Deep Learning: Python Deep Learning Masterclass

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

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