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Deep Learning: Python Deep Learning Masterclass - Panter - 03.01.2024 Deep Learning: Python Deep Learning Masterclass 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 What you'll learn 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. Requirements Understanding Python fundamentals is recommended for implementing deep learning concepts covered in the course. Description 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: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 Curriculum Overview Section 1: Introduction Lecture 1 Links for the Course's Materials and Codes Section 2: Deep Learning:Deep Neural Network for Beginners Using Python Lecture 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 Aspiring 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 domain. |