In this video I explain how Word2Vec works and it's two model variants in Continuous Bag of words (CBOW) and Skip-Gram. I also give an intuitive understanding of what embeddings are, why they are important as this is fundamentally what this algorithm is trying to learn.

Timestamps:
0:00 - Introduction to Word2vec
0:54 - Understanding Embeddings
5:20 - CBOW model of Word2Vec
8:46 - Skip-Gram model of Word2Vec
9:34 - Outro

Devil may cry 3 all cutscenes full game movie.

devil may cry 3: Dante's Awakening, released in Japan as Devil May Cry 3 (Japanese: デビル メイ クライ 3 Hepburn: Debiru Mei Kurai Surī), is an action-adventure hack and slash video game directed by Hideaki Itsuno, developed and published by Capcom, released in 2005 for the PlayStation 2 and ported to Microsoft Windows in 2006. The game is a prequel to the original Devil May Cry, with a younger Dante.[3] It introduces new combat mechanics with an emphasis on combos and fast paced action. The story is told primarily in cutscenes using the game's engine, with several pre-rendered full motion videos.

Set a decade before the events of the first Devil May Cry in an enchanted tower, Temen-ni-gru, the story centers on the dysfunctional relationship between Dante and his brother Vergil.

Devil May Cry 3 was widely praised by critics, hailing it as a return to form for the series, yet did receive some criticisms for its high level of difficulty in the North American version. This was later addressed by the release of the rebalanced Special Edition of the game.[4][5]. The game was re‐released in 2006 as Devil May Cry 3: Dante’s Awakening, Special Edition (デビル メイ クライ 3 Special Edition) with retooled difficulty levels, the addition of mid-mission checkpoints, "GOLD mode", and Vergil as a playable character. Combined sales of both versions were over 2.3 million and garnering a review aggregate score of 88% on Gamerankings. It has been cited as one of the greatest video games of all time.

A 2005 manga prequel to Devil May Cry 3's storyline was published in Japan and later the United States.

Prologue Vergil - 00:00
Prologue Vergil - 2 01:36
Prologue - 04:05
Mission 1 Opening - 06:09
Mission 1 Break out - 10:25
Mission 2 Temen-ni-gru - 11:25
Mission 2 Chance Meeting - 12:08
Mission 3 Arkham & Vergil - 13:04
Mission 3 The Lady - 14:50
Mission 3 Cerberus - 15:20
Mission 3 Ice-chucks -16:36
Mission 3 First Contact - 17:31
Mission 4 The Uninvited - 18:43

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Our previous discussions of deep net applications were limited to static patterns, but how can a net decipher and label patterns that change with time? For example, could a net be used to scan traffic footage and immediately flag a collision? Through the use of a recurrent net, these real-time interactions are now possible.

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The Recurrent Neural Net (RNN) is the brainchild of Juergen Schmidhuber and Sepp Hochreiter. The three deep nets we’ve seen thus far – MLP, DBN, and CNN – are known as feedforward networks since a signal moves in only one direction across the layers. In contrast, RNNs have a feedback loop where the net’s output is fed back into the net along with the next input. Since RNNs have just one layer of neurons, they are structurally one of the simplest types of nets.

Like other nets, RNNs receive an input and produce an output. Unlike other nets, the inputs and outputs can come in a sequence. Here are some sample applications for different input-output scenarios:
- Single input, sequence of outputs: image captioning
- Sequence of inputs, single output: document classification
- Sequence of inputs, sequence of outputs: video processing by frame, statistical forecasting of demand in Supply Chain Planning

Have you ever used an RNN in one of your projects before? If so, please comment and tell us about your experience.

RNNs are trained using backpropagation through time, which reintroduces the vanishing gradient problem. In fact, the problem is worse with an RNN because each time step is the equivalent of a layer in a feedforward net. Thus if the net is trained for 1000 time steps, the gradient will vanish exponentially as it would in a 1000-layer MLP.

There are different approaches to address this problem, the most popular of which is gating. Gating takes the output of any time step and the next input, and performs a transformation before feeding the result back into the RNN. There are several types of gates, the LSTM being the most popular. Other approaches to address this problem include gradient clipping, steeper gates, and better optimizers.

GPUs are an essential tool for training an RNN. A team at Indico compared the speed boost from using a GPU over a CPU, and found a 250-fold increase. That’s the difference between 1 day and over 8 months!

A recurrent net has one additional capability – it can predict the next item in a sequence, essentially acting as a forecasting engine.

Credits
Nickey Pickorita (YouTube art) -
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Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
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In this video, I am building Deep Learning PC build from scratch. Now just a disclaimer, in order to learn deep learning you don't need to have such an expensive setup. Whatever laptop or personal computer you have should still work ok. If you have a requirements for running heavy jobs, you can utilize cloud for that. For me, NVIDIA gave 2500$ Titan RTX GPU for free and than I spent close to 1000$ for other parts to build this PC. My plan is to run some heavy deep learning jobs on this computer and I will be making videos on that in future.
Special thanks to my nephew (Harsh Patel) who helped me a lot during entire PC build process. Thanks Harsh! :)

Do you want to learn technology from me? Check https://codebasics.io/ for my affordable video courses.

PC Parts
========
Processor: AMD Ryzen 7 3700 8-Core
Motherboard: Asus ROG Strix B450-F Gaming Motherboard
GPU: NVIDIA Titan RTX
SSD: Sabrent Rocket Q 1TB SSD
Power Supply: Thermaltake 650 W power supply
32 GB RAM
Cooling Graphite Thermal Pad (To keep the CPU cool)
BenQ 27 inch monitor
Thermaltake V200 Tempered Glass Case (Cabin)
Magnetic LED strip (for inside lighting)
Wireless keyboard and mouse

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Transformers are the rage nowadays, but how do they work? This video demystifies the novel neural network architecture with step by step explanation and illustrations on how transformers work.

CORRECTIONS:
The sine and cosine functions are actually applied to the embedding dimensions and time steps!

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Έσπρωξες το μαχαίρι, μα η πληγή δεν μένει
Δίχως σημάδι πάλι θα προχωράω εγώ…

Τόσες ευκαιρίες σου δίνω
Να με κοιτάς στα μάτια όταν τον λόγο σου απευθύνω
Θα φύγω δεν θα επιμείνω
Αιτία μη μου δώσεις πίσω να ξαναγυρίσω

Νόμιζες θα ξεχάσω, δίπλα σου θα αλλάξω
Πόσα να ξεπεράσω, πώς να παραδοθώ;
Χόρευα με τα λάθη, όλα δικά σου πάθη
Έδωσα μόνο αγάπη, πες μου τί πήρα εγώ;

Άλλη μια μάχη, πάλι μονάχη ψάχνω προορισμό
Έδωσες λίγα, άλλη σελίδα τώρα ξαναγυρνώ
Φεύγω για να σωθώ…


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Official music video for The Weeknd “Die For You”, from Starboy available everywhere now: http://theweeknd.co/Starboy

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Music video by The Weeknd performing Die For You. © 2021 The Weeknd XO, Inc., Manufactured and Marketed by Republic Records, a Division of UMG Recordings, Inc.

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This Edureka Docker Tutorial for Beginners video on "What is Docker" will help you understand how to use Docker Hub, Docker Images, Docker Container & Docker Compose. ( Docker Tutorial Blog Series: https://goo.gl/32kupf )It also explains Docker's working Architecture and Docker Engine in detail. It also includes a Hands-On session around Docker by the end of which you will learn to pull a centos Docker Image and spin your own Docker Container.
00:00:00 Introduction
00:00:43 Agenda
0001:49 Why Docker
00:08:44 What is Docker
00:12:45 Case Study
00:14:18 Docker Workflow & Architecture
00:16:56 Docker Components
00:20:25 Docker Compose
00:22:17 Docker Swarm
00:24:35 Docker Installation & Setup
00:27:41 Hands On

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This Jenkins tutorial will help you understand what is Jenkins, issues before Jenkins came in, what is continuous integration, features of Jenkins, Jenkins architecture, and you will also see a case study using Jenkins. Jenkins is a continuous integration tool that allows continuous development, test and deployment of newly created codes. Jenkins is primarily a build and release tool, it was written originally by the community as a build and release tool. They did not target it as a continuous integration, continuous deployment or an orchestration tool, it was simply used for releasing builds to production but now Jenkins has a lot of other uses like it's a continuous integration tool. It allows developers to make sure that their environments have the exact same code as their code repositories. Now let us get started and understand how Jenkins is being used in DevOps.

Below topics are explained in this Jenkins tutorial:
1. Before Jenkins ( 00:46 )
2. Issues before Jenkins
3. What is Jenkins? ( 02:17 )
4. What is continuous integration? ( 04:10 )
5. Continuous integration tools ( 05:45 )
6. Features of Jenkins ( 08:43 )
7. Jenkins pipeline ( 10:50 )
8. Jenkins architecture ( 12:52 )
9. Jenkins case study ( 14:43 )

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➡️ Post Graduate Program In DevOps
DevOps training course will bring out the scientist in you. You'll learn how to formalize and document development processes and create a self-documenting system. DevOps certification course will also cover advanced tools like Puppet, SaltStack, and Ansible that help self-governance and automated management at scale.

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This video on Project Management Professional covers all the concepts from basics to advanced. Also, it covers all the information about Project Management like what Project Management is ?, what is A Project ?, what are the Components of Project Management ?, What is A Project Life Cycle ?, and what are the Key Focus Areas in Project Management?

These are the facts we will be discussing in this video:
00:00 Introduction
01:34 What is Project Management?
02:38 What is A Project?
03:15 Components of Project Management
05:14 What is A Project Life Cycle?
09:10 Key Focus Areas in Project Management

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The PMP certification is the global gold standard for project management professionals. Keep abreast of the changes in the project management practices updated in the PMBOK guide - 6th edition and pass the PMP® exam on your first attempt with Simplilearn’s new PMP course. The course covers new trends, emerging practices, and tailoring considerations, and places a greater emphasis on strategic and business knowledge. It also includes a new section on the role of the project manager.

PMP Certification Course Overview
The course covers new trends, emerging practices, tailoring considerations, and core competencies required of a Project Management professional. Placing a greater emphasis on strategic and business knowledge, this course also includes a new section on the role of the project manager in both large and small companies.

PMP Course Key Features:
- 35 contact hours/PDUs
- 8 industry case studies, 20 industry-based scenarios
- 6 hands-on projects, 7 simulation test papers (200 questions each)

Eligibility:
The PMP® certification is an essential professional requirement for senior project manager roles across all industries. The course is best suited for: Project Managers, Associate/Assistant Project Managers, Team Leads/Team Managers, Project Executives/Project Engineers, Software Developers, and Any professional aspiring to be a Project Manager.

Pre-requisites:
You should have a secondary degree (i.e. high school diploma, associate’s degree, or the global equivalent) with 7,500 hours of leading and directing projects along with 35 hours of project management education. OR You should have a four-year degree with 4,500 hours of leading and directing projects along with 35 hours of project management education.

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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his discussion on factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA).

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

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How to Summarize a Book or a PDF file with GPT-3 and Python

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In today's video, we're going to be showing you how to use GPT-3 to easily summarize a book or paper in PDF form.

Let's face it, sometimes we don't have the time to read a 200 page, 73,000 word document from start to finish. But with GPT-3, you can quickly and easily extract the most important information and summarize it into just 230 words.

This can be a great way to create a step by step guide, research notes, blog posts, or even mid-journey prompts.

In this video, we'll be using the book "Deep Work" by Cal Newport as an example. This book is 190 pages and 73,000 words, so it can be a daunting task to get through it all.

But with the help of GPT-3 and a Python script with the OpenAI API, we'll show you how to automate the process and make summarizing a breeze.
So if you're ready to learn how to use GPT-3 to summarize a book, let's get started!

00:00 Intro Summarize a book or pdf with GPT-3
00:17 Summarizing with GPT-3 and Python Script
04:21 Deep Work Summary with Midjourney V4

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In this video I am going to show you how to tailor (train) the OpenAI Davinci model responses for your chatbot using JavaScript.

00:00 Intro
00:07 Preparing the Data
01:11 Installing OpenAI
02:35 Getting the API Key
02:51 Uploading the data to OpenAI
05:00 Creating a Fine Tune Job
06:16 Checking the progress of the Fine Tune Job
09:20 Get list of Fine Tune Models
10:30 Testing the model
12:00 Cancel a Fine Tune Job
12:41 Delete a Fine Tune Model
13:03 Outro

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This AWS Bootcamp 2023 will help you understand what is AWS (Amazon Web Services), how did AWS become so successful, the services that AWS provides (AWS EC2, Amazon Elastic Beanstalk, Amazon Lightsail, Amazon Lambda, Amazon S3, Amazon redshift, amazon ECS, amazon route 53, amazon vpc, AWS CloudFront, AWS sagemaker, AWS autoscaling, and AWS elastic beanstalk), the future of AWS and a demonstration on deploying, in the end, we’ll also be discussing AWS certification and AWS interview questions for beginners and advanced level. This AWS Solutions Architect Training 2023 video is suitable for those individuals who aspire to become AWS Certified Solution Architects. However, let move ahead and understand what AWS actually is and what are the services that AWS provides to an organization.

AWS Bootcamp Content:
00:00:00 AWS Bootcamp 2023
00:03:57 AWS in 5 Minutes
00:08:49 What is AWS?
00:40:30 Cloud Computing Services
00:00:00 AWS EC2
00:59:03 AWS S3
02:16:32 AWS IAM
00:00:00 AWS Cloud Formation
03:17:45 AWs ECS
03:54:56 AWS Route 53
04:21:05 AWS Beanstalk
05:44:45 AWS Sagemaker
06:09:40 AWS Cloud Front
06:24:15 AWS Autoscaling
06:42:11 AWS Redshift
07:04:00 AWS vs Azure
07:11:48 AWS vs GCP
07:19:43 AWS vs Azure vs GCP
07:32:23 Kubernetes on AWS
08:11:23 How to become Solution Architect
08:20:25 How to become a Cloud Practitioner
08:46:27 Top 10 Resons to choose AWS
08:55:35 AWS Interview Questions

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➡️ Caltech Cloud Computing Bootcamp
This Caltech Cloud Computing Bootcamp, helps you become an expert in designing, planning, and scaling cloud implementations. Our Cloud Computing course helps you master the core skill sets required to design and deploy dynamically scalable and reliable applications on Microsoft Azure, and AWS.

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- 40+ Hands-On Projects and Integrated Labs
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- Receive up to 15 CEUs from Caltech CTME
- Masterclasses taught by Caltech CTME instructor
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✅ Skills Covered
- Cloud Provider Selection
- Application Migration
- Performance Testing
- Cloud Workloads
- Identity Access Management
- Autoscaling
- Disaster Recovery
- Web Services and API
- Cloud Migration and Deployment
- Database Management
- MultiCloud Deployment
- Storage Services

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