Канал: kmit vista

UNIT - 2_NAVIGATING WITH Q-LEARNING - INTRODUCING THE GRIDWORLD GAME ENGINE

UNIT - 2_NAVIGATING WITH Q-LEARNING - INTRODUCING THE GRIDWORLD GAME ENGINE

UINT - 2_NAVIGATING WITH Q-LEARNING - BUILDING THE NETWORK

UINT - 2_NAVIGATING WITH Q-LEARNING - BUILDING THE NETWORK

UNIT - 2_NAVIGATING WITH Q-LEARNING - HYPERPARAMETERS AND DISCOUNT FACTOR

UNIT - 2_NAVIGATING WITH Q-LEARNING - HYPERPARAMETERS AND DISCOUNT FACTOR

UNIT - 2_NAVIGATING WITH Q-LEARNING-Q-LEARNING AND TACKLING GRIDWORLD

UNIT - 2_NAVIGATING WITH Q-LEARNING-Q-LEARNING AND TACKLING GRIDWORLD

UNIT - 2_THE Q FUNCTION

UNIT - 2_THE Q FUNCTION

UNIT - 2_Perform EDA on Given Dataset and Prepare Dataset to Train and Test ML Model

UNIT - 2_Perform EDA on Given Dataset and Prepare Dataset to Train and Test ML Model

UNIT - 2_LAB EXPERIMENT- 3, TOPIC -2_UML DIAGRAMS - USECASE

UNIT - 2_LAB EXPERIMENT- 3, TOPIC -2_UML DIAGRAMS - USECASE

UNIT - 2_ Apply Central Tendency and Variabiliti on Given Dataset

UNIT - 2_ Apply Central Tendency and Variabiliti on Given Dataset

WSMA_UNIT_2_TOPIC-7

WSMA_UNIT_2_TOPIC-7

WSMA-UNIT-2-TOPIC-6

WSMA-UNIT-2-TOPIC-6

WSMA-UNIT-1-TOPIC-5

WSMA-UNIT-1-TOPIC-5

UNIT-2-TOPIC-7-Performance Metrics

UNIT-2-TOPIC-7-Performance Metrics

UNIT - 2_ VISUALIZATION AND ITS TOOLS

UNIT - 2_ VISUALIZATION AND ITS TOOLS

WSMA_UNIT-2_Topic-4

WSMA_UNIT-2_Topic-4

UNIT - 2_DATA PREPARATION AND PREPROCESSING STEPS USING SAMPLE DATESET - PART - 2

UNIT - 2_DATA PREPARATION AND PREPROCESSING STEPS USING SAMPLE DATESET - PART - 2

WSMA_UNIT_2_TOPIC-3

WSMA_UNIT_2_TOPIC-3

WSMA-UNIT_2_TOPIC-2

WSMA-UNIT_2_TOPIC-2

WSMA-UNIT_2-TOPIC-1

WSMA-UNIT_2-TOPIC-1

WSMA-UNIT-1-TOPIC-5

WSMA-UNIT-1-TOPIC-5

UNIT - 2_DATA PREPARATION AND PREPROCESSING STEPS USING SAMPLE DATESET - PART - 1

UNIT - 2_DATA PREPARATION AND PREPROCESSING STEPS USING SAMPLE DATESET - PART - 1

WSMA_UNIT-1_Topic-4

WSMA_UNIT-1_Topic-4

UNIT - 2_OTHER TWO LEVEL IMPLEMENTATION &EXCLUSIVE-OR FUNCTIONS

UNIT - 2_OTHER TWO LEVEL IMPLEMENTATION &EXCLUSIVE-OR FUNCTIONS

UNIT -2_NAND AND NOR IMPLEMENTATION

UNIT -2_NAND AND NOR IMPLEMENTATION

UNIT - 2_SSOP & SPOS BOOLEAN FUNCTIONS SIMPLIFICATION WITH DON'T CARES

UNIT - 2_SSOP & SPOS BOOLEAN FUNCTIONS SIMPLIFICATION WITH DON'T CARES

UNIT-2-TOPIC-3-Basic Components of CNN-1

UNIT-2-TOPIC-3-Basic Components of CNN-1

UNIT - 2_CONCEPTUAL MODEL OF UML

UNIT - 2_CONCEPTUAL MODEL OF UML

UNIT - 2_UNDERSTANDING REQUIREMENTS

UNIT - 2_UNDERSTANDING REQUIREMENTS

UNIT-2-TOPIC-6-Adding Dropout Layers

UNIT-2-TOPIC-6-Adding Dropout Layers

UNIT - 2_Data Preprocessing

UNIT - 2_Data Preprocessing

UNIT - 2_calculating correlation, covariance, causation

UNIT - 2_calculating correlation, covariance, causation

UNIT-2-TOPIC-5-Image Classification with CNN

UNIT-2-TOPIC-5-Image Classification with CNN

UNIT-2-TOPIC-4-Basic Components of CNN-2

UNIT-2-TOPIC-4-Basic Components of CNN-2

UNIT - 2_Statistical Learning

UNIT - 2_Statistical Learning

DLVS-UNIT-2-TOPIC 2-CNN Architecture

DLVS-UNIT-2-TOPIC 2-CNN Architecture

UNIT - 2_SOFTWARE REQUIREMENTS DOCUMENT

UNIT - 2_SOFTWARE REQUIREMENTS DOCUMENT

UNIT - 2_Variability

UNIT - 2_Variability

Generative AI : Project School Embedded Learning (3 - 1 Sem)

Generative AI : Project School Embedded Learning (3 - 1 Sem)

Unit 2- Topic 05- Collective Communication

Unit 2- Topic 05- Collective Communication

Generative AI : Project School Embedded Learning (3 - 1 Sem)

Generative AI : Project School Embedded Learning (3 - 1 Sem)

DLVS-UNIT 2-TOPIC- 1- Image Classification with MLP

DLVS-UNIT 2-TOPIC- 1- Image Classification with MLP

UNIT - 2_CENTRAL TEDENCY - CALCULATING MEAN MODE MEDIAN, MEAN VSMEDIAN USING SAMPLE DATASET

UNIT - 2_CENTRAL TEDENCY - CALCULATING MEAN MODE MEDIAN, MEAN VSMEDIAN USING SAMPLE DATASET

UNIT - 2_INRODUCTION TO DATA REPRESENTATION TYPES OF DATA NORMAL ORDINAL, INTERVAL AND CONTINUOUS

UNIT - 2_INRODUCTION TO DATA REPRESENTATION TYPES OF DATA NORMAL ORDINAL, INTERVAL AND CONTINUOUS

FIVE -VARIABLE SPOS SIMPLIFICATION

FIVE -VARIABLE SPOS SIMPLIFICATION

UNIT - 2_FIVE -VARIABLE SSOP SIMPLIFICATION

UNIT - 2_FIVE -VARIABLE SSOP SIMPLIFICATION

Generative AI : Project School Embedded Learning (3 - 1 Sem)

Generative AI : Project School Embedded Learning (3 - 1 Sem)

UNIT - 2_FOUR -VARIABLE SPOS SIMPLIFICATION

UNIT - 2_FOUR -VARIABLE SPOS SIMPLIFICATION

UNIT - 2_FOUR -VARIABLE SSOP SIMPLIFICATION

UNIT - 2_FOUR -VARIABLE SSOP SIMPLIFICATION

UNIT - 2_TWO AND THREE -VARIABLE KARNAUGH MAP METHOD

UNIT - 2_TWO AND THREE -VARIABLE KARNAUGH MAP METHOD

UNIT - 2_SUM OF PRODUCTS SIMPLIFICATION & PRODUCT OF SUMS SIMPLIFICATION

UNIT - 2_SUM OF PRODUCTS SIMPLIFICATION & PRODUCT OF SUMS SIMPLIFICATION

Imagineering School Season-2 Inaugural Session
Keshav Memorial Group of Institutions

Imagineering School Season-2 Inaugural Session Keshav Memorial Group of Institutions

Unit 2 - Topic 04- Send and Receive Commands

Unit 2 - Topic 04- Send and Receive Commands

Unit 2 -Topic 03- Basics of MPI Programming

Unit 2 -Topic 03- Basics of MPI Programming

Unit 2- Topic 02- Parallel Programming on CPU II

Unit 2- Topic 02- Parallel Programming on CPU II

Unit 2 -Topic 01- Parallel Programming on CPU I

Unit 2 -Topic 01- Parallel Programming on CPU I

HTML - Embedded Learning

HTML - Embedded Learning

Experiment – 2_Implementation of File Read command (Display the contents of a file)

Experiment – 2_Implementation of File Read command (Display the contents of a file)

Experiment – 1_File system commands

Experiment – 1_File system commands

WSMA_UNIT1_TOPIC-3

WSMA_UNIT1_TOPIC-3

WSMA_UNIT-1-TOPIC- 2

WSMA_UNIT-1-TOPIC- 2

Full Stack Development : Project School Embedded Learning (3 - 1 Sem)

Full Stack Development : Project School Embedded Learning (3 - 1 Sem)

UNIT - 2_Predicting Future Rewards : Value and Policy Functions

UNIT - 2_Predicting Future Rewards : Value and Policy Functions

UNIT - 2_The Markov Property

UNIT - 2_The Markov Property

Full Stack Development : Project School Embedded Learning (3 - 1 Sem)

Full Stack Development : Project School Embedded Learning (3 - 1 Sem)

CN_UNIT-1_TOPIC-5

CN_UNIT-1_TOPIC-5

WSMA_UNIT-1_TOPIC-1

WSMA_UNIT-1_TOPIC-1

Generative AI : Project School Embedded Learning (3 - 1 Sem)

Generative AI : Project School Embedded Learning (3 - 1 Sem)

Generative AI : Project School Embedded Learning (3 - 1 Sem )

Generative AI : Project School Embedded Learning (3 - 1 Sem )

UNIT - 1_DEVOPS LIFECYCLE

UNIT - 1_DEVOPS LIFECYCLE

UNIT - 2_THE MAP METHOD

UNIT - 2_THE MAP METHOD

Generative AI : Project School Embedded Learning (3 - 1 Sem)

Generative AI : Project School Embedded Learning (3 - 1 Sem)

UNIT - 2_Types of Keys

UNIT - 2_Types of Keys

UNIT - 1_SOLVING CONTEXTUAL BANDITS

UNIT - 1_SOLVING CONTEXTUAL BANDITS

UNIT - 1_BUILDING NETWORKS WITH PYTORCH

UNIT - 1_BUILDING NETWORKS WITH PYTORCH

UNIT - 1_BUILDING NETWORKS WITH PYTORCH

UNIT - 1_BUILDING NETWORKS WITH PYTORCH

UNIT - 1_APPLYING BANDITS TO OPTIMIZE AD PLACEMENTS

UNIT - 1_APPLYING BANDITS TO OPTIMIZE AD PLACEMENTS

UNIT - 1_SCRUM FRAMEWORK AND INTRODUCTION TO DEVOPS

UNIT - 1_SCRUM FRAMEWORK AND INTRODUCTION TO DEVOPS

DLVS-UNIT-1-TOPIC-12-Backpropagation Algorithm

DLVS-UNIT-1-TOPIC-12-Backpropagation Algorithm

DLVS-UNIT-1-TOPIC-11-Optimization Algorithm-3

DLVS-UNIT-1-TOPIC-11-Optimization Algorithm-3

DLVS UNIT-1-TOPIC-10-Optimization Algorithm-2

DLVS UNIT-1-TOPIC-10-Optimization Algorithm-2

DLVS-UNIT-1-TOPIC-9-Optimization Algorithm -1

DLVS-UNIT-1-TOPIC-9-Optimization Algorithm -1

DLVS-UNIT-1-TOPIC-8-Feed Forward Process

DLVS-UNIT-1-TOPIC-8-Feed Forward Process

DLVS-UNIT-1-TOPIC-7-Activation Function

DLVS-UNIT-1-TOPIC-7-Activation Function

UNIT - 1_Jupyter Notebook, Google colab

UNIT - 1_Jupyter Notebook, Google colab

DLVS-UNIT-1-TOPIC-6-Multilayer Perceptron

DLVS-UNIT-1-TOPIC-6-Multilayer Perceptron

DLVS-UNIT-1-TOPIC-5-Perceptron

DLVS-UNIT-1-TOPIC-5-Perceptron

UNIT - 1_TRAINING VAEs, DECODING IN VAEs, APPLICATIONS OF VAEs

UNIT - 1_TRAINING VAEs, DECODING IN VAEs, APPLICATIONS OF VAEs

UNIT - 1_Comparison of Epsilon-Greedy and Softmax Selection Policy for Solving the Multi-Armed

UNIT - 1_Comparison of Epsilon-Greedy and Softmax Selection Policy for Solving the Multi-Armed

UNIT - 1_TRAINING VAEs, DECODING IN VAEs, APPLICATIONS OF VAEs

UNIT - 1_TRAINING VAEs, DECODING IN VAEs, APPLICATIONS OF VAEs

UNIT - 1_INTRODUCTION TO AGILE

UNIT - 1_INTRODUCTION TO AGILE

UNIT -1_THEORY AND LAB TOPIC - SOLVING THE MULTI-ARMED BANDIT PROBLEM-USING SOFTMAX SELECTION POLICY

UNIT -1_THEORY AND LAB TOPIC - SOLVING THE MULTI-ARMED BANDIT PROBLEM-USING SOFTMAX SELECTION POLICY

UNIT - 1_SOLVING THE MULTI-ARMED BANDIT PROBLEM- USING EPSILON-GREEDY STRATEGY

UNIT - 1_SOLVING THE MULTI-ARMED BANDIT PROBLEM- USING EPSILON-GREEDY STRATEGY

UNIT - 1_TRAINING GANs, INTRODUCTION TO VAEs

UNIT - 1_TRAINING GANs, INTRODUCTION TO VAEs

UNIT - 1_Type of Generative AI models, Key components of GAN

UNIT - 1_Type of Generative AI models, Key components of GAN

DLVS-UNIT-1-TOPIC -4-Classification Model and Classifier Learning Algorithm

DLVS-UNIT-1-TOPIC -4-Classification Model and Classifier Learning Algorithm

DLVS-UNIT-1-TOPIC-3- CV Pipeline Process- 2

DLVS-UNIT-1-TOPIC-3- CV Pipeline Process- 2

UNIT - 1_PROCESS MODELS – EVOLUTIONARY PROCESS MODELS

UNIT - 1_PROCESS MODELS – EVOLUTIONARY PROCESS MODELS

UNIT - 1_PROCESS MODELS – SDLC, WATERFALL MODEL, INCREMENTAL PROCESS MODEL

UNIT - 1_PROCESS MODELS – SDLC, WATERFALL MODEL, INCREMENTAL PROCESS MODEL

DLVS-UNIT-1-TOPIC-2-Computer Vision Pipeline Process-1

DLVS-UNIT-1-TOPIC-2-Computer Vision Pipeline Process-1

DLVS-UNIT-1-TOPIC-1 Computer Vision and its Applications

DLVS-UNIT-1-TOPIC-1 Computer Vision and its Applications

UNIT - 1_SOLVING THE MULTI-ARMED BANDIT _BALANCING EXPLORATION & EXPLOITATION

UNIT - 1_SOLVING THE MULTI-ARMED BANDIT _BALANCING EXPLORATION & EXPLOITATION

UNIT - 1_TRAINING OF LLMs – Fine -Tuning,Applications of LLMs

UNIT - 1_TRAINING OF LLMs – Fine -Tuning,Applications of LLMs

UNIT - 1_TRAINING OF LLMs – Pre -Training

UNIT - 1_TRAINING OF LLMs – Pre -Training

UNIT - 1_Key Features of LLMs

UNIT - 1_Key Features of LLMs

UNIT - 1_CANONICAL AND STANDARD FORMS

UNIT - 1_CANONICAL AND STANDARD FORMS

UNIT - 1_MINIMIZATION OF BOOLEAN FUNCTIONS

UNIT - 1_MINIMIZATION OF BOOLEAN FUNCTIONS

UNIT - 1_LOGIC GATES

UNIT - 1_LOGIC GATES

UNIT - 1_OTHER LOGIC OPERATIONS

UNIT - 1_OTHER LOGIC OPERATIONS

PP- Unit 1- Parallel Algorithms and Patterns

PP- Unit 1- Parallel Algorithms and Patterns

PP - Unit 1 -Characterizing Your Application

PP - Unit 1 -Characterizing Your Application

PP - Unit 1 - Performance Limits and Profiling

PP - Unit 1 - Performance Limits and Profiling

UNIT - 1_BOOLEAN ALGEBRA

UNIT - 1_BOOLEAN ALGEBRA

UNIT - 1_String Diagrams

UNIT - 1_String Diagrams

UNIT - 1_Applications of Reinforcement Learning

UNIT - 1_Applications of Reinforcement Learning

PP - Unit 1- Parallel Strategies

PP - Unit 1- Parallel Strategies

PP - Unit 1 - Fundamental Laws & Parallel Approaches

PP - Unit 1 - Fundamental Laws & Parallel Approaches

PP - UNIT 1 - Basics of Parallel Programming

PP - UNIT 1 - Basics of Parallel Programming

UNIT - 1_Reinforcement Learning Framework

UNIT - 1_Reinforcement Learning Framework

UNIT - 1_GRAY CODE AND ERROR DETECTING CODES

UNIT - 1_GRAY CODE AND ERROR DETECTING CODES

UNIT - 1_LAB EXERCISE -1, Topic - 2

UNIT - 1_LAB EXERCISE -1, Topic - 2

UNIT - 1_LAB EXERCISE - 1, Topic -1

UNIT - 1_LAB EXERCISE - 1, Topic -1

UNIT - 1_Introduction to Generative AI

UNIT - 1_Introduction to Generative AI

UNIT - 1_Essential libraries and Tools

UNIT - 1_Essential libraries and Tools

UNIT - 1_Machine learning Applications and challenges

UNIT - 1_Machine learning Applications and challenges

UNIT -1 _Types of Machine Learning Systems

UNIT -1 _Types of Machine Learning Systems

DEEP LEARNING - NEURAL NETWORKS - ANN, INTRODUCTION

DEEP LEARNING - NEURAL NETWORKS - ANN, INTRODUCTION

UNIT - 1_OpenAI Gym PYTHON LIBRARY

UNIT - 1_OpenAI Gym PYTHON LIBRARY

UNIT - 1_THE NATURE OF SOFTWARE,SOFTWARE APPLICATION DOMAINS – Part - 2

UNIT - 1_THE NATURE OF SOFTWARE,SOFTWARE APPLICATION DOMAINS – Part - 2

UNIT - 1_Machine learning - Deep learning

UNIT - 1_Machine learning - Deep learning

UNIT - 1_SOFTWARE ENGINEERING – A LAYERED TECHNOLOGY, PROCESS FRAMEWORK, GENERIC PROCESS MODEL

UNIT - 1_SOFTWARE ENGINEERING – A LAYERED TECHNOLOGY, PROCESS FRAMEWORK, GENERIC PROCESS MODEL

THE NATURE OF SOFTWARE,SOFTWARE APPLICATION DOMAINS

THE NATURE OF SOFTWARE,SOFTWARE APPLICATION DOMAINS

UNIT - 1_Introduction to Security, The need of Security

UNIT - 1_Introduction to Security, The need of Security

UNIT - 1_Security Approaches, Principles of Security

UNIT - 1_Security Approaches, Principles of Security

UNIT - 1_Importance of search for AI, AI Applications

UNIT - 1_Importance of search for AI, AI Applications

UNIT - 1_Searching for Solutions

UNIT - 1_Searching for Solutions

UNIT - 1_KEY CONCEPTS- PROBLEM SOLVING, REASONING,LEARNING

UNIT - 1_KEY CONCEPTS- PROBLEM SOLVING, REASONING,LEARNING

UNIT -1_AI TECHNIQUES AND APPROACHES, RULE-BASED SYSTEMS

UNIT -1_AI TECHNIQUES AND APPROACHES, RULE-BASED SYSTEMS

UNIT - 1_ERROR DETECTION AND CORRECTION CODES

UNIT - 1_ERROR DETECTION AND CORRECTION CODES

UNIT - 1_Bcd codes

UNIT - 1_Bcd codes

UNIT - 1_Subtraction using complements

UNIT - 1_Subtraction using complements

UNIT - 1_Complements

UNIT - 1_Complements

UNIT -1 _ LEGACY SOFTWARE AND SOFTWARE MYTHS

UNIT -1 _ LEGACY SOFTWARE AND SOFTWARE MYTHS

UNIT - 1_INTRODUCTION

UNIT - 1_INTRODUCTION

UNIT - 1_Dynamic Programming Vs Monte Carlo

UNIT - 1_Dynamic Programming Vs Monte Carlo

UNIT - 1_DEEP REINFORCEMENT LEARNING

UNIT - 1_DEEP REINFORCEMENT LEARNING

UNIT - 1_INTRODUCTION

UNIT - 1_INTRODUCTION

UNIT - 1_NUMBER BASE CONVERSION

UNIT - 1_NUMBER BASE CONVERSION

UNIT - 1_BINARY ARITHMETIC

UNIT - 1_BINARY ARITHMETIC

UNIT - 1_ANY NUMBER BASE TO DECIMAL AND OTHER BASE CONVERSION

UNIT - 1_ANY NUMBER BASE TO DECIMAL AND OTHER BASE CONVERSION

UNIT - 1_BINARY, OCTAL AND HEXADECIMAL NUMBER SYSTEMS AND NUMBER BASE CONVERSION

UNIT - 1_BINARY, OCTAL AND HEXADECIMAL NUMBER SYSTEMS AND NUMBER BASE CONVERSION

UNIT - 1_Introduction - History and evolution of AI

UNIT - 1_Introduction - History and evolution of AI

UNIT - 1_INTRODUCTION TO DIGITAL SYSTEM AND BINARY NUMBERS

UNIT - 1_INTRODUCTION TO DIGITAL SYSTEM AND BINARY NUMBERS

BURP SUITE INTRODUCTION

BURP SUITE INTRODUCTION

JUICE SHOP SETUP

JUICE SHOP SETUP

SQL INJECTION

SQL INJECTION

WEB APPLICATION SECURITY & THE OWASP STANDARD

WEB APPLICATION SECURITY & THE OWASP STANDARD

CYBERSECURITY -XSS ATTACK & PREVENTION

CYBERSECURITY -XSS ATTACK & PREVENTION

CYBER SECURITY INTRODUCTION

CYBER SECURITY INTRODUCTION

UNIT - 5_Data Base Administrator

UNIT - 5_Data Base Administrator

UNIT - 5_Data Control Language

UNIT - 5_Data Control Language

UNIT - 5_Stored Procedures

UNIT - 5_Stored Procedures

UNIT - 5_Triggers

UNIT - 5_Triggers

UNIT - 5_Hbase – Building Query System, RDBMS Vs Hbase, Praxis

UNIT - 5_Hbase – Building Query System, RDBMS Vs Hbase, Praxis

UNIT - 5_Hbase – Installation Create, Load, Clients

UNIT - 5_Hbase – Installation Create, Load, Clients

UNIT - 5_Hbase – Introduction, History Shell, Options

UNIT - 5_Hbase – Introduction, History Shell, Options

UNIT - 5_Generating captions-improving performance and state-of-the-art model

UNIT - 5_Generating captions-improving performance and state-of-the-art model

UNIT - 5_The Transformer model - Training the transformer model with visual encoder

UNIT - 5_The Transformer model - Training the transformer model with visual encoder

UNIT - 5_Image processing with CNN and RestNet - Image feature extraction with RestNet

UNIT - 5_Image processing with CNN and RestNet - Image feature extraction with RestNet

UNIT - 5_Multi-model deeplearning, Image captioning with MCOCO database

UNIT - 5_Multi-model deeplearning, Image captioning with MCOCO database

Lab Program 1

Lab Program 1

CN_UNIT_5_TOPIC-4-Web Sockets

CN_UNIT_5_TOPIC-4-Web Sockets

UNIT - 5_Lab Program-7 MangoDB & Express

UNIT - 5_Lab Program-7 MangoDB & Express

UNIT - 5_Integration With Express

UNIT - 5_Integration With Express

CN_UNIT_5-TOPIC-3-IPV6,TCP SOCKET OPTIONS

CN_UNIT_5-TOPIC-3-IPV6,TCP SOCKET OPTIONS

UNIT-5_Case study-Serving & deploying NLP applications-Interpreting & visualizing model predictions

UNIT-5_Case study-Serving & deploying NLP applications-Interpreting & visualizing model predictions

UNIT - 5_Architecting and Deploying NLP models

UNIT - 5_Architecting and Deploying NLP models

CN_UNIT 5_TOPIC 2

CN_UNIT 5_TOPIC 2

UNIT - 5_Hyperparameter Tuning

UNIT - 5_Hyperparameter Tuning

UNIT - 5_VAE

UNIT - 5_VAE

UNIT5_AUTOENCODER

UNIT5_AUTOENCODER

UNIT 5_LSTM & GRU

UNIT 5_LSTM & GRU

UNIT5_SEQUENCE REPRESENTATION METHOD

UNIT5_SEQUENCE REPRESENTATION METHOD

UNIT5_INTRODUCTION TO RNN

UNIT5_INTRODUCTION TO RNN

UNIT - 5_Dealing with Imbalanced datasets

UNIT - 5_Dealing with Imbalanced datasets

UNIT - 5_Spark Shared Variables, Anatomy of Spark Job Running

UNIT - 5_Spark Shared Variables, Anatomy of Spark Job Running

UNIT - 5_Avoiding Overfitting

UNIT - 5_Avoiding Overfitting

UNIT - 5_Best practices in developing NLP applications Tokenization for neural models

UNIT - 5_Best practices in developing NLP applications Tokenization for neural models

UNIT - 5_Interacting with an application

UNIT - 5_Interacting with an application

UNIT - 5_Best practices in Developing NLP Applications Batching Instances

UNIT - 5_Best practices in Developing NLP Applications Batching Instances

UNIT - 5_Lab Program - 6 Creating Student Database in MongoDB

UNIT - 5_Lab Program - 6 Creating Student Database in MongoDB

UNIT - 5_Aggregation Pipeline in MangoDB part-3

UNIT - 5_Aggregation Pipeline in MangoDB part-3

UNIT - 5_Aggregation PipeLine in MangoDB Part-2

UNIT - 5_Aggregation PipeLine in MangoDB Part-2

UNIT - 5_Aggregation Pipeline in MangoDB - Part -1

UNIT - 5_Aggregation Pipeline in MangoDB - Part -1

UNIT - 5 _CRUD Operations Update & Delete

UNIT - 5 _CRUD Operations Update & Delete

UNIT - 5_Http Requests - Axios Package Fetching Data

UNIT - 5_Http Requests - Axios Package Fetching Data

CN_UNIT_5_TOPIC -1-Introduction to Sockets

CN_UNIT_5_TOPIC -1-Introduction to Sockets

OS Unit 5 Topic 4 - Access Matrix

OS Unit 5 Topic 4 - Access Matrix

OS Unit 5 Topic 3 - Protection

OS Unit 5 Topic 3 - Protection

OS Unit 5 Topic 2 - Disk Scheduling

OS Unit 5 Topic 2 - Disk Scheduling

OS Unit 5- Topic 1- Mass Storage

OS Unit 5- Topic 1- Mass Storage

UNIT - 5_CRUD Operation in MongoDB

UNIT - 5_CRUD Operation in MongoDB