Learn Mojo in this full tutorial. The Mojo programming language combines the usability of Python with the performance of C. It's basically an enhanced version of Python specifically designed for Artificial Intelligence developers.

💻 Code: https://github.com/Infatoshi/intro-to-mojo

✏️ Course created by @elliotarledge
Elliot's discord: https://discord.gg/pV7ByF9VNm

⭐️ Contents ⭐️
⌨️ (0:00:00) Intro
⌨️ (0:04:16) What is Mojo
⌨️ (0:07:57) Modular Community
⌨️ (0:12:06) Setting Up
⌨️ (0:17:04) Hello World
⌨️ (0:19:20) Local Jupyter Notebook
⌨️ (0:22:21) Variables, Declarations, and Datatypes
⌨️ (0:33:27) Getting User Input
⌨️ (0:36:28) IF/ELSE Statements
⌨️ (0:40:28) Loops & Functions
⌨️ (0:47:41) Python VS Mojo functions
⌨️ (0:52:26) OOP
⌨️ (1:05:21) Importing Libraries
⌨️ (1:08:48) Raises, Error handling, Exceptions
⌨️ (1:14:49) Inout, Borrowed, Owned, and With Statements
⌨️ (1:21:49) Variable Scope
⌨️ (1:24:46) Mojo CLI
⌨️ (1:35:54) SIMD (single instruction, multiple data)
⌨️ (1:43:47) Decorators & Metaprogramming
⌨️ (1:46:01) Speed test (Mojo VS Python)
⌨️ (1:58:48) How to Ask Questions & Post Errors
⌨️ (2:08:13) Final Comments
⌨️ (2:11:49) Outro

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Learn how to use Next.js to build an SEO-optimized blog using. Also, use TailwindCSS, contentlayer, Supabase, an dmore. The course covers building a homepage, blog pages, category pages, an about page, and a contact page. Also, you'll learn to implementing powerful SEO strategies for higher Google rankings. Our blog features both dark and light themes, offering a minimal and elegant design that's fully responsive on mobile devices. Plus, you'll discover how to showcase your Markdown-based blogs easily on this website.

Course developed by @CodeBucks

Watch the final section of this course here: https://youtu.be/1QGLHOaRLwM?si=TnQmWgGoL6ferRIu&t=19896

Demo Link: https://create-blog-with-nextjs.vercel.app/

✏️ Starter Code: https://github.com/codebucks27..../Nextjs-contentlayer
✏️ Final Code: https://github.com/codebucks27..../Nextjs-tailwindcss-

⭐️ Contents ⭐️
⌨️ (0:00:00) Intro + Demo
⌨️ (0:10:10) Setup and Installation
⌨️ (0:13:34) Tailwind CSS Setup, Dark Mode and project files
⌨️ (0:19:09) Adding Fonts
⌨️ (0:26:56) Navbar Component
⌨️ (0:46:31) Setup Contentlayer
⌨️ (1:08:31) How to render a Blog
⌨️ (1:13:11) Creating Home Cover Section
⌨️ (1:34:16) Featured Posts Component
⌨️ (1:50:11) Recent Posts Component
⌨️ (2:00:46) Footer Component
⌨️ (2:16:31) Blog Page
⌨️ (3:02:06) Creating TOC Component
⌨️ (3:20:51) Category Page
⌨️ (3:45:31) About page
⌨️ (3:58:26) Contact page
⌨️ (4:14:11) Insights Component
⌨️ (4:23:41) Store blog views using Supabase
⌨️ (4:49:16) Adding SEO
⌨️ (5:18:36) Adding Dark Mode
⌨️ (5:30:36) Making it Responsive

Watch the final section of this course here: https://youtu.be/1QGLHOaRLwM?si=TnQmWgGoL6ferRIu&t=19896
⌨️ Adding Sitemap
⌨️ Image Optimization
⌨️ Adding Favicon and manifest file
⌨️ Adding google's structured json-ld for SEO

Correction:
6:55:15 We have created one file called ThemeScript.js but that is not needed. Instead, just add id and strategy to the script that is in the layout.js file.

🎉 Thanks to our Champion and Sponsor supporters:
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👾 Serhiy Kalinets
👾 Justin Hual
👾 Otis Morgan
👾 Oscar Rahnama

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This Python programming full course video will cover all the basics to advanced concepts of Python programming. This Python Full Course is filled with real-life examples along with in-depth hands-on demos.
00:00:00 Introduction
00:10:49 Python Installation
00:44:45 Object oriented programming concepts
00:22:05 Python project
01:46:57 Type Hierarchy
02:04:13 Operator Precedence
02:35:07 Conditional Statements
02:54:25 Iterators, Modules, Packages, Generators
06:14:26 List Comprehensions
06:26:06 Regular Expression
06:59:02 Serialization
07:09:26 Closures & decorators

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What is artificial general intelligence? And how do we get there?

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In this .Net Tutorial video, you will learn everything about .net from basic to advanced topics. This .Net Framework tutorial video covers all the basic concepts of dot net, OOPS, Parameters & How strings are stored in memory, Boxing & Unboxing, Static class, method & variables, the difference between String & String builder, Exception Handling, SQL command, Lambda Operators & Delegates, and Dot Net interview questions.

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00:01:35 - Introduction to .Net
00:46:03 - Hands-on Demo
01:04:28 - Looping control structure
01:34:50 - Boxing and Unboxing
01:40:35 - Method Overloading
01:45:32 - Constructors
01:53:14 - Static Methods
02:04:10 - Inheritance
02:13:47 - Polymorphism
02:14:51 - String vs StringBuilder
02:25:18 - Exception Handling
02:50:13 - Abstract Class
04:09:14 - Connected Architecture and SQL Command
04:40:07 - LINQ and Lamda Expressions
05:10:32 - .NET Interview questions

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🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py
This video on Artificial Intelligence gives you a brief introduction to AI discussing how it can be a threat as well as useful.

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⏩ NEW Top 10 Technologies To Learn In 2024 - https://www.youtube.com/watch?v=vaLXPv0ewHU

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Prepare for a job interview about deep learning. This course covers 50 common interview questions related to deep learning and gives detailed explanations.

✏️ Course created by Tatev Karen Aslanyan.

✏️ Expanded course with 100 questions: https://academy.lunartech.ai/p....roduct/deep-learning

⭐️ Contents ⭐️
⌨️ 0:00:00 Introduction
⌨️ 0:08:20 Question 1: What is Deep Learning?
⌨️ 0:11:45 Question 2: How does Deep Learning differ from traditional Machine Learning?
⌨️ 0:15:25 Question 3: What is a Neural Network?
⌨️ 0:21:40 Question 4: Explain the concept of a neuron in Deep Learning
⌨️ 0:24:35 Question 5: Explain architecture of Neural Networks in simple way
⌨️ 0:31:45 Question 6: What is an activation function in a Neural Network?
⌨️ 0:35:00 Question 7: Name few popular activation functions and describe them
⌨️ 0:47:40 Question 8: What happens if you do not use any activation functions in a neural network?
⌨️ 0:48:20 Question 9: Describe how training of basic Neural Networks works
⌨️ 0:53:45 Question 10: What is Gradient Descent?
⌨️ 1:03:50 Question 11: What is the function of an optimizer in Deep Learning?
⌨️ 1:09:25 Question 12: What is backpropagation, and why is it important in Deep Learning?
⌨️ 1:17:25 Question 13: How is backpropagation different from gradient descent?
⌨️ 1:19:55 Question 14: Describe what Vanishing Gradient Problem is and it’s impact on NN
⌨️ 1:25:55 Question 15: Describe what Exploding Gradients Problem is and it’s impact on NN
⌨️ 1:33:55 Question 16: There is a neuron in the hidden layer that always results in an error. What could be the reason?
⌨️ 1:37:50 Question 17: What do you understand by a computational graph?
⌨️ 1:43:28 Question 18: What is Loss Function and what are various Loss functions used in Deep Learning?
⌨️ 1:47:15 Question 19: What is Cross Entropy loss function and how is it called in industry?
⌨️ 1:50:18 Question 20: Why is Cross-entropy preferred as the cost function for multi-class classification problems?
⌨️ 1:53:10 Question 21: What is SGD and why it’s used in training Neural Networks?
⌨️ 1:58:24 Question 22: Why does stochastic gradient descent oscillate towards local minima?
⌨️ 2:03:38 Question 23: How is GD different from SGD?
⌨️ 2:08:19 Question 24: How can optimization methods like gradient descent be improved? What is the role of the momentum term?
⌨️ 2:14:22 Question 25: Compare batch gradient descent, minibatch gradient descent, and stochastic gradient descent.
⌨️ 2:19:12 Question 26: How to decide batch size in deep learning (considering both too small and too large sizes)?
⌨️ 2:26:01 Question 27: Batch Size vs Model Performance: How does the batch size impact the performance of a deep learning model?
⌨️ 2:29:33 Question 28: What is Hessian, and how can it be used for faster training? What are its disadvantages?
⌨️ 2:34:12 Question 29: What is RMSProp and how does it work?
⌨️ 2:38:43 Question 30: Discuss the concept of an adaptive learning rate. Describe adaptive learning methods
⌨️ 2:43:34 Question 31: What is Adam and why is it used most of the time in NNs?
⌨️ 2:49:59 Question 32: What is AdamW and why it’s preferred over Adam?
⌨️ 2:54:50 Question 33: What is Batch Normalization and why it’s used in NN?
⌨️ 3:03:19 Question 34: What is Layer Normalization, and why it’s used in NN?
⌨️ 3:06:20 Question 35: What are Residual Connections and their function in NN?
⌨️ 3:15:05 Question 36: What is Gradient clipping and their impact on NN?
⌨️ 3:18:09 Question 37: What is Xavier Initialization and why it’s used in NN?
⌨️ 3:22:13 Question 38: What are different ways to solve Vanishing gradients?
⌨️ 3:25:25 Question 39: What are ways to solve Exploding Gradients?
⌨️ 3:26:42 Question 40: What happens if the Neural Network is suffering from Overfitting relate to large weights?
⌨️ 3:29:18 Question 41: What is Dropout and how does it work?
⌨️ 3:33:59 Question 42: How does Dropout prevent overfitting in NN?
⌨️ 3:35:06 Question 43: Is Dropout like Random Forest?
⌨️ 3:39:21 Question 44: What is the impact of Drop Out on the training vs testing?
⌨️ 3:41:20 Question 45: What are L2/L1 Regularizations and how do they prevent overfitting in NN?
⌨️ 3:44:39 Question 46: What is the difference between L1 and L2 regularisations in NN?
⌨️ 3:48:43 Question 47: How do L1 vs L2 Regularization impact the Weights in a NN?
⌨️ 3:51:56 Question 48: What is the curse of dimensionality in ML or AI?
⌨️ 3:53:04 Question 49: How deep learning models tackle the curse of dimensionality?
⌨️ 3:56:47 Question 50: What are Generative Models, give examples?

🔥Edureka Business Intelligence Masters Training: https://www.edureka.co/masters....-program/business-in
This Edureka Business Intelligence Full Course video will help you understand and learn the fundamentals concepts of Tableau and Power BI. This Business Intelligence training video is ideal for both beginners as well as professionals who want to master the fundamentals of Tableau and Power BI. Topics covered
what is BI? - 00:02:44
Quiz - 06:54:28
How to Become a Business Intelligence Analyst? - 00:07:23
Data Warehousing and BI - 00:22:30
Quiz - 01:50:29
Data Warehouse Concepts - 01:50:51
Excel for Beginners - 02:28:25
Quiz - 02:44:23
Excel Formulas and Functions - 02:44:39
Quiz - 03:12:40
VLOOKUP in Excel - 03:12:59
Quiz - 03:31:53
Excel Pivot Tables - 03:32:07
Quiz - 03:47:42
Advanced Excel - 03:48:05
Quiz - 04:18:21
Power BI for Beginners - 04:18:44
How to Build Power BI Dashboards - 05:19:34
Power Query - 05:45:16
Data Visualization Using Power BI Desktop - 06:16:21
Introduction to Tableau - 06:30:48
Getting Started with Tableau - 06:34:09
Quiz - 06:44:09
Tableau vs QlikView - 06:44:30
What is Tableau Server? - 06:57:41
Tableau Online - 07:07:57
Top 5 BI Tools - 07:58:17
Power BI vs MSBI - 08:04:38
Data Warehouse Interview Questions and Answers - 08:13:57
BI Analyst Interview Questions & Answers - 09:33:37

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In this video I show how to go one level deeper and not only do model using subclassing but also build the layers by yourself. Specifically we're building a very simple model consisting of dense layers and relu and our goal is to build the dense and relu layers by yourself and show the general structure of building custom layers.

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🔥 Edureka Data Science Master Program Training Certification (Use Code: YOUTUBE20) : https://www.edureka.co/masters....-program/data-scient
This Edureka video on 'How to become a Data Scientist' will give you an insight into why the data scientist job is in such a high demand. It then elaborates on different roles and responsibilities of a Data Scientist and what skills are required to fulfill those roles and responsibilities. Then, it build a roadmap that you can follow to become a Data Science Wizard (i.e. Data Scientist) and also it briefly provides you with a sense of how much time it takes to go through the roadmap. The video ends with some incredibly useful resources and courses to help you achieve your goal of becoming a Data Scientist.

00:00:00 Agenda
00:01:28 Why Become a Data Scientist?
00:03:58 What does a Data Scientist do?
00:07:14 Skills Required
00:09:50 How to become a Data Scientist?
00:18:35 Useful Resources

Following pointers are covered in this How to Become an AI Engineer:
1) Why become a Data Scientist?
2) What does a Data Scientist do?
3) Skills required
4) How to become a Data Scientist
5) Useful Resources

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About the Master's Program

This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands-on expertise. By the end of the program, you will be ready for seasoned Data Science job roles.

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Why should I enroll for the Masters Program?

The Data Scientist Masters Program has been curated after thorough research and recommendations from industry experts. It will help you master concepts of Data Management, Statistics, Machine Learning and Big Data together with hands-on experience of tools & systems used by Data Scientists including Data Visualisation using Tableau. Edureka will be by your side throughout the learning journey - We’re Ridiculously Committed.

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What are the prerequisites for enrollment?

There are no prerequisites for enrollment to the Masters Program. Whether you are an experienced professional working in the IT industry, or an aspirant planning to enter the world of Data Scientist, Masters Program is designed and developed to accommodate various professional backgrounds.

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How long will it take me to be a Certified Data Science professional?

The recommended duration to complete this program is 34 weeks, however, it is up to the individual to complete this program as per their own pace

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