For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

November 11, 2025
This lecture covers agents, prompts, and RAG.

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

Please follow along with the course schedule and syllabus: https://cs230.stanford.edu/syllabus/

More lectures will be published regularly.
View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

NOTE: There was no class on November 4, 2025 (Lecture 7). The previous lecture is Lecture 6.

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department

Baltimore 8K Video Ultra HD 120 FPS - City in Maryland || 8K TV Test Video
8K Video Ultra HD, 60fps, 120fps, 240fps for Your 8K Resolution Device: Apple TV, 8K TV Samsung, LG TV, OLED TV, QLED TV, Sony Device or iPhone, Huawei, Xiaomi, Asus Phone and your others 8k device.

》This 8K Demo video is make for Educational and Entertainment purposes. You can use it to learn About Country, City, Nature, Place, River and etc with 8K Ultra HD Resolution.

》I did high color correction, color change, bit rate, raw video editing, merge files, optimize 8K demo video and more.

》About Baltimore : Baltimore is a major city in Maryland with a long history as an important seaport. Fort McHenry, birthplace of the U.S. national anthem, “The Star-Spangled Banner,” sits at the mouth of Baltimore’s Inner Harbor. Today, this harbor area offers shops, upscale crab shacks and attractions like the Civil War–era warship the USS Constellation and the National Aquarium, showcasing thousands of marine creatures. ― Google
Area: 239 km²
Weather: 22°C, Wind E at 5 km/h, 78% Humidity weather.com
Local time: Saturday 8:33 AM
Area code: Area code 410
Mayor: Brandon Scott
Population: 602,274 (2020)
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1. Fort McHenry National Monument and Historic Shrine
2. National Aquarium
3. American Visionary Art Museum
4. Baltimore Museum of Art
5. Inner Harbor and Historic Ships
6. See a Game: Oriole Park at Camden Yards
7. Fell's Point
8. Baltimore and Ohio Railroad Museum
9. Maryland Science Center
10. Baltimore Museum of Industry
11. National Cryptologic Museum
12. Maryland Zoo in Baltimore
13. Basilica of the National Shrine of the Assumption of the Blessed Virgin Mary
14. Washington Monument and Mount Vernon
15. Little Italy & etc.

----------------------------------------------------
》Thumbnail Credits: Canva
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》Music in This Video by Envato Element
I Use Paid Music in this Video
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》All Credits Goes to Youtube
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© Disclaimer:
》All The Footage Used In this Video Licensed by 8K Earth.
》I Use Paid Stock Footage & All Pictures In My Channel.
》Video Footage Copyright Under Standard License.
》I tried to Present a new way by Changing the color of the Video.

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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐉𝐚𝐯𝐚 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: https://www.edureka.co/java-j2ee-training-course (Use code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")
This Edureka video on "Interface on Java" will provide you with detailed knowledge about Java Interface and also cover some real time examples in order to provide you a better understanding of the functionality of Java Interface. This video will cover the following topics:
00:00:00 Introduction
00:00:22 Agenda
00:00:56 What is an Interface?
00:01:30 Why do we need an Interface?
00:03:20 Practical examples on Interface
00:05:32 Interface Nesting
00:07:02 Difference between Class and Interface
00:08:30 Advantages and Disadvantages of Interface
00:09:10 Key Points on Interface.
00:09:55 Practical Examples of Interface on Key Points

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬

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📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
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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 Tensorflow Training (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"):
https://www.edureka.co/ai-deep....-learning-with-tenso
This Edureka "𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝" video will help you in understanding why we need Transformers and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using Transformers.

🔹Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
🔹Check our complete Deep Learning With TensorFlow Blog Series: http://bit.ly/2sqmP4s
🔴Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV

00:00 Introduction
00:41 Introduction to Deep Learning
04:48 NLP Using RNN
06:16 Scaling up NLP Task
08:13 Transformers Walk Through
10:16 Top Language Models


📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
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📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/
00:00 Agenda
01:28 Introduction to Deep Learning
08:24 What is Deep Learning
13:02 NLP using RNN
15:59 Scaling up NLP task
16:49 Transformers WalkThrough
19:51 Top Language Models

#Edureka #EdurekaDeepLearning #Transformersxplained #TransformerNeuralNetworks #DeepLearningLanguageModels #DeepearningTutorial #EdurekaTraining

---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧---------

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---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬---------

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----------------------------------
How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
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 have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

- - - - - - - - - - - - - -

About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

- - - - - - - - - - - - - -

Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

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

#ai #artificialintelligence #chatgpt #midjourney #llm #largelanguagemodels #largelanguagemodel

This is a short and very simple video I created to explain some of the concepts that go into large language models like Chat-GPT4. It covers various aspects of LLM artificial intelligence like foundations, architecture, pre-training, fine-tuning, tokenization, embeddings, self-attention, positional encoding, layers and headers, and decoding. The text was created with the use of chat gpt4. All of the images were created using Midjourney AI based on the text.

🔥NIT Warangal Post Graduate Diploma in AI & Machine Learning with Edureka: https://www.edureka.co/nitw-ai-ml-pgp
This Edureka video of "Chatbots using TensorFlow" gives you an idea about what are chatbots and how did they come into existence. It provides a brief introduction about all the layers involved in creating a chatbot using TensorFlow and Machine Learning.

Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV

Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz

---------------------------------------Edureka Post Graduate Courses-------------------------------------------

🔵 Artificial and Machine Learning PGD: https://bit.ly/3AylL0q

- - - - - - - - - - - - - -

How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
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 have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

- - - - - - - - - - - - - -

About the Course

Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.


- - - - - - - - - - - - - -

Who should go for this course?

The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'

2. Analytics Managers who are leading a team of analysts

3. Business Analysts who want to understand Deep Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. Professionals who want to captivate and analyze Big Data

6. Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.

- - - - - - - - - - - - - -

Why Learn Deep Learning With TensorFlow?

TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

---------------------------------
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#Autoencoder #Tensorflow #DeepLearning #NeuralNetworks #python #MachineLearning #DimensionalityReduction
-------------------------------------

Got a question on the topic?
Please share it in the comment section below and our experts will answer it for you.

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

In this video you will learn about three very common methods for data dimensionality reduction: PCA, t-SNE and UMAP. These are especially useful when you want to visualise the latent space of an autoencoder.

If you want to learn more about these techniques, here are some key papers:
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction https://arxiv.org/abs/1802.03426
- Stochastic Neighbor Embedding https://papers.nips.cc/paper_f....iles/paper/2002/hash
- Visualizing Data using t-SNE https://www.jmlr.org/papers/vo....lume9/vandermaaten08

And if you want to learn about even more recent techniques such as TriMAP and PACMAP, here are the papers:
- TriMap: Large-scale Dimensionality Reduction Using Triplets https://arxiv.org/abs/1910.00204
- PaCMAP https://arxiv.org/abs/2012.04456

Chapters:
00:36 PCA
05:15 t-SNE
13:30 UMAP
18:02 Conclusion

This video features animations created with Manim, inspired by Grant Sanderson's work at @3blue1brown. Here is the code that I used to make this video: https://github.com/ytdeepia/La....tent-Space-Visualisa

If you enjoyed the content, please like, comment, and subscribe to support the channel!


#DeepLearning #PCA #ArtificialIntelligence #tsne #DataScience #LatentSpace #Manim #Tutorial #machinelearning #education #somepi

This video shows how you can detect if a sequence is periodic or not, and also find its period, using the Discrete Fourier Transform.

This is the second in a series of videos about Fourier transforms.

- First video: The Discrete Fourier Transform https://www.youtube.com/watch?v=T8XZxR5H04E
- Second video: Detecting Periodicity with the DFT (this one)

Grokking Machine Learning Book:
https://www.manning.com/books/....grokking-machine-lea
40% discount promo code: serranoyt

00:00 Introduction
2:37: Periodic sequences
12:54 In the inverse DFT

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

February 6, 2026
This lecture covers:
• A randomized controlled trial examining the impact of AI-mediated feedback on students' disciplinary writing performance and learning
• An introduction to and evaluation of FeedbackWriter
• How knowledge engineering can enhance cognitive fidelity and enable reliable feedback generation

To follow along with the seminar schedule, visit: https://hci.stanford.edu/

Xu Wang is an Assistant Professor in Computer Science and Engineering and the School of Information (By courtesy) at the University of Michigan.

Simple solution to the #kaggle competition which obtains a final score of over 0.92 which would rank us in the top 1% of submissions! :)

Link to santander customer transaction prediction:
https://www.kaggle.com/c/santa....nder-customer-transa

GitHub Repository:
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PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

Timestamps:
0:00 - Introduction to competition
1:50 - Get data
8:22 - Simple NN baseline
20:50 - First results 0.86 score
21:33 - Understanding the data
23:40 - Modifying our NN
28:10 - Improvement to baseline
29:00 - Feature engineering
44:54 - Modifying our NN v2
50:45 - Final result and submission
56:50 - Ending

Theano is a Python library that defines a set of mathematical functions for building deep nets. Nets that use these functions as their building blocks will be highly optimized for training.

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The core feature of Theano is the use of vectors and matrices for all of its functions. Vectorized code runs quickly since multiple values can be processed in parallel. Since Deep Nets require large amounts of computation throughout the training process, vectorization is a highly-recommended feature. Theano is multi-threaded with GPU support, so deep nets can be trained on just a single machine within a reasonable amount of time.

To use Theano for Deep Learning, you must code every aspect of a deep net including the layers, the nodes, the activation, and the training rate. However, all the functions that run your code will be vectorized, resulting in an efficient implementation. Many software libraries extend Theano, making it easier to use in your projects. The Blocks library helps by parameterizing Theano functions. The Lasagne library allows you to specify hyper-parameters in order to build a net layer by layer. Niche libraries like Passage help implement recurrent nets for text analysis.

Do you have experience coding neural nets with the Theano library? Please comment and share your thoughts.

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

In this video, we'll explore the risks and opportunities associated with large language models (LLMs). We'll discuss the potential benefits of LLMs, including their ability to improve natural language processing, generate human-like text, and enhance decision-making. However, we'll also examine the potential risks, such as biases and misuse.

Join us for a balanced discussion of LLMs and their impact on the future of AI.
Learn more about LLMs on our latest blog article: https://www.boost.ai/blog/llms....-large-language-mode

🔥PGP in Generative AI and ML in collaboration with Illinois Tech: https://www.edureka.co/executi....ve-programs/pgp-gene
🔥Generative AI Course: Masters Program: https://www.edureka.co/masters....-program/generative-
00:00:00 Introduction
00:04:16 What is Generative AI?
00:17:44 Generative AI Examples
00:36:23 Generative AI Tools
00:54:10 What Is Artificial Intelligence?
01:00:14 Types Of Artificial Intelligence
01:10:29 What is Deep Learning
01:21:42 TensorFlow Explained
01:46:28 Convolutional Neural Network
02:07:04 Artificial Neural Network
02:38:55Recurrent Neural Networks
03:07:38 Keras
03:32:58 Generative AI Course - Part 1 - What is LLM?
03:52:42 What Are GANs?
04:05:03 Transformers In Gen AI
04:12:42 Prompt Engineering Explained
04:26:27 Prompt Engineering for Code Generation
04:35:46 How to Become a Prompt Engineer
04:40:50 Building a Chatbot with Prompt Engineering
04:57:00 GitHub Copilot
05:14:25 Generative AI Course - Part 2 - What is LangChain?
05:31:40 Generative AI Course - Part 3 - What is RAG?
05:54:33 Generate Images Using DC-GAN
06:19:07 Midjourney
06:37:28 OpenAI API using Python
06:45:43 Generative AI in Marketing
06:56:13 What is Agentic AI?
07:06:13 The Future of Generative AI and Job Opportunities
07:11:57 Nvidia's Latest Breakthrough in Generative AI
07:18:34 DeepSeek vs OpenAI: Who Wins the AI Race?
07:31:07 Alibaba’s Qwen 2.5-Max Just Beat GPT-4 & DeepSeek?
07:35:30 DeepSeek Training Cost
07:40:59 Exploring the 07:48:29 Ethics of Generative AI
Dangers of AI
08:11:15 Artificial Intelligence Project Ideas
08:24:52 Top 10 Benefits Of Artificial Intelligence
08:36:40 GenAI Roadmap
08:45:25 Top 5 Generative AI Career Opportunities
09:01:38 Generative AI Interview Questions

Explore *Generative AI* with this Beginner to Advanced Full Course! Learn key concepts like LLMs, GANs, Transformers, Prompt Engineering, and AI Ethics with hands-on projects. Whether you're a beginner or an AI enthusiast, this course will guide you step-by-step to mastering Generative AI. Watch now and start building with AI today

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For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

October 21, 2025
This lecture covers deep reinforcement learning.

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

To follow along with the course schedule and syllabus, visit: https://cs230.stanford.edu/syllabus/

More lectures will be published regularly.
View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department

📌Generative AI Course: Masters Program : https://www.edureka.co/masters....-program/generative-

This Generative AI full course is designed to help you understand and explore the power of AI in the most simple and practical way. Whether you're a beginner or just curious about how tools like ChatGPT, Midjourney, or other AI systems work, this course covers everything from the basics of what Generative AI is, how to communicate with AI using effective prompts, and real-life use cases in content creation, business, and productivity.
00:00:00:Introduction
00:02:06 What is Machine learning?
00:18:42 Types of Machine Learning Models
00:25:56 Mathematics for Machine Learning
02:08:43 Machine Learning Algo
02:30:38 How to select the correct predictive modeling techniques?
02:42:48 Linear Regression Algorithm
02:50:14 Logistic Regression Algorithm
03:37:27 Linear Regression Vs Logistic Regression
03:40:58 MLOps for Beginners
03:53:00 How to Become a Machine Learning Engineer?
04:02:31 Machine learning Engineer Skills
04:10:40 Machine Learning Roadmap
04:20:32 Machine Learning Tips
04:27:12 What is Generative AI?
04:40:42 Generative AI Examples
04:59:21 Generative AI Tools
05:20:46 What Are GANs?
05:33:08 Generate Images Using DC-GAN
05:57:41 Transformers In Gen AI
06:05:20 Generative AI Course - Part 1 - What is LLM?
06:25:04 Generative AI Course - Part 2 - What is LangChain?
06:42:19 Generative AI Course - Part 3 - What is RAG?
07:05:11 Prompt Engineering Explained
07:18:56 Prompt Engineering for Code Generation
07:28:16 Building a Chatbot with Prompt Engineering
07:44:10 GitHub Copilot
08:01:36 OpenAI API using Python
08:09:51 Midjourney
08:28:12 Generative AI in Marketing
08:38:42 Exploring the Ethics of Generative AI
08:46:12 Nvidia's Latest Breakthrough in Generative AI
08:52:49 DeepSeek vs OpenAI: Who Wins the AI Race?
09:05:22 The Future of Generative AI and Job Opportunities
09:11:06 GenAI Roadmap
09:19:51 Generative AI Interview Questions

🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py

📢📢Check out the latest 2025 video on Top 10 Technologies for the most up-to-date insights!
📌 Top 10 Technologies to Learn in 2025 → https://youtu.be/5kjWh8lBxC4

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