Free Data Engineering Ebooks & Courses 👇👇
https://t.me/sql_engineer

Will LLMs always hallucinate?

As large language models (LLMs) become more powerful and pervasive, it's crucial that we understand their limitations.

A new paper argues that hallucinations - where the model generates false or nonsensical information - are not just occasional mistakes, but an inherent property of these systems.

While the idea of hallucinations as features isn't new, the researchers' explanation is.

They draw on computational theory and Gödel's incompleteness theorems to show that hallucinations are baked into the very structure of LLMs.

In essence, they argue that the process of training and using these models involves undecidable problems - meaning there will always be some inputs that cause the model to go off the rails.

This would have big implications. It suggests that no amount of architectural tweaks, data cleaning, or fact-checking can fully eliminate hallucinations.

So what does this mean in practice? For one, it highlights the importance of using LLMs carefully, with an understanding of their limitations.

It also suggests that research into making models more robust and understanding their failure modes is crucial.

No matter how impressive the results, LLMs are not oracles - they're tools with inherent flaws and biases

LLM & Generative AI Resources: https://t.me/generativeai_gpt

Andrew Ng just released two new AI Python courses for beginners!

The course teaches how to write code using AI.

If you're thinking about learning to code, now is the perfect time to do so.

https://deeplearning.ai/short-courses/ai-python-for-beginners/

How to Develop an AI Powered Mobile App

Are you ready to dive into the world of artificial intelligence and mobile app development? In the ever-changing tech landscape of India, the development of an AI-powered mobile app is becoming a necessity for both wannabe developers as well as the experienced ones. In this guide, we’ll focus on the steps to build an app with AI, setting out the challenges and prospects faced in the market. (AI App)

Access Full Guide to create an AI app

Here are 8 concise tips to help you ace a technical AI engineering interview:

𝟭. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝗟𝗟𝗠 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 - Cover the high-level workings of models like GPT-3, including transformers, pre-training, fine-tuning, etc.

𝟮. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 - Talk through techniques like demonstrations, examples, and plain language prompts to optimize model performance.

𝟯. 𝗦𝗵𝗮𝗿𝗲 𝗟𝗟𝗠 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 - Walk through hands-on experiences leveraging models like GPT-4, Langchain, or Vector Databases.

𝟰. 𝗦𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 𝗼𝗻 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 - Mention latest papers and innovations in few-shot learning, prompt tuning, chain of thought prompting, etc.

𝟱. 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗺𝗼𝗱𝗲𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 - Compare transformer networks like GPT-3 vs Codex. Explain self-attention, encodings, model depth, etc.

𝟲. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 - Explain supervised fine-tuning, parameter efficient fine tuning, few-shot learning, and other methods to specialize pre-trained models for specific tasks.

𝟳. 𝗗𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 - From tokenization to embeddings to deployment, showcase your ability to operationalize models at scale.

𝟴. 𝗔𝘀𝗸 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - Inquire about model safety, bias, transparency, generalization, etc. to show strategic thinking.

The AI research winery in India is the pillar of the laboratories. AI is seen to be the core of this transformation in all the systems, starting with healthcare and going through agriculture, education, and urban planning, and the Indian research labs are the engines of this rapid transformation. Literally, everything you need to know about the elite

AI research lab centers in India are one step crucial to your living. These centers pave the way not only for cutting-edge research but also for the smartest contribution to the AI revolution in India. For students in their final years of graduate studies and young professionals looking to pursue a Ph.D. in AI or launch an AI startup, understanding the top AI research labs in India is crucial.

Read more......

We are now on WhatsApp as well

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Prompt Engineering in itself does not warrant a separate job.

Most of the things you see online related to prompts (especially things said by people selling courses) is mostly just writing some crazy text to get ChatGPT to do some specific task. Most of these prompts are just been found by serendipity and are never used in any company. They may be fine for personal usage but no company is going to pay a person to try out prompts 😅. Also a lot of these prompts don't work for any other LLMs apart from ChatGPT.

You have mostly two types of jobs in this field nowadays, one is more focused on training, optimizing and deploying models. For this knowing the architecture of LLMs is critical and a strong background in PyTorch, Jax and HuggingFace is required. Other engineering skills like System Design and building APIs is also important for some jobs. This is the work you would find in companies like OpenAI, Anthropic, Cohere etc.

The other is jobs where you build applications using LLMs (this comprises of majority of the companies that do LLM related work nowadays, both product based and service based). Roles in these companies are called Applied NLP Engineer or ML Engineer, sometimes even Data Scientist roles. For this you mostly need to understand how LLMs can be used for different applications as well as know the necessary frameworks for building LLM applications (Langchain/LlamaIndex/Haystack). Apart from this, you need to know LLM specific techniques for applications like Vector Search, RAG, Structured Text Generation. This is also where some part of your role involves prompt engineering. Its not the most crucial bit, but it is important in some cases, especially when you are limited in the other techniques.

You can use ChatGPT to make money online.

Here are 10 prompts by ChatGPT

1. Develop Email Newsletters:

Make interesting email newsletters to keep audience updated and engaged.

Prompt→ "I run a local community news website. Can you help me create a weekly email newsletter that highlights key local events, stories, and updates in a compelling way?"

2. Create Online Course Material:

Make detailed and educational online course content.

Prompt→ "I'm creating an online course about basic programming for beginners. Can you help me generate a syllabus and detailed lesson plans that cover fundamental concepts in an easy-to-understand manner?"

3. Ghostwrite eBooks:

Use ChatGPT to write eBooks on different topics for online sale.

Prompt→ "I want to publish an eBook about healthy eating habits. Can you help me outline and ghostwrite the chapters, focusing on practical tips and easy recipes?"

4. Compose Music Reviews or Critiques:

Use ChatGPT to write detailed reviews of music, albums, and artists.

Prompt: "I run a music review blog. Can you help me write a detailed review of the latest album by [Artist Name], focusing on their musical style, lyrics, and overall impact?"

5. Develop Mobile App Content:

Use ChatGPT to create mobile app content like descriptions, guides, and FAQs.

Prompt: "I'm developing a fitness app and need help writing the app description for the store, user instructions, and a list of frequently asked questions."

6. Create Resume Templates:

Use ChatGPT to create diverse resume templates for various jobs.

Prompt→ "I want to offer a range of professional resume templates on my website. Can you help me create five different templates, each tailored to a specific career field like IT, healthcare, and marketing?"

7. Write Travel Guides:

Use ChatGPT to write travel guides with tips and itineraries for different places.

Prompt→ "I'm creating a travel blog about European cities. Can you help me write a comprehensive guide for first-time visitors to Paris, including must-see sights, local dining recommendations, and travel tips?"

8. Draft Legal Documents:

Use ChatGPT to write basic legal documents like contracts and terms of service.

Prompt→ "I need to draft a terms of service document for my new e-commerce website. Can you help me create a draft that covers all necessary legal points in clear language?"

9. Write Video Game Reviews:

Use ChatGPT to write engaging video game reviews, covering gameplay and graphics.

Prompt→ "I run a gaming blog. Can you help me write a detailed review of the latest [Game Title], focusing on its gameplay mechanics, storyline, and graphics quality?"

10. Develop Personal Branding Materials:

Use ChatGPT to help build a personal branding package, including bios, LinkedIn profiles, and website content.

Prompt→ "I'm a freelance graphic designer looking to strengthen my personal brand. Can you help me write a compelling biography, update my LinkedIn profile, and create content for my portfolio website?"

ENJOY LEARNING 👍👍

30-days learning plan to cover data science fundamental algorithms, important concepts, and practical applications 👇👇

### Week 1: Introduction and Basics

Day 1: Introduction to Data Science
- Overview of data science, its importance, and key concepts.

Day 2: Python Basics for Data Science
- Python syntax, variables, data types, and basic operations.

Day 3: Data Structures in Python
- Lists, dictionaries, sets, and tuples.

Day 4: Data Manipulation with Pandas
- Introduction to Pandas, Series, DataFrame, basic operations.

Day 5: Data Visualization with Matplotlib and Seaborn
- Creating basic plots (line, bar, scatter), customizing plots.

Day 6: Introduction to Numpy
- Arrays, array operations, mathematical functions.

Day 7: Data Cleaning and Preprocessing
- Handling missing values, data normalization, and scaling.

### Week 2: Exploratory Data Analysis and Statistical Foundations

Day 8: Exploratory Data Analysis (EDA)
- Techniques for summarizing and visualizing data.

Day 9: Probability and Statistics Basics
- Descriptive statistics, probability distributions, and hypothesis testing.

Day 10: Introduction to SQL for Data Science
- Basic SQL commands for data retrieval and manipulation.

Day 11: Linear Regression
- Concept, assumptions, implementation, and evaluation metrics (R-squared, RMSE).

Day 12: Logistic Regression
- Concept, implementation, and evaluation metrics (confusion matrix, ROC-AUC).

Day 13: Regularization Techniques
- Lasso and Ridge regression, preventing overfitting.

Day 14: Model Evaluation and Validation
- Cross-validation, bias-variance tradeoff, train-test split.

### Week 3: Supervised Learning

Day 15: Decision Trees
- Concept, implementation, advantages, and disadvantages.

Day 16: Random Forest
- Ensemble learning, bagging, and random forest implementation.

Day 17: Gradient Boosting
- Boosting, Gradient Boosting Machines (GBM), and implementation.

Day 18: Support Vector Machines (SVM)
- Concept, kernel trick, implementation, and tuning.

Day 19: k-Nearest Neighbors (k-NN)
- Concept, distance metrics, implementation, and tuning.

Day 20: Naive Bayes
- Concept, assumptions, implementation, and applications.

Day 21: Model Tuning and Hyperparameter Optimization
- Grid search, random search, and Bayesian optimization.

### Week 4: Unsupervised Learning and Advanced Topics

Day 22: Clustering with k-Means
- Concept, algorithm, implementation, and evaluation metrics (silhouette score).

Day 23: Hierarchical Clustering
- Agglomerative clustering, dendrograms, and implementation.

Day 24: Principal Component Analysis (PCA)
- Dimensionality reduction, variance explanation, and implementation.

Day 25: Association Rule Learning
- Apriori algorithm, market basket analysis, and implementation.

Day 26: Natural Language Processing (NLP) Basics
- Text preprocessing, tokenization, and basic NLP tasks.

Day 27: Time Series Analysis
- Time series decomposition, ARIMA model, and forecasting.

Day 28: Introduction to Deep Learning
- Neural networks, perceptron, backpropagation, and implementation.

Day 29: Convolutional Neural Networks (CNNs)
- Concept, architecture, and applications in image processing.

Day 30: Recurrent Neural Networks (RNNs)
- Concept, LSTM, GRU, and applications in sequential data.

Best Resources to learn Data Science 👇👇

kaggle.com/learn

t.me/datasciencefun

developers.google.com/machine-learning/crash-course

topmate.io/coding/914624

t.me/pythonspecialist

freecodecamp.org/learn/machine-learning-with-python/

Join @free4unow_backup for more free courses

Like for more ❤️

ENJOY LEARNING👍👍

Understanding Popular ML Algorithms:

1️⃣ Linear Regression: Think of it as drawing a straight line through data points to predict future outcomes.

2️⃣ Logistic Regression: Like a yes/no machine - it predicts the likelihood of something happening or not.

3️⃣ Decision Trees: Imagine making decisions by answering yes/no questions, leading to a conclusion.

4️⃣ Random Forest: It's like a group of decision trees working together, making more accurate predictions.

5️⃣ Support Vector Machines (SVM): Visualize drawing lines to separate different types of things, like cats and dogs.

6️⃣ K-Nearest Neighbors (KNN): Friends sticking together - if most of your friends like something, chances are you'll like it too!

7️⃣ Neural Networks: Inspired by the brain, they learn patterns from examples - perfect for recognizing faces or understanding speech.

8️⃣ K-Means Clustering: Imagine sorting your socks by color without knowing how many colors there are - it groups similar things.

9️⃣ Principal Component Analysis (PCA): Simplifies complex data by focusing on what's important, like summarizing a long story with just a few key points.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍

For those of you who are new to Neural Networks, let me try to give you a brief overview.

Neural networks are computational models inspired by the human brain's structure and function. They consist of interconnected layers of nodes (or neurons) that process data and learn patterns. Here's a brief overview:

1. Structure: Neural networks have three main types of layers:
- Input layer: Receives the initial data.
- Hidden layers: Intermediate layers that process the input data through weighted connections.
- Output layer: Produces the final output or prediction.

2. Neurons and Connections: Each neuron receives input from several other neurons, processes this input through a weighted sum, and applies an activation function to determine the output. This output is then passed to the neurons in the next layer.

3. Training: Neural networks learn by adjusting the weights of the connections between neurons using a process called backpropagation, which involves:
- Forward pass: Calculating the output based on current weights.
- Loss calculation: Comparing the output to the actual result using a loss function.
- Backward pass: Adjusting the weights to minimize the loss using optimization algorithms like gradient descent.

4. Activation Functions: Functions like ReLU, Sigmoid, or Tanh are used to introduce non-linearity into the network, enabling it to learn complex patterns.

5. Applications: Neural networks are used in various fields, including image and speech recognition, natural language processing, and game playing, among others.

Overall, neural networks are powerful tools for modeling and solving complex problems by learning from data.

30 Days of Data Science: https://t.me/datasciencefun/1704

Like if you want me to continue data science series 😄❤️

ENJOY LEARNING 👍👍

To start with Machine Learning:

1. Learn Python
2. Practice using Google Colab


Take these free courses:

https://t.me/datasciencefun/290

If you need a bit more time before diving deeper, finish the Kaggle tutorials.

At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.

If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.

From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.

The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:

https://t.me/datasciencefree/259

Many different books will help you. The attached image will give you an idea of my favorite ones.

Finally, keep these three ideas in mind:

1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others.

During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.

Here is the good news:

Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.

Focus on finding your path, and Write. More. Code.

That's how you win.✌️✌️

Let's go from “What can AI do?” to “How can AI deliver scalable value?”

Here are three things you should be watching out for:

𝟭. 𝗔𝗜’𝘀 𝗠𝗮𝗿𝗸𝗲𝘁 𝗖𝗼𝗿𝗿𝗲𝗰𝘁𝗶𝗼𝗻: We’ve seen this before—markets overhype technology, but only those who deliver real business outcomes thrive. This means a combination of strong product differentiation, technical excellence, a competitive business model, and a sustainable growth strategy.

𝟮. 𝗧𝗵𝗲 𝗟𝗮𝘀𝘁-𝗠𝗶𝗹𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: The challenge isn’t building a model, it’s deploying it at scale. To succeed, startups/enterprises must ensure robust data pipelines, optimize for real-world latency, and design scalable infrastructure.

𝟯. 𝗛𝘆𝗽𝗲𝗿𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜: Whether in e-commerce recommendations or AI-driven healthcare, hyperpersonalization brings immense potential but also serious challenges around privacy, fairness, and transparency. Companies building B2B or B2C solutions must focus on building toolkits and guardrails that help them track how data is being collected, what use cases they are used for, and how the output is being processed to various target outcomes.

How do you start AI and ML ?

Where do you go to learn these skills? What courses are the best?

There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.

What’s more important than how you start is why you start.

Start with why.

Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.

Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.

Got a why? Good. Time for some hard skills.

I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course

You can completed courses from (in order):

Treehouse / youtube( free) - Introduction to Python

Udacity - Deep Learning & AI Nanodegree

fast.ai - Part 1and Part 2

They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.

If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.

Join for more: https://t.me/machinelearning_deeplearning

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All the best 👍👍

The power of Ai Hype and LinkedIn