Exercise SolutionLecture 275 DNN Overview: DNN Gradient Descent ImplementationLecture 276 DNN Overview: DNN Gradient Descent Stochastic Batch MinibatchLecture 277 DNN Overview: DNN Implemenation Gradient StepLecture 278 DNN Overview: DNN Implemenation Stochastic Gradient DescentLecture 279 DNN Overview: DNN Gradient Descent SummaryLecture 280 DNN Overview: DNN Implemenation Batch Gradient DescentLecture 281 DNN Overview: DNN Implemenation Minibatch Gradient DescentLecture 282 DNN Overview: DNN Implemenation In PyTorchLecture 283 DNN Overview: DNN Weights InitializationsLecture 284 DNN Overview: DNN Learning RateLecture 285 DNN Overview: DNN Batch NormalizationLecture 286 DNN Overview: DNN batch Normalization ImplementationLecture 287 DNN Overview: DNN OptimizationsLecture 288 DNN Overview: DNN DropoutLecture 289 DNN Overview: DNN Dropout In PyTorchLecture 290 DNN Overview: DNN Early StoppingLecture 291 DNN Overview: DNN HyperparametersLecture 292 DNN Overview: DNN Pytorch CIFAR10 ExampleLecture 293 RNN Architecture: Introduction to ModuleLecture 294 RNN Architecture: Fixed Length Memory ModelLecture 295 RNN Architecture: Fixed Length Memory Model ExerciseLecture 296 RNN Architecture: Fixed Length Memory Model Exercise Solution Part 01Lecture 297 RNN Architecture: Fixed Length Memory Model Exercise Solution Part 02Lecture 298 RNN Architecture: Infinite Memory ArchitectureLecture 299 RNN Architecture: Infinite Memory Architecture ExerciseLecture 300 RNN Architecture: Infinite Memory Architecture SolutionLecture 301 RNN Architecture: Weight SharingLecture 302 RNN Architecture: NotationsLecture 303 RNN Architecture: ManyToMany ModelLecture 304 RNN Architecture: ManyToMany Model Exercise 01Lecture 305 RNN Architecture: ManyToMany Model Solution 01Lecture 306 RNN Architecture: ManyToMany Model Exercise 02Lecture 307 RNN Architecture: ManyToMany Model Solution 02Lecture 308 RNN Architecture: ManyToOne ModelLecture 309 RNN Architecture: OneToMany Model ExerciseLecture 310 RNN Architecture: OneToMany Model SolutionLecture 311 RNN Architecture: OneToMany ModelLecture 312 RNN Architecture: ManyToOne Model ExerciseLecture 313 RNN Architecture: ManyToOne Model SolutionLecture 314 RNN Architecture: Activity Many to OneLecture 315 RNN Architecture: Activity Many to One ExerciseLecture 316 RNN Architecture: Activity Many to One SolutionLecture 317 RNN Architecture: ManyToMany Different Sizes ModelLecture 318 RNN Architecture: Activity Many to Many NmtLecture 319 RNN Architecture: Models SummaryLecture 320 RNN Architecture: Deep RNNsLecture 321 RNN Architecture: Deep RNNs ExerciseLecture 322 RNN Architecture: Deep RNNs SolutionLecture 323 Gradient Decsent in RNN: Introduction to Gradient Descent ModuleLecture 324 Gradient Decsent in RNN: Example SetupLecture 325 Gradient Decsent in RNN: EquationsLecture 326 Gradient Decsent in RNN: Equations ExerciseLecture 327 Gradient Decsent in RNN: Equations SolutionLecture 328 Gradient Decsent in RNN: Loss FunctionLecture 329 Gradient Decsent in RNN: Why GradientsLecture 330 Gradient Decsent in RNN: Why Gradients ExerciseLecture 331 Gradient Decsent in RNN: Why Gradients SolutionLecture 332 Gradient Decsent in RNN: Chain RuleLecture 333 Gradient Decsent in RNN: Chain Rule in ActionLecture 334 Gradient Decsent in RNN: BackPropagation Through TimeLecture 335 Gradient Decsent in RNN: ActivityLecture 336 RNN implementation: Automatic DiffrenciationLecture 337 RNN implementation: Automatic Diffrenciation PytorchLecture 338 RNN implementation: Language Modeling Next Word Prediction Vocabulary IndexLecture 339 RNN implementation: Language Modeling Next Word Prediction Vocabulary Index EmbeddingsLecture 340 RNN implementation: Language Modeling Next Word Prediction RNN ArchitectureLecture 341 RNN implementation: Language Modeling Next Word Prediction Python 1Lecture 342 RNN implementation: Language Modeling Next Word Prediction Python 2Lecture 343 RNN implementation: Language Modeling Next Word Prediction Python 3Lecture 344 RNN implementation: Language Modeling Next Word Prediction Python 4Lecture 345 RNN implementation: Language Modeling Next Word Prediction Python 5Lecture 346 RNN implementation: Language Modeling Next Word Prediction Python 6Lecture 347 Sentiment Classification using RNN: Vocabulary ImplementationLecture 348 Sentiment Classification using RNN: Vocabulary Implementation HelpersLecture 349 Sentiment Classification using RNN: Vocabulary Implementation From FileLecture 350 Sentiment Classification using RNN: VectorizerLecture 351 Sentiment Classification using RNN: RNN Setup 1Lecture 352 Sentiment Classification using RNN: RNN Setup 2Lecture 353 Sentiment Classification using RNN: WhatNextLecture 354 Vanishing Gradients in RNN: Introduction to Better RNNs ModuleLecture 355 Vanishing Gradients in RNN: Introduction Vanishing Gradients in RNNLecture 356 Vanishing Gradients in RNN: GRULecture 357 Vanishing Gradients in RNN: GRU OptionalLecture 358 Vanishing Gradients in RNN: LSTMLecture 359 Vanishing Gradients in RNN: LSTM OptionalLecture 360 Vanishing Gradients in RNN: Bidirectional RNNLecture 361 Vanishing Gradients in RNN: Attention ModelLecture 362 Vanishing Gradients in RNN: Attention Model OptionalLecture 363 TensorFlow: Introduction to TensorFlowLecture 364 TensorFlow: TensorFlow Text Classification Example using RNNLecture 365 Project I: Book Writer: IntroductionLecture 366 Project I: Book Writer: Data MappingLecture 367 Project I: Book Writer: Modling RNN ArchitectureLecture 368 Project I: Book Writer: Modling RNN Model in TensorFlowLecture 369 Project I: Book Writer: Modling RNN Model TrainingLecture 370 Project I: Book Writer: Modling RNN Model Text GenerationLecture 371 Project I: Book Writer: ActivityLecture 372 Project II: Stock Price Prediction: Problem StatementLecture 373 Project II: Stock Price Prediction: Data SetLecture 374 Project II: Stock Price Prediction: Data PreprationLecture 375 Project II: Stock Price Prediction: RNN Model Training and EvaluationLecture 376 Project II: Stock Price Prediction: ActivityLecture 377 Further Readings and Resourses: Further Readings and Resourses 1Section 5: NLP-Natural Language Processing in Python(Theory & Projects)Lecture 378 Links for the Course's Materials and CodesLecture 379 Introduction: Introduction to CourseLecture 380 Introduction: Introduction to InstructorLecture 381 Introduction: Introduction to Co-InstructorLecture 382 Introduction: Course IntroductionLecture 383 Introduction(Regular Expressions): What Is Regular ExpressionLecture 384 Introduction(Regular Expressions): Why Regular ExpressionLecture 385 Introduction(Regular Expressions): ELIZA ChatbotLecture 386 Introduction(Regular Expressions): Python Regular Expression PackageLecture 387 Meta Characters(Regular Expressions): Meta CharactersLecture 388 Meta Characters(Regular Expressions): Meta Characters Bigbrackets ExerciseLecture 389 Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise SolutionLecture 390 Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise 2Lecture 391 Meta Characters(Regular Expressions): Meta Characters Bigbrackets Exercise 2 SolutionLecture 392 Meta Characters(Regular Expressions): Meta Characters CapLecture 393 Meta Characters(Regular Expressions): Meta Characters Cap Exercise 3Lecture 394 Meta Characters(Regular Expressions): Meta Characters Cap Exercise 3 SolutionLecture 395 Meta Characters(Regular Expressions): BackslashLecture 396 Meta Characters(Regular Expressions): Backslash ContinuedLecture 397 Meta Characters(Regular Expressions): Backslash Continued 01Lecture 398 Meta Characters(Regular Expressions): Backslash Squared Brackets ExerciseLecture 399 Meta Characters(Regular Expressions): Backslash Squared Brackets Exercise SolutionLecture 400 Meta Characters(Regular Expressions): Backslash Squared Brackets Exercise Another SolutionLecture 401 Meta Characters(Regular Expressions): Backslash ExerciseLecture 402 Meta Characters(Regular Expressions): Backslash Exercise Solution And Special Sequences ExerciseLecture 403 Meta Characters(Regular Expressions): Solution And Special Sequences Exercise SolutionLecture 404 Meta Characters(Regular Expressions): Meta Character AsteriskLecture 405 Meta Characters(Regular Expressions): Meta Character Asterisk ExerciseLecture 406 Meta Characters(Regular Expressions): Meta Character Asterisk Exercise SolutionLecture 407 Meta Characters(Regular Expressions): Meta Character Asterisk HomeworkLecture 408 Meta Characters(Regular Expressions): Meta Character Asterisk GreedymatchingLecture 409 Meta Characters(Regular Expressions): Meta Character Plus And QuestionmarkLecture 410 Meta Characters(Regular Expressions): Meta Character Curly Brackets ExerciseLecture 411 Meta Characters(Regular Expressions): Meta Character Curly Brackets Exercise SolutionLecture 412 Pattern Objects: Pattern ObjectsLecture 413 Pattern Objects: Pattern Objects Match Method ExersizeLecture 414 Pattern Objects: Pattern Objects Match Method Exersize SolutionLecture 415 Pattern Objects: Pattern Objects Match Method Vs Search MethodLecture 416 Pattern Objects: Pattern Objects Finditer MethodLecture 417 Pattern Objects: Pattern Objects Finditer Method Exersize SolutionLecture 418 More Meta Characters: Meta Characters Logical OrLecture 419 More Meta Characters: Meta Characters Beginning And End PatternsLecture 420 More Meta Characters: Meta Characters ParanthesisLecture 421 String Modification: String ModificationLecture 422 String Modification: Word Tokenizer Using Split MethodLecture 423 String Modification: Sub Method ExerciseLecture 424 String Modification: Sub Method Exercise SolutionLecture 425 Words and Tokens: What Is A WordLecture 426 Words and Tokens: Definition Of Word Is Task DependentLecture 427 Words and Tokens: Vocabulary And CorpusLecture 428 Words and Tokens: TokensLecture 429 Words and Tokens: Tokenization In SpacyLecture 430 Sentiment Classification: Yelp Reviews Classification Mini Project IntroductionLecture 431 Sentiment Classification: Yelp Reviews Classification Mini Project Vocabulary InitializationLecture 432 Sentiment Classification: Yelp Reviews Classification Mini Project Adding Tokens To VocabularyLecture 433 Sentiment Classification: Yelp Reviews Classification Mini Project Look Up Functions In VocabularyLecture 434 Sentiment Classification: Yelp Reviews Classification Mini Project Building Vocabulary From DataLecture 435 Sentiment Classification: Yelp Reviews Classification Mini Project One Hot EncodingLecture 436 Sentiment Classification: Yelp Reviews Classification Mini Project One Hot Encoding ImplementationLecture 437 Sentiment Classification: Yelp Reviews Classification Mini Project Encoding DocumentsLecture 438 Sentiment Classification: Yelp Reviews Classification Mini Project Encoding Documents ImplementationLecture 439 Sentiment Classification: Yelp Reviews Classification Mini Project Train Test SplitsLecture 440 Sentiment Classification: Yelp Reviews Classification Mini Project FeaturecomputationLecture 441 Sentiment Classification: Yelp Reviews Classification Mini Project ClassificationLecture 442 Language Independent Tokenization: Tokenization In Detial IntroductionLecture 443 Language Independent Tokenization: Tokenization Is HardLecture 444 Language Independent Tokenization: Tokenization Byte Pair EncodingLecture 445 Language Independent Tokenization: Tokenization Byte Pair Encoding ExampleLecture 446 Language Independent Tokenization: Tokenization Byte Pair Encoding On Test DataLecture 447 Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation GetpaircountsLecture 448 Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation MergeincorpusLecture 449 Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE TrainingLecture 450 Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE EncodingLecture 451 Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE Encoding One PairLecture 452 Language Independent Tokenization: Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair 1Lecture 453 Text Nomalization: Word Normalization Case FoldingLecture 454 Text Nomalization: Word Normalization LematizationLecture 455 Text Nomalization: Word Normalization StemmingLecture 456 Text Nomalization: Word Normalization Sentence SegmentationLecture 457 String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance IntroLecture 458 String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance ExampleLecture 459 String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Table FillingLecture 460 String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Dynamic ProgrammingLecture 461 String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance PsudocodeLecture 462 String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance ImplementationLecture 463 String Matching and Spelling Correction: Spelling Correction Minimum Edit Distance Implementation BugfixingLecture 464 String Matching and Spelling Correction: Spelling Correction ImplementationLecture 465 Language Modeling: What Is A Language ModelLecture 466 Language Modeling: Language Model Formal DefinitionLecture 467 Language Modeling: Language Model Curse Of DimensionalityLecture 468 Language Modeling: Language Model Markov Assumption And N-GramsLecture 469 Language Modeling: Language Model Implementation SetupLecture 470 Language Modeling: Language Model Implementation Ngrams FunctionLecture 471 Language Modeling: Language Model Implementation Update Counts FunctionLecture 472 Language Modeling: Language Model Implementation Probability Model FuncitonLecture 473 Language Modeling: Language Model Implementation Reading CorpusLecture 474 Language Modeling: Language Model Implementation Sampling TextLecture 475 Topic Modelling with Word and Document Representations: One Hot VectorsLecture 476 Topic Modelling with Word and Document Representations: One Hot Vectors ImplementatonLecture 477 Topic Modelling with Word and Document Representations: One Hot Vectors LimitationsLecture 478 Topic Modelling with Word and Document Representations: One Hot Vectors Uses As Target LabelingLecture 479 Topic Modelling with Word and Document Representations: Term Frequency For Document RepresentationsLecture 480 Topic Modelling with Word and Document Representations: Term Frequency For Document Representations ImplementationsLecture 481 Topic Modelling with Word and Document Representations: Term Frequency For Word RepresentationsLecture 482 Topic Modelling with Word and Document Representations: TFIDF For Document RepresentationsLecture 483 Topic Modelling with Word and Document Representations: TFIDF For Document Representations Implementation Reading CorpusLecture 484 Topic Modelling with Word and Document Representations: TFIDF For Document Representations Implementation Computing Document FrequencyLecture 485 Topic Modelling with Word and Document Representations: TFIDF For Document Representations Implementation Computing TFIDFLecture 486 Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 1Lecture 487 Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 3Lecture 488 Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 4Lecture 489 Topic Modelling with Word and Document Representations: Topic Modeling With TFIDF 5Lecture 490 Topic Modelling with Word and Document Representations: Topic Modeling With GensimLecture 491 Word Embeddings LSI: Word Co-occurrence MatrixLecture 492 Word Embeddings LSI: Word Co-occurrence Matrix vs Document-term MatrixLecture 493 Word Embeddings LSI: Word Co-occurrence Matrix Implementation Preparing DataLecture 494 Word Embeddings LSI: Word Co-occurrence Matrix Implementation Preparing Data 2Lecture 495 Word Embeddings LSI: Word Co-occurrence Matrix Implementation Preparing Data Getting VocabularyLecture 496 Word Embeddings LSI: Word Co-occurrence Matrix Implementation Final FunctionLecture 497 Word Embeddings LSI: Word Co-occurrence Matrix Implementation Handling Memory Issues On Large CorpLecture 498 Word Embeddings LSI: Word Co-occurrence Matrix SparsityLecture 499 Word Embeddings LSI: Word Co-occurrence Matrix Positive Point Wise Mutual Information PPMILecture 500 Word Embeddings LSI: PCA For Dense EmbeddingsLecture 501 Word Embeddings LSI: Latent Semantic AnalysisLecture 502 Word Embeddings LSI: Latent Semantic Analysis ImplementationLecture 503 Word Semantics: Cosine SimilarityLecture 504 Word Semantics: Cosine Similarity Geting Norms Of VectorsLecture 505 Word Semantics: Cosine Similarity Normalizing VectorsLecture 506 Word Semantics: Cosine Similarity With More Than One VectorsLecture 507 Word Semantics: Cosine Similarity Getting Most Similar Words In The VocabularyLecture 508 Word Semantics: Cosine Similarity Getting Most Similar Words In The Vocabulary Fixingbug Of DLecture 509 Word Semantics: Cosine Similarity Word2Vec EmbeddingsLecture 510 Word Semantics: Words AnalogiesLecture 511 Word Semantics: Words Analogies Implemenation 1Lecture 512 Word Semantics: Words Analogies Implemenation 2Lecture 513 Word Semantics: Words VisualizationsLecture 514 Word Semantics: Words Visualizations ImplementaionLecture 515 Word Semantics: Words Visualizations Implementaion 2Lecture 516 Word2vec: Static And Dynamic EmbeddingsLecture 517 Word2vec: Self SupervisionLecture 518 Word2vec: Word2Vec Algorithm AbstractLecture 519 Word2vec: Word2Vec Why Negative SamplingLecture 520 Word2vec: Word2Vec What Is Skip GramLecture 521 Word2vec: Word2Vec How To Define Probability LawLecture 522 Word2vec: Word2Vec SigmoidLecture 523 Word2vec: Word2Vec Formalizing Loss FunctionLecture 524 Word2vec: Word2Vec Loss FunctionLecture 525 Word2vec: Word2Vec Gradient Descent StepLecture 526 Word2vec: Word2Vec Implemenation Preparing DataLecture 527 Word2vec: Word2Vec Implemenation Gradient StepLecture 528 Word2vec: Word2Vec Implemenation Driver FunctionLecture 529 Need of Deep Learning for NLP: Why RNNs For NLPLecture 530 Need of Deep Learning for NLP: Pytorch Installation And Tensors IntroductionLecture 531 Need of Deep Learning for NLP: Automatic Diffrenciation PytorchLecture 532 Introduction(NLP with Deep Learning DNN): Why DNNs In Machine LearningLecture 533 Introduction(NLP with Deep Learning DNN): Representational Power And Data Utilization Capacity Of DNNLecture 534 Introduction(NLP with Deep Learning DNN): PerceptronLecture 535 Introduction(NLP with Deep Learning DNN): Perceptron ImplementationLecture 536 Introduction(NLP with Deep Learning DNN): DNN ArchitectureLecture 537 Introduction(NLP with Deep Learning DNN): DNN Forwardstep ImplementationLecture 538 Introduction(NLP with Deep Learning DNN): DNN Why Activation Function Is RequireLecture 539 Introduction(NLP with Deep Learning DNN): DNN Properties Of Activation FunctionLecture 540 Introduction(NLP with Deep Learning DNN): DNN Activation Functions In PytorchLecture 541 Introduction(NLP with Deep Learning DNN): DNN What Is Loss FunctionLecture 542 Introduction(NLP with Deep Learning DNN): DNN Loss Function In PytorchLecture 543 Training(NLP with DNN): DNN Gradient DescentLecture 544 Training(NLP with DNN): DNN Gradient Descent ImplementationLecture 545 Training(NLP with DNN): DNN Gradient Descent Stochastic Batch MinibatchLecture 546 Training(NLP with DNN): DNN Gradient Descent SummaryLecture 547 Training(NLP with DNN): DNN Implemenation Gradient StepLecture 548 Training(NLP with DNN): DNN Implemenation Stochastic Gradient DescentLecture 549 Training(NLP with DNN): DNN Implemenation Batch Gradient DescentLecture 550 Training(NLP with DNN): DNN Implemenation Minibatch Gradient DescentLecture 551 Training(NLP with DNN): DNN Implemenation In PytorchLecture 552 Hyper parameters(NLP with DNN): DNN Weights InitializationsLecture 553 Hyper parameters(NLP with DNN): DNN Learning RateLecture 554 Hyper parameters(NLP with DNN): DNN Batch NormalizationLecture 555 Hyper parameters(NLP with DNN): DNN Batch Normalization ImplementationLecture 556 Hyper parameters(NLP with DNN): DNN OptimizationsLecture 557 Hyper parameters(NLP with DNN): DNN DropoutLecture 558 Hyper parameters(NLP with DNN): DNN Dropout In PytorchLecture 559 Hyper parameters(NLP with DNN): DNN Early StoppingLecture 560 Hyper parameters(NLP with DNN): DNN HyperparametersLecture 561 Hyper parameters(NLP with DNN): DNN Pytorch CIFAR10 ExampleLecture 562 Introduction(NLP with Deep Learning RNN): What Is RNNLecture 563 Introduction(NLP with Deep Learning RNN): Understanding RNN With A Simple ExampleLecture 564 Introduction(NLP with Deep Learning RNN): RNN Applications Human Activity RecognitionLecture 565 Introduction(NLP with Deep Learning RNN): RNN Applications Image CaptioningLecture 566 Introduction(NLP with Deep Learning RNN): RNN Applications Machine TranslationLecture 567 Introduction(NLP with Deep Learning RNN): RNN Applications Speech Recognition Stock Price PredictionLecture 568 Introduction(NLP with Deep Learning RNN): RNN ModelsLecture 569 Mini-project Language Modelling: Language Modeling Next Word PredictionLecture 570 Mini-project Language Modelling: Language Modeling Next Word Prediction Vocabulary IndexLecture 571 Mini-project Language Modelling: Language Modeling Next Word Prediction Vocabulary Index EmbeddingsLecture 572 Mini-project Language Modelling: Language Modeling Next Word Prediction Rnn ArchitectureLecture 573 Mini-project Language Modelling: Language Modeling Next Word Prediction Python 1Lecture 574 Mini-project Language Modelling: Language Modeling Next Word Prediction Python 2Lecture 575 Mini-project Language Modelling: Language Modeling Next Word Prediction Python 3Lecture 576 Mini-project Language Modelling: Language Modeling Next Word Prediction Python 4Lecture 577 Mini-project Language Modelling: Language Modeling Next Word Prediction Python 5Lecture 578 Mini-project Language Modelling: Language Modeling Next Word Prediction Python 6Lecture 579 Mini-project Sentiment Classification: Vocabulary ImplementationLecture 580 Mini-project Sentiment Classification: Vocabulary Implementation HelpersLecture 581 Mini-project Sentiment Classification: Vocabulary Implementation From FileLecture 582 Mini-project Sentiment Classification: VectorizerLecture 583 Mini-project Sentiment Classification: RNN SetupLecture 584 Mini-project Sentiment Classification: RNN Setup 1Lecture 585 RNN in PyTorch: RNN In Pytorch IntroductionLecture 586 RNN in PyTorch: RNN In Pytorch Embedding LayerLecture 587 RNN in PyTorch: RNN In Pytorch Nn RnnLecture 588 RNN in PyTorch: RNN In Pytorch Output ShapesLecture 589 RNN in PyTorch: RNN In Pytorch GatedunitsLecture 590 RNN in PyTorch: RNN In Pytorch Gatedunits GRU LSTMLecture 591 RNN in PyTorch: RNN In Pytorch Bidirectional RNNLecture 592 RNN in PyTorch: RNN In Pytorch Bidirectional RNN Output ShapesLecture 593 RNN in PyTorch: RNN In Pytorch Bidirectional RNN Output Shapes SeperationLecture 594 RNN in PyTorch: RNN In Pytorch ExampleLecture 595 Advanced RNN models: RNN Encoder DecoderLecture 596 Advanced RNN models: RNN AttentionLecture 597 Neural Machine Translation: Introduction To Dataset And PackagesLecture 598 Neural Machine Translation: Implementing Language ClassLecture 599 Neural Machine Translation: Testing Language Class And Implementing NormalizationLecture 600 Neural Machine Translation: Reading DatafileLecture 601 Neural Machine Translation: Reading Building VocabularyLecture 602 Neural Machine Translation: EncoderRNNLecture 603 Neural Machine Translation: DecoderRNNLecture 604 Neural Machine Translation: DecoderRNN Forward StepLecture 605 Neural Machine Translation: DecoderRNN Helper FunctionsLecture 606 Neural Machine Translation: Training ModuleLecture 607 Neural Machine Translation: Stochastic Gradient DescentLecture 608 Neural Machine Translation: NMT TrainingLecture 609 Neural Machine Translation: NMT EvaluationSection 6: Advanced Chatbots with Deep Learning & PythonLecture 610 Links for the Course's Materials and CodesLecture 611 Introduction: Course and Instructor IntroductionLecture 612 Introduction: AI Sciences IntroductionLecture 613 Introduction: Course DescriptionLecture 614 Fundamentals of Chatbots for Deep Learning: Module IntroductionLecture 615 Fundamentals of Chatbots for Deep Learning: Conventional vs AI ChatbotsLecture 616 Fundamentals of Chatbots for Deep Learning: Geneative vs Retrievel ChatbotsLecture 617 Fundamentals of Chatbots for Deep Learning: Benifits of Deep Learning ChatbotsLecture 618 Fundamentals of Chatbots for Deep Learning: Chatbots in Medical DomainLecture 619 Fundamentals of Chatbots for Deep Learning: Chatbots in BusinessLecture 620 Fundamentals of Chatbots for Deep Learning: Chatbots in E-CommerceLecture 621 Deep Learning Based Chatbot Architecture and Develpment: Module IntroductionLecture 622 Deep Learning Based Chatbot Architecture and Develpment: Deep Learning ArchitectLecture 623 Deep Learning Based Chatbot Architecture and Develpment: Encoder DecoderLecture 624 Deep Learning Based Chatbot Architecture and Develpment: Steps InvolvedLecture 625 Deep Learning Based Chatbot Architecture and Develpment: Project Overview and PackagesLecture 626 Deep Learning Based Chatbot Architecture and Develpment: Importing LibrariesLecture 627 Deep Learning Based Chatbot Architecture and Develpment: Data PreprationLecture 628 Deep Learning Based Chatbot Architecture and Develpment: Develop VocabularyLecture 629 Deep Learning Based Chatbot Architecture and Develpment: Max Story and Question LengthLecture 630 Deep Learning Based Chatbot Architecture and Develpment: TokenizerLecture 631 Deep Learning Based Chatbot Architecture and Develpment: Separation and SequenceLecture 632 Deep Learning Based Chatbot Architecture and Develpment: Vectorize StoriesLecture 633 Deep Learning Based Chatbot Architecture and Develpment: Vectorizing Train and Test DataLecture 634 Deep Learning Based Chatbot Architecture and Develpment: EncodingLecture 635 Deep Learning Based Chatbot Architecture and Develpment: Answer and ResponseLecture 636 Deep Learning Based Chatbot Architecture and Develpment: Model CompletionLecture 637 Deep Learning Based Chatbot Architecture and Develpment: PredictionsSection 7: Recommender Systems: An Applied Approach using Deep LearningLecture 638 Links for the Course's Materials and CodesLecture 639 Introduction: Course OutlineLecture 640 Deep Learning Foundation for Recommender Systems: Module IntroductionLecture 641 Deep Learning Foundation for Recommender Systems: OverviewLecture 642 Deep Learning Foundation for Recommender Systems: Deep Learning in Recommendation SystemsLecture 643 Deep Learning Foundation for Recommender Systems: Inference After TrainingLecture 644 Deep Learning Foundation for Recommender Systems: Inference MechanismLecture 645 Deep Learning Foundation for Recommender Systems: Embeddings and User ContextLecture 646 Deep Learning Foundation for Recommender Systems: Neutral Collaborative FilterinLecture 647 Deep Learning Foundation for Recommender Systems: VAE Collaborative FilteringLecture 648 Deep Learning Foundation for Recommender Systems: Strengths and Weaknesses of DL ModelsLecture 649 Deep Learning Foundation for Recommender Systems: Deep Learning QuizLecture 650 Deep Learning Foundation for Recommender Systems: Deep Learning Quiz SolutionLecture 651 Project Amazon Product Recommendation System: Module OverviewLecture 652 Project Amazon Product Recommendation System: TensorFlow RecommendersLecture 653 Project Amazon Product Recommendation System: Two Tower ModelLecture 654 Project Amazon Product Recommendation System: Project OverviewLecture 655 Project Amazon Product Recommendation System: Download LibrariesLecture 656 Project Amazon Product Recommendation System: Data Visualization with WordCloudLecture 657 Project Amazon Product Recommendation System: Make Tensors from DataFrameLecture 658 Project Amazon Product Recommendation System: Rating Our DataLecture 659 Project Amazon Product Recommendation System: Random Train-Test SplitLecture 660 Project Amazon Product Recommendation System: Making the Model and Query TowerLecture 661 Project Amazon Product Recommendation System: Candidate Tower and Retrieval SystemLecture 662 Project Amazon Product Recommendation System: Compute LossLecture 663 Project Amazon Product Recommendation System: Train and ValidationLecture 664 Project Amazon Product Recommendation System: Accuracy vs RecommendationsLecture 665 Project Amazon Product Recommendation System: Making RecommendationsAspiring Data Scientists: Individuals aiming to specialize in deep learning and expand their knowledge in AI applications.,Programmers and Developers: Those seeking to venture into the field of artificial intelligence and harness Python for deep learning projects.,AI Enthusiasts and Learners: Anyone passionate about understanding CNNs, RNNs, NLP, chatbots, and recommender systems within the realm of deep learning.,Students and Researchers: Those pursuing academic endeavors or conducting research in machine learning and AI-related fields.,Professionals Exploring Career Shifts: Individuals interested in transitioning or advancing their careers in artificial intelligence and deep learning.,Tech Enthusiasts: Individuals keen on exploring cutting-edge technologies and applications within the AI 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