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GeeksforGeeks brings you everything you need to crack GATE 2026 – 900+ live hours, 300+ recorded sessions, and expert mentorship to keep you on track.
What’s inside?
✔ Live & recorded classes with India’s top educators
✔ 200+ mock tests to track your progress
✔ Study materials - PYQs, workbooks, formula book & more
✔ 1:1 mentorship & AI doubt resolution for instant support
✔ Interview prep for IITs & PSUs to help you land opportunities
Learn from Experts Like:
Satish Kumar Yadav – Trained 20K+ students
Dr. Khaleel – Ph.D. in CS, 29+ years of experience
Chandan Jha – Ex-ISRO, AIR 23 in GATE
Vijay Kumar Agarwal – M.Tech (NIT), 13+ years of experience
Sakshi Singhal – IIT Roorkee, AIR 56 CSIR-NET
Shailendra Singh – GATE 99.24 percentile
Devasane Mallesham – IIT Bombay, 13+ years of experience
Use code UPSKILL30 to get an extra 30% OFF (Limited time only)
📌 Enroll for a free counseling session now: https://gfgcdn.com/tu/UI2/
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6. Vue - vueschool.io
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5 Free Courses for Mastering LLMs
1. Introduction to Large Language Models by Google :-
Course Link
2. AI for Educators by Microsoft:- Course Link
3. Cohere’s LLM University:-
Course Link
4. Anthropic Prompt Engineering Courses:-
Course Link
5. Large Language Model Agents:- Course Link
#generativeai
1. Introduction to Large Language Models by Google :-
Course Link
2. AI for Educators by Microsoft:- Course Link
3. Cohere’s LLM University:-
Course Link
4. Anthropic Prompt Engineering Courses:-
Course Link
5. Large Language Model Agents:- Course Link
#generativeai
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Stanford just uploaded their new *"Building LLMS"* lecture. It's a must watch.
These lecture provides a concise overview of building a ChatGPT-like model, covering both pretraining (language modeling) and post-training (SFT/RLHF).
For each component, it explores common practices in data collection, algorithms, and evaluation methods. https://www.youtube.com/watch?v=9vM4p9NN0Ts
These lecture provides a concise overview of building a ChatGPT-like model, covering both pretraining (language modeling) and post-training (SFT/RLHF).
For each component, it explores common practices in data collection, algorithms, and evaluation methods. https://www.youtube.com/watch?v=9vM4p9NN0Ts
YouTube
Stanford CS229 I Machine Learning I Building Large Language Models (LLMs)
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai
This lecture provides a concise overview of building a ChatGPT-like model, covering both pretraining (language modeling) and post-training (SFT/RLHF). For…
This lecture provides a concise overview of building a ChatGPT-like model, covering both pretraining (language modeling) and post-training (SFT/RLHF). For…
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Understanding Generative AI: It's Not AGI
What is Generative AI?
Generative AI refers to algorithms designed to generate new content — from text to images — based on patterns learned from a dataset. Technologies like GPT-4 and DALL-E are popular examples, extensively used for tasks ranging from writing articles to designing graphics.
How Does Generative AI Work?
1 Training: Generative AI models are trained on large datasets, learning the structure, style, and intricacies of the data without human intervention.
2 Pattern Recognition: Through training, these models recognize patterns and correlations in the data, enabling them to predict and generate similar outputs.
3 Output Generation: When provided with a prompt, generative AI uses its training to produce content that aligns with what it has learned, attempting to mimic the input style or respond to the query coherently.
Generative AI vs. AGI:
• Specialization: Generative AI excels in specific tasks it's trained for but lacks the ability to perform beyond its training.
• No Consciousness or Understanding: Unlike AGI, generative AI does not possess consciousness, understanding, or reasoning. It doesn't "think" like humans; it merely processes data based on pre-defined mathematical and probabilistic models.
• Task-Specific: Generative AI operates within the confines of its programming and training, contrasting with AGI's potential to perform any intellectual task that a human can.
Why It Matters:
Understanding the capabilities and limitations of generative AI helps set realistic expectations for its applications. It's a powerful tool for specific tasks but is far from the sci-fi notion of an all-knowing, all-purpose AI.
Generative AI is nowhere near AGI, it even works on different principles. It basically is an average function for non-numerical data. It can create an average text or an average picture from all the texts and pictures it has seen.
What is Generative AI?
Generative AI refers to algorithms designed to generate new content — from text to images — based on patterns learned from a dataset. Technologies like GPT-4 and DALL-E are popular examples, extensively used for tasks ranging from writing articles to designing graphics.
How Does Generative AI Work?
1 Training: Generative AI models are trained on large datasets, learning the structure, style, and intricacies of the data without human intervention.
2 Pattern Recognition: Through training, these models recognize patterns and correlations in the data, enabling them to predict and generate similar outputs.
3 Output Generation: When provided with a prompt, generative AI uses its training to produce content that aligns with what it has learned, attempting to mimic the input style or respond to the query coherently.
Generative AI vs. AGI:
• Specialization: Generative AI excels in specific tasks it's trained for but lacks the ability to perform beyond its training.
• No Consciousness or Understanding: Unlike AGI, generative AI does not possess consciousness, understanding, or reasoning. It doesn't "think" like humans; it merely processes data based on pre-defined mathematical and probabilistic models.
• Task-Specific: Generative AI operates within the confines of its programming and training, contrasting with AGI's potential to perform any intellectual task that a human can.
Why It Matters:
Understanding the capabilities and limitations of generative AI helps set realistic expectations for its applications. It's a powerful tool for specific tasks but is far from the sci-fi notion of an all-knowing, all-purpose AI.
Generative AI is nowhere near AGI, it even works on different principles. It basically is an average function for non-numerical data. It can create an average text or an average picture from all the texts and pictures it has seen.
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Applications of Generative AI
✅ Content Creation – AI writes articles, designs graphics, and generates videos.
✅ Software Development – AI assists in coding, debugging, and software optimization.
✅ Healthcare – AI helps in medical imaging and drug discovery.
✅ Marketing & Business – AI powers chatbots, personalized ads, and customer insights.
✅ Content Creation – AI writes articles, designs graphics, and generates videos.
✅ Software Development – AI assists in coding, debugging, and software optimization.
✅ Healthcare – AI helps in medical imaging and drug discovery.
✅ Marketing & Business – AI powers chatbots, personalized ads, and customer insights.
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Generative AI vs. AGI
⚡ Generative AI is task-specific, working within predefined training limits.
⚡ AGI (Artificial General Intelligence) can adapt and think like a human, which doesn’t exist yet.
Generative AI is powerful but lacks true understanding—it predicts, not thinks.
Limitations of Generative AI
⚠ Bias & Ethical Issues – AI can inherit biases from its training data.
⚠ Computational Costs – Running AI models requires high computing power.
⚠ Lack of True Creativity – AI mimics existing styles but doesn’t create original ideas.
Future of Generative AI
🔹 More efficient models with lower costs
🔹 Stronger ethical guidelines to prevent misuse
🔹 AI as a tool to enhance human creativity, not replace it
⚡ Generative AI is task-specific, working within predefined training limits.
⚡ AGI (Artificial General Intelligence) can adapt and think like a human, which doesn’t exist yet.
Generative AI is powerful but lacks true understanding—it predicts, not thinks.
Limitations of Generative AI
⚠ Bias & Ethical Issues – AI can inherit biases from its training data.
⚠ Computational Costs – Running AI models requires high computing power.
⚠ Lack of True Creativity – AI mimics existing styles but doesn’t create original ideas.
Future of Generative AI
🔹 More efficient models with lower costs
🔹 Stronger ethical guidelines to prevent misuse
🔹 AI as a tool to enhance human creativity, not replace it
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Risks & Challenges of Generative AI
1️⃣ Misinformation & Deepfakes
⚠ AI-generated fake news, images, and videos can spread misinformation, making it harder to distinguish truth from fiction.
2️⃣ Bias in AI Models
🔍 AI learns from existing data, which may include biases. This can lead to unfair or discriminatory outputs in hiring, lending, and law enforcement.
3️⃣ Job Displacement
👨💻 While AI creates new roles, automation may replace repetitive jobs, requiring workers to upskill.
4️⃣ Ethical & Legal Issues
⚖️ AI-generated content raises copyright concerns, data privacy risks, and ethical dilemmas in areas like deepfake misuse.
5️⃣ High Computational Costs
💰 Training and running AI models require massive computing power, making AI expensive for smaller businesses.
6️⃣ Lack of True Understanding
🤖 AI doesn’t "think" or "understand" like humans; it generates content based on patterns, which can sometimes be inaccurate or misleading.
7️⃣ Security Threats
🔐 AI can be exploited for cyberattacks, including generating phishing emails and automating malware creation.
AI is powerful but needs responsible use to prevent harm. Awareness is the first step! 🚀
1️⃣ Misinformation & Deepfakes
⚠ AI-generated fake news, images, and videos can spread misinformation, making it harder to distinguish truth from fiction.
2️⃣ Bias in AI Models
🔍 AI learns from existing data, which may include biases. This can lead to unfair or discriminatory outputs in hiring, lending, and law enforcement.
3️⃣ Job Displacement
👨💻 While AI creates new roles, automation may replace repetitive jobs, requiring workers to upskill.
4️⃣ Ethical & Legal Issues
⚖️ AI-generated content raises copyright concerns, data privacy risks, and ethical dilemmas in areas like deepfake misuse.
5️⃣ High Computational Costs
💰 Training and running AI models require massive computing power, making AI expensive for smaller businesses.
6️⃣ Lack of True Understanding
🤖 AI doesn’t "think" or "understand" like humans; it generates content based on patterns, which can sometimes be inaccurate or misleading.
7️⃣ Security Threats
🔐 AI can be exploited for cyberattacks, including generating phishing emails and automating malware creation.
AI is powerful but needs responsible use to prevent harm. Awareness is the first step! 🚀
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Calculating Free-to-Paid Conversion Rate with SQL Project
Career Track Analysis with SQL and Tableau Project
Like this post if you need more data analytics projects in the channel 😄
Hope it helps :)
Calculating Free-to-Paid Conversion Rate with SQL Project
Career Track Analysis with SQL and Tableau Project
Like this post if you need more data analytics projects in the channel 😄
Hope it helps :)
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How to Use Generative AI Effectively
1️⃣ Be Clear with Prompts
✍️ The more specific your input, the better the output. Use detailed instructions for accurate results.
2️⃣ Verify AI-Generated Content
🔍 AI can make mistakes. Always fact-check information before using it.
3️⃣ Use AI as an Assistant, Not a Replacement
🤝 AI enhances productivity but still needs human creativity and judgment.
4️⃣ Avoid Bias and Ethical Risks
⚠ AI reflects the data it learns from. Be mindful of biases in its responses.
5️⃣ Experiment with Different Models
🔄 Try multiple AI tools (GPT, DALL·E, Midjourney) to see which works best for your needs.
6️⃣ Stay Updated with AI Trends
📢 AI evolves fast. Keep learning about new updates and ethical guidelines.
7️⃣ Use AI to Automate Repetitive Tasks
⏳ AI can handle tasks like summarization, translation, and data analysis, freeing up your time.
AI is a tool—use it wisely to boost efficiency and creativity. 🚀
1️⃣ Be Clear with Prompts
✍️ The more specific your input, the better the output. Use detailed instructions for accurate results.
2️⃣ Verify AI-Generated Content
🔍 AI can make mistakes. Always fact-check information before using it.
3️⃣ Use AI as an Assistant, Not a Replacement
🤝 AI enhances productivity but still needs human creativity and judgment.
4️⃣ Avoid Bias and Ethical Risks
⚠ AI reflects the data it learns from. Be mindful of biases in its responses.
5️⃣ Experiment with Different Models
🔄 Try multiple AI tools (GPT, DALL·E, Midjourney) to see which works best for your needs.
6️⃣ Stay Updated with AI Trends
📢 AI evolves fast. Keep learning about new updates and ethical guidelines.
7️⃣ Use AI to Automate Repetitive Tasks
⏳ AI can handle tasks like summarization, translation, and data analysis, freeing up your time.
AI is a tool—use it wisely to boost efficiency and creativity. 🚀
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How Generative AI is Changing the World
1️⃣ Revolutionizing Content Creation
📝 AI drafts blogs, summarizes reports, and refines content
🎨 AI generates images, logos, and even full animations
🎬 AI enhances video editing and automates noscriptwriting
2️⃣ Boosting Productivity in Tech
💻 AI suggests code, fixes bugs, and improves efficiency
📊 AI extracts insights from large datasets in seconds
🔐 AI detects fraud and strengthens online security
3️⃣ Transforming Healthcare
🩺 AI improves diagnostics from X-rays and MRIs
💊 AI speeds up the process of finding new medicines
📈 AI analyzes patient data to prevent diseases
4️⃣ Reshaping Business & Marketing
🤖 AI chatbots automate customer service and support
📢 AI analyzes behavior to create targeted marketing
💰 AI predicts stock trends and detects fraud
5️⃣ The Ethical & Practical Challenges
⚠ Fake content & deepfakes can mislead people
⚠ AI reflects biases from its training data
⚠ Automation may replace certain jobs
6️⃣ The Future of AI
🔹 AI will become more efficient and less costly
🔹 Regulations will improve ethical AI use
🔹 AI will enhance human creativity, not replace it
Generative AI is here to stay. Are we ready to use it responsibly? 🚀
1️⃣ Revolutionizing Content Creation
📝 AI drafts blogs, summarizes reports, and refines content
🎨 AI generates images, logos, and even full animations
🎬 AI enhances video editing and automates noscriptwriting
2️⃣ Boosting Productivity in Tech
💻 AI suggests code, fixes bugs, and improves efficiency
📊 AI extracts insights from large datasets in seconds
🔐 AI detects fraud and strengthens online security
3️⃣ Transforming Healthcare
🩺 AI improves diagnostics from X-rays and MRIs
💊 AI speeds up the process of finding new medicines
📈 AI analyzes patient data to prevent diseases
4️⃣ Reshaping Business & Marketing
🤖 AI chatbots automate customer service and support
📢 AI analyzes behavior to create targeted marketing
💰 AI predicts stock trends and detects fraud
5️⃣ The Ethical & Practical Challenges
⚠ Fake content & deepfakes can mislead people
⚠ AI reflects biases from its training data
⚠ Automation may replace certain jobs
6️⃣ The Future of AI
🔹 AI will become more efficient and less costly
🔹 Regulations will improve ethical AI use
🔹 AI will enhance human creativity, not replace it
Generative AI is here to stay. Are we ready to use it responsibly? 🚀
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