A quick look at CBOW and how it looks like it in code. I explained how this model works in the previous example so check that out before you look at this one to get a better understanding.

Timestamps:
0:00 - Introduction
0:48 - Imports
0:53 - CBOW model
5:10 - Example of how to train it
8:52 - Ending

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A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model that is “just right” will avoid these important problems.

Deep Learning TV on
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Suppose you are trying to classify big cats based on features such as claw size, sex, body dimensions, bite strength, color, speed, and the presence of a mane. Due to various deficiencies in the training process and data set, the resultant model may fail to fully differentiate the various types of cats. For example, a rule-based model may predict that any cat with a mane that roars is a lion, while ignoring that if such a cat is female, it is technically referred to as a lioness. Since this model does not take all necessary features into account while performing classification, it is said to underfit the data. The problem of underfitting is solved by adding more detail to the model to ensure that it properly captures the differences between classes.

On the other hand, a model may also consider every possible detail and develop very specific, complex rules for classification. For example, if one data point represents a lion that is 3.5 feet tall, weighs 305 pounds, and has 2.9-inch claws, the model may develop a rule that classifies every 3.5 foot tall, 305 pound cat with 2.9-inch claws as a lion. Such rules will accurately classify the training data, but will poorly generalize to new data samples. A model that develops these kinds of rules is said to overfit the data. In other words, the model has failed to identify the true patterns that differentiate the classes. As a separate example, if the data only contained tigers that grew up in a zoo, the model may have difficulty classifying tigers that grew up in the wild. So while improving the process of data collection is helpful to prevent this problem, the model must be designed to identify the most important patterns that identify a class, so that new samples can be properly classified.

With regards to neural networks, overfitting typically stems from too many input features, or the use of an overly-complicated network configuration. If the input count is too large, the training process may start to assign weights to features that either aren't needed or add unnecessary complexity to the model. An overly-complicated configuration may lead to the development of specific rules that improperly relate many different features, resulting in poor generalization.

Overfitting is a common problem in data science. One popular method to reduce overfitting is the use of a cross-validation data set along with parameter averaging. For neural networks, a common method is regularization. There are different types such as L1 and L2, but each of these follows the same principle – penalize the model for letting weights and biases become too large. Another method is Max Norm constraints, which directly adds a size limit to the weights and biases. A different approach is dropout, which randomly switches off certain neurons in the network, preventing the model from becoming too dependent on a set of neurons and the associated weights and biases. While these methods are broadly applied across the model rather than used for systematically searching for problem patterns, they have been proven to reduce and sometimes prevent the problem of overfitting.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Reviewing Lambda and Razer's Tensorbook, a laptop aimed at deep learning, with 16GB of VRAM (GPU memory), 64GB of RAM, 2TB of NVMe storage and an 8-core intel i7 11800H CPU.
https://lambdalabs.com/deep-le....arning/laptops/tenso

Neural Networks from Scratch book: https://nnfs.io
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"Symmetry, as wide or narrow as you may define its meaning, is one idea by which man through the ages has tried to comprehend and create order, beauty, and perfection." and that was a quote from Hermann Weyl, a German mathematician who was born in the late 19th century.

The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Many high-dimensional learning tasks previously thought to be beyond reach -- such as computer vision, playing Go, or protein folding -- are in fact tractable given enough computational horsepower. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature learning and second, learning by local gradient-descent type methods, typically implemented as backpropagation.

While learning generic functions in high dimensions is a cursed estimation problem, many tasks are not uniform and have strong repeating patterns as a result of the low-dimensionality and structure of the physical world.

Geometric Deep Learning unifies a broad class of ML problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases.

This week we spoke with Professor Michael Bronstein (head of graph ML at Twitter) and Dr.
Petar Veličković (Senior Research Scientist at DeepMind), and Dr. Taco Cohen and Prof. Joan Bruna about their new proto-book Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

Enjoy the show!

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
https://arxiv.org/abs/2104.13478

[00:00:00] Tim Intro
[00:01:55] Fabian Fuchs article
[00:04:05] High dimensional learning and curse
[00:05:33] Inductive priors
[00:07:55] The proto book
[00:09:37] The domains of geometric deep learning
[00:10:03] Symmetries
[00:12:03] The blueprint
[00:13:30] NNs don't deal with network structure (TedX)
[00:14:26] Penrose - standing edition
[00:15:29] Past decade revolution (ICLR)
[00:16:34] Talking about the blueprint
[00:17:11] Interpolated nature of DL / intelligence
[00:21:29] Going tack to Euclid
[00:22:42] Erlangen program
[00:24:56] “How is geometric deep learning going to have an impact”
[00:26:36] Introduce Michael and Petar
[00:28:35] Petar Intro
[00:32:52] Algorithmic reasoning
[00:36:16] Thinking fast and slow (Petar)
[00:38:12] Taco Intro
[00:46:52] Deep learning is the craze now (Petar)
[00:48:38] On convolutions (Taco)
[00:53:17] Joan Bruna's voyage into geometric deep learning
[00:56:51] What is your most passionately held belief about machine learning? (Bronstein)
[00:57:57] Is the function approximation theorem still useful? (Bruna)
[01:11:52] Could an NN learn a sorting algorithm efficiently (Bruna)
[01:17:08] Curse of dimensionality / manifold hypothesis (Bronstein)
[01:25:17] Will we ever understand approximation of deep neural networks (Bruna)
[01:29:01] Can NNs extrapolate outside of the training data? (Bruna)
[01:31:21] What areas of math are needed for geometric deep learning? (Bruna)
[01:32:18] Graphs are really useful for representing most natural data (Petar)
[01:35:09] What was your biggest aha moment early (Bronstein)
[01:39:04] What gets you most excited? (Bronstein)
[01:39:46] Main show kick off + Conservation laws
[01:49:10] Graphs are king
[01:52:44] Vector spaces vs discrete
[02:00:08] Does language have a geometry? Which domains can geometry not be applied? +Category theory
[02:04:21] Abstract categories in language from graph learning
[02:07:10] Reasoning and extrapolation in knowledge graphs
[02:15:36] Transformers are graph neural networks?
[02:21:31] Tim never liked positional embeddings
[02:24:13] Is the case for invariance overblown? Could they actually be harmful?
[02:31:24] Why is geometry a good prior?
[02:34:28] Augmentations vs architecture and on learning approximate invariance
[02:37:04] Data augmentation vs symmetries (Taco)
[02:40:37] Could symmetries be harmful (Taco)
[02:47:43] Discovering group structure (from Yannic)
[02:49:36] Are fractals a good analogy for physical reality?
[02:52:50] Is physical reality high dimensional or not?
[02:54:30] Heuristics which deal with permutation blowups in GNNs
[02:59:46] Practical blueprint of building a geometric network architecture
[03:01:50] Symmetry discovering procedures
[03:04:05] How could real world data scientists benefit from geometric DL?
[03:07:17] Most important problem to solve in message passing in GNNs
[03:09:09] Better RL sample efficiency as a result of geometric DL (XLVIN paper)
[03:14:02] Geometric DL helping latent graph learning
[03:17:07] On intelligence
[03:23:52] Convolutions on irregular objects (Taco)

Machine learning using neural networks is a very powerful methodology which has demonstrated utility in many different situations. In this talk I will show how work in the mathematical discipline called topological data analysis can be used to (1) lessen the amount of data needed in order to be able to learn and (2) make the computations more transparent. We will work primarily with image and video data.

This talk was part of the workshop on "Topological Data Analysis - Theory and Applications" supported by the Tutte Institute and Western University: https://math.sci.uwo.ca/~jardine/TDA-2021.html

In this project I built a neural network and trained it to play Snake using a genetic algorithm.

Thanks for watching! Subscribe if you enjoyed and Share if you know anyone who would be interested!

GitHub Repo: https://github.com/greerviau/SnakeAI
Twitter: https://twitter.com/greerviau
Support me on Patreon: https://www.patreon.com/greerviau

Thanks to Josh Cominelli for the music!
Soundcloud: https://soundcloud.com/josh-cominelli

Learn how to get started with Hugging Face and the Transformers Library in 15 minutes! Learn all about Pipelines, Models, Tokenizers, PyTorch & TensorFlow integration, and more!

Get your Free Token for AssemblyAI Speech-To-Text API 👇https://www.assemblyai.com/?utm_source=youtube&utm_medium=referral&utm_campaign=yt_pat_26

Hugging Face Tutorial
Hugging Face Crash Course
Sentiment Analysis, Text Generation, Text Classification

Resources:
Website: https://huggingface.co
Course: https://huggingface.co/course
Finetune: https://huggingface.co/docs/transformers/training

▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬

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▶️ Subscribe: https://www.youtube.com/c/Asse....mblyAI?sub_confirmat
🔥 We're hiring! Check our open roles: https://www.assemblyai.com/careers

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Timestamps:
00:00 Intro
00:40 Installation
01:02 Pipeline
04:37 Tokenizer & Model
08:32 PyTorch / TensorFlow
11:07 Save / Load
11:35 Model Hub
13:25 Finetune

HuggingFace Tutorial
HuggingFace Crash Course

#MachineLearning #DeepLearning #HuggingFace

In this Python tutorial, We'll see how to create an AI Text Generation Solution with GPT-Neo from Eleuther AI.
We'll learn
1. About GPT-Neo
2. How to install the latest Hugging Face Transformers Package
3. Load Text Generation pipeline and Download Pre-trained GPT-Neo Models
4. Text Generation

Eleuther AI - https://www.eleuther.ai/
GPT-Neo on Hugging Face Model Hub - https://huggingface.co/EleutherAI/gpt-neo-1.3B
GPT-Neo - https://github.com/EleutherAI/gpt-neo
GPU Focused GPT-NeoX - https://github.com/EleutherAI/gpt-neox/
Colab Code - https://colab.research.google.....com/drive/1UByjdT5l_

All in one place: the best AI course you’ve ever watched!

You can now enjoy all 6.5 hours of Google’s legendary AI course designed to enlighten AI beginners, grow technology leaders, inform better citizens, and amuse AI experts!

This video is the feast version. If you prefer to learn in bite-sized nibbles, you can find the individual chapters as short videos on the http://bit.ly/mf-ml playlist. The episode guide is in the top right hand corner of every video.

Don't forget to hit subscribe+notify! If you found this video useful or enjoyable, the best way to say thank you is by sharing it.

Looking for hands-on ML/AI tutorials? Here are some of my favorite 10 minute walkthroughs:

AutoML - https://console.cloud.google.c....om/?walkthrough_id=a
Vertex AI - https://bit.ly/kozvertex
AI notebooks - https://bit.ly/kozvertexnotebooks
ML for tabular data - https://bit.ly/kozvertextables
Text classification - https://bit.ly/kozvertextext
Image classification - https://bit.ly/kozverteximage
Video classification - https://bit.ly/kozvertexvideo

This Edureka "ServiceNow Tutorial For Beginners" video will help you to get started with ServiceNow. This video mostly focuses on points like Cloud and Cloud Services, ServiceNow Applications and Processes.

Subscribe to our channel to get video updates. Hit the subscribe button above.


#ServiceNowTutorial #ServiceNowDemo #ServiceNow #ITSM #ITOperationsManagement #WhatIsServiceNow #ServiceNowCertification #ServiceNowCertifiedSystemAdministrator
#ServiceNowPlatform


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How it Works?

1. This is a 3 Week Instructor led Online Course, which would include assignments & sufficient hands-on.

2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.

3. At the end of the training you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate!
--------------------------------------------------------------------
About the Course

Edureka's ‘ServiceNow System Administration’ Training is designed for IT professionals and System Administrators who are new to the ServiceNow ecosystem. In this training, you will learn to implement various system administration functions, configure and perform fundamental administration tasks. You will:
· Perform core configuration tasks and understand User Interface (UI) policies, data policies, UI actions, business rules and client scripts

· Understanding basics of ServiceNow table structuring, its relationships and administration

· Learn administration of users and basics of application security

· Learn important concepts of Configuration management database (CMBD) in ServiceNow

· Import sets and update sets

· Create workflow activities and approvals

· Understand knowledge base and ServiceNow service catalog

· Configure alerts and notifications

· Generate reports

· Configure SLAs

· Learn how to customize and perform branding of instance

· Using the social features in ServiceNow
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Who should go for this course?

This ServiceNow Admin training is designed for the IT professionals who want to pursue a career in Cloud Computing and become ServiceNow Administrator. This ServiceNow course is a best fit for:
· Professionals who are working or want to work in Cloud Computing platform

· Functional consultants who are looking to switch to ServiceNow

· Freshers who want to start their career in Cloud computing

· Developers who have experience in C#, Java, JavaScript

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Why learn ServiceNow?


ServiceNow System Administration Certification Training will make you an expert in the concepts related to Administration of ServiceNow. This course will help you to pass ServiceNow System Administration Certification Exam, and acts as a pre-requisite for Advanced ServiceNow Certifications.
------------------------------------------------------------------------

For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).


Website: https://www.edureka.co/service....now-admin-certificat
Facebook: https://www.facebook.com/edurekaIN/
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LinkedIn: https://www.linkedin.com/company/edureka

This Edureka video on 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐂𝐫𝐚𝐬𝐡 𝐂𝐨𝐮𝐫𝐬𝐞 will explain what ChatGPT is all about. In this ChatGPT tutorial, you will learn about how ChatGPT works and the language model that ChatGPT uses.

ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and has been fine-tuned using both supervised and reinforcement learning techniques.

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐂𝐨𝐮𝐫𝐬𝐞 - 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 𝐭𝐨 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝: https://www.edureka.co/openai-....chatgpt-training-cou

⏩ Edureka ChatGPT Explained Playlist: http://bit.ly/3HGRy3G

#ChatGPTCrashCourse #edureka #chatgptexplained #chatgpt #openai #chatgpttutorial #chatgpt3 #ai #nlp #artificialintelligence

Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

🔵 DevOps Online Training: http://bit.ly/3VkBRUT
🌕 AWS Online Training: http://bit.ly/3ADYwDY
🔵 React Online Training: http://bit.ly/3Vc4yDw
🌕 Tableau Online Training: http://bit.ly/3guTe6J
🔵 Power BI Online Training: http://bit.ly/3VntjMY
🌕 Selenium Online Training: http://bit.ly/3EVDtis
🔵 PMP Online Training: http://bit.ly/3XugO44
🌕 Salesforce Online Training: http://bit.ly/3OsAXDH
🔵 Data Science Online Training: http://bit.ly/3V3nLrc
🌕 CEHv12 Online Training: http://bit.ly/3Vhq8Hj

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬

🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC
🌕 Cloud Architect Masters Program: http://bit.ly/3OvueZy
🔵 Data Scientist Masters Program: http://bit.ly/3tUAOiT
🌕 Big Data Architect Masters Program: http://bit.ly/3tTWT0V
🔵 Business Intelligence Masters Program: http://bit.ly/3UZPqJz

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬

🔵 Artificial and Machine Learning PGD with E&ICT Academy
NIT Warangal: http://bit.ly/3OuZ3xs

🌕 Professional Certificate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh

📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 2023 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

🔵 About Edureka ChatGPT Certification Training Course

Edureka’s ChatGPT Certification Training Course will teach you about ChatGPT architecture, GPT models, methodology, and real-world applications. The ChatGPT certification training program is an excellent choice for individuals and organizations seeking to improve their language processing and AI skills and knowledge.

🔵 Why take up the Online ChatGPT Certification Course?

Interested individuals can opt for the Online ChatGPT Certification course as this course provides:

Comprehensive knowledge: The course provides a comprehensive understanding of ChatGPT, including its architecture, training methodology, and real-world applications.

Career advancement: The certification demonstrates an individual's expertise and competence in working with ChatGPT, making them a valuable asset to any organization.

Stay ahead of the curve: ChatGPT is a rapidly growing field, and with the certification, one can stay ahead of the curve and stay updated with the latest advancements in the industry.

Enhance skills: The course provides practical skills and knowledge that can be applied to various areas and industries.

🔵 Who should take up this ChatGPT Certification Course?

The ChatGPT Certification Course is suitable for a wide range of individuals, including:

AI professionals: This course is ideal for AI professionals who want to gain expertise in ChatGPT and expand their knowledge in the field of AI.

Developers: Developers who want to integrate ChatGPT into their projects and create new AI applications can benefit from the course.

Data scientists: Data scientists who want to understand how ChatGPT can be used to process and analyze large amounts of data can also benefit from the course.

📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka
📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/
📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/
📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚'𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐚𝐧𝐝 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 𝐜𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐧𝐝 𝐜𝐨𝐮𝐫𝐬𝐞𝐬 (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎) : https://www.edureka.co/program....ming-and-frameworks-
This Edureka video on Programming For Beginners will help you to Learn Programming basics with examples. This Programming for beginner video covers all the topics for all the three categories of learners - beginner, intermediate and experienced professionals.

Below are the programming languages covered in this Programming Full course video:

00:00:00 Introduction
00:01:30 Agenda
00:02:44 Pyhton Basics
00:04:27 Why use python?
00:25:09 Comparision Operators
00:43:16 range
00:48:26 Counter
00:55:20 Defaultdict
00:58:47 When to use Sets in python
01:01:23 Python set operations
01:12:35 What is an Array ?
01:14:19 How to create arrays in python
01:31:34 Slicing an array
01:34:09 Looping through an Array
01:37:36 Classes and objects in python
01:42:47 Python Classes
02:04:20 Java
02:10:00 Variables in /java
02:31:56 Operator in java
02:46:36 Loops in Java
02:59:32 Object Oriented Programming (OOPS)
03:03:51 OOPS Concept
03:27:13 OOPS Characteristics
03:34:54 Exception in java
03:46:07Exception Handling Methods
03:54:44 History of C
03:55:36 Features of C Language
04:00:46 Keywords
04:03:22 Strings
04:05:05 Identifiers
04:06:49 Rules for Identifier
04:09:46 Operators
04:14:34 Datatypes
04:16:18 Local Variable
04:21:28 PreProcessor Directives
04:27:31 Control Statement
04:45:37 Loops
04:55:58 Pointers
05:00:17 Functions
05:04:36 Rules for using Functions
05:06:19 How to use Functions
05:08:03 Data Structure
05:10:38 One-Dimensional Array
05:14:05 Lists
05:27:53 I+Undirected Graphs
05:31:20 Files
05:35:39 Strings
05:39:58 Structure
05:41:41 Union
05:43:25 Structure v/s Union
05:44:16 Memory allocations
05:48:35 Sorting algorithm
05:53:45 Insertion Algorithm
05:58:56 SelectionAlgorithm
06:01:31 Selection Algorithm
06:04:06 Features of C++ Language
06:04:58 Installation of C++ in windows
06:07:33 Structure of C++ Program
06:10:09 Identifers
06:11:52 Keywords
06:15:19 Arrays
06:22:13 Lists
06:36:01 Functions
06:42:55 Structure
06:43:47 Enumeration
06:47:14 Variable types
06:52:24 Modifiers
06:53:16 Type of Qualifiers
06:54:00 Types of operators
06:56:00 Control Statement
07:06:12 Loops
07:10:31 Features of OOPS
07:12:15 Encapsulation
07:20:00 Interface
07:21:44 Constructor
07:22:36 Destructor
07:28:38 Deallocation
07:30:21 Multithreading
07:33:48 Interview Questions
09:29:23 What is Javascript
09:33:42 Benefits of Javascript
10:18:31 Loops in Javascript
10:39:14 Javascript Array
10:41:50 Javascript Array Methods
11:03:23 Why Learn coding



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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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🔥 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐏𝐀 𝐮𝐬𝐢𝐧𝐠 𝐔𝐢𝐏𝐚𝐭𝐡 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠:https://www.edureka.co/robotic....-process-automation- (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This Edureka session on "Who is a RPA Developer?" will provide a glimpse of the "RPA Developer" and what RPA Developer Roadmap.

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🌕 Professional Certificate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh

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Welcome to this comprehensive SQL tutorial course. This course covers the basics of relational databases and SQL, including setting up MySQL, inserting data, and working with aggregation, grouping, and pagination in SQL queries. It also covers advanced topics such as combining tables using joins, executing SQL queries using Python and SQL Alchemy, and solving technical interview questions. By the end of this course, you'll have the knowledge and confidence to excel in SQL.

✏️ Course created by@jovianhq

Important Links
🔗 Relational Databases notebook - https://jovian.com/aakashns/re....lational-databases-a
🔗 Advanced SQL Aggregation & Joins notebook - https://jovian.com/aakashns/ad....vanced-sql-aggregati
🔗 SQL data file: https://raw.githubusercontent.....com/harsha547/Classi

A database is an organized collection of structured information, typically stored in the form of tables (rows & columns). Relational databases allow storing and retrieving different kinds of related information e.g. products, customers, and orders for an online shopping site. Structured Query Language or SQL (pronounced "sequel") is the most widely used language for interacting with relational databases, and is an essential skill for Data Science professionals.

⭐️ Contents ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:01:28) Relational Databases & SQL
⌨️ (0:03:05) Setting up MySQL
⌨️ (0:29:23) Inserting Data into the Table
⌨️ (0:56:32) Practice Exercises
⌨️ (1:22:12) Aggregation, grouping & pagination in SQL queries
⌨️ (2:23:17) Mapping and Arithmetic Functions
⌨️ (2:54:53) Working with Dates
⌨️ (3:12:16) Combining Tables using Joins
⌨️ (3:30:14) Executing SQL queries using Python and SQL Alchemy
⌨️ (4:11:06) 3 step approach to Interview Questions
⌨️ (4:36:58) Interview Q - Apple
⌨️ (4:38:59) Interview Q - Linkedin
⌨️ (4:43:09) Interview Q - Meta
⌨️ (4:50:41) Interview Q - Uber
⌨️ (4:59:18) Interview Q - Amazon
⌨️ (5:07:27) Interview Q - Google

🎉 Thanks to our Champion and Sponsor supporters:
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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 : https://www.edureka.co/power-b....i-certification-trai (Use code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")
This Edureka "what is Power BI" video will help you to understand the value brought by the data connection technology into Power BI Desktop and how it provides a powerful tool for transforming and presenting business intelligence data.
Topics Covered:
00:00:00 Introduction
00:01:12 Agenda
00:01:39 Why we need BI
00:02:40 Why Power BI

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC
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🔵 Data Scientist Masters Program: http://bit.ly/3tUAOiT
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🌕 Business Intelligence Masters Program: http://bit.ly/3UZPqJz
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🌕 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh


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Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐀𝐖𝐒 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞: https://www.edureka.co/aws-certification-training (Use code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")
This Edureka video on What is Cloud Computing will help you learn the fundamentals of Cloud Computing.
Topics Covered:
00:00:00 Introduction
00:00:28 Agenda
00:01:04 Why Cloud?
00:03:48 What is Cloud Computing?
00:06:31 Cloud Models
00:14:26 Cloud Providers
00:15:14 Hands-On

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

🔵 DevOps Online Training: http://bit.ly/3VkBRUT
🌕 AWS Online Training: http://bit.ly/3ADYwDY
🔵 React Online Training: http://bit.ly/3Vc4yDw
🌕 Tableau Online Training: http://bit.ly/3guTe6J
🔵 Power BI Online Training: http://bit.ly/3VntjMY
🌕 Selenium Online Training: http://bit.ly/3EVDtis
🔵 PMP Online Training: http://bit.ly/3XugO44
🌕 Salesforce Online Training: http://bit.ly/3OsAXDH
🔵 Cybersecurity Online Training: http://bit.ly/3tXgw8t
🌕 Java Online Training: http://bit.ly/3tRxghg
🔵 Big Data Online Training: http://bit.ly/3EvUqP5
🌕 RPA Online Training: http://bit.ly/3GFHKYB
🔵 Python Online Training: http://bit.ly/3Oubt8M
🌕 Azure Online Training: http://bit.ly/3i4P85F
🔵 GCP Online Training: http://bit.ly/3VkCzS3
🌕 Microservices Online Training: http://bit.ly/3gxYqqv
🔵 Data Science Online Training: http://bit.ly/3V3nLrc
🌕 CEHv12 Online Training: http://bit.ly/3Vhq8Hj
🔵 Angular Online Training: http://bit.ly/3EYcCTe

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬

🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC
🌕 Cloud Architect Masters Program: http://bit.ly/3OvueZy
🔵 Data Scientist Masters Program: http://bit.ly/3tUAOiT
🌕 Big Data Architect Masters Program: http://bit.ly/3tTWT0V
🔵 Machine Learning Engineer Masters Program: http://bit.ly/3AEq4c4
🌕 Business Intelligence Masters Program: http://bit.ly/3UZPqJz
🔵 Python Developer Masters Program: http://bit.ly/3EV6kDv
🌕 RPA Developer Masters Program: http://bit.ly/3OteYfP
🔵 Web Development Masters Program: http://bit.ly/3U9R5va
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This Simplilearn video on How To Introduce Yourself In Interview will brief you on how you can craft a well-prepared self-introduction. Here, we will provide you with an outline of how to get going with a smart self-introduction that will help you grab your dream job. So, let's begin!

The topics covered in this video on How To Introduce Yourself In Interview are:
Introduction 00:00:00
What the Interviewer Wishes to See in Your Self-Introduction 00:00:51
Self-Introduction Outline 00:01:46
Tip 1: Start Your Introduction With a Greeting 00:01:58
Tip 2: Brief About Your Educational Background 00:02:21
Tip 3: Speak About Your Current Job 00:02:47
Tip 4: Hobbies and Passion 00:03:34
Tip 5: Closing Statement 00:03:50
Additional Self-Introduction Tips 00:04:31

Don't forget to participate in the contest at 03:26!

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In this video on what is an OSI model?, we will understand how our system, smartphones, etc. share the different formats of data and information over the network channel.

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Simplilearn’s Introduction to Cyber Security course for beginners is designed to give you a foundational look at today’s cybersecurity landscape and provide you with the tools to evaluate and manage security protocols in information processing systems. In this Introduction to Cyber Security training course, you will gain a comprehensive overview of the cybersecurity principles and concepts and learn the challenges of designing a security program. This course helps you develop and manage an Information Security Program, perform business impact analysis, and carry out disaster recovery testing.

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