Ikramullah Naqvi.

02/07/2024

02/07/2024

Bado Badi 😁

02/07/2024

😭

02/07/2024

😂😁😁😂

02/07/2024

Every Pakistani women with no reason 😁😁😂

02/07/2024

Ap konsi percent ma Aati ho? 😁

07/05/2024

Here are the concepts of ML, DL, and AI with simple explanations, examples, and interview questions and answers:

*Machine Learning (ML)*

Concept: Machine Learning is a subset of Artificial Intelligence that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed.

Simple Explanation: Imagine you want to build a system that can recognize images of cats and dogs. You show the system many images of cats and dogs, and it learns to identify patterns and features that distinguish them. Once trained, the system can recognize new images of cats and dogs it has never seen before.

Example: Image classification, speech recognition, recommendation systems

Interview Questions:

1. What is Machine Learning, and how does it differ from traditional programming?
Answer: Machine Learning is a type of AI that enables algorithms to learn from data and make predictions or decisions without being explicitly programmed. Traditional programming involves writing rules and logic to solve a problem, whereas Machine Learning involves training algorithms to learn from data.

2. What are the types of Machine Learning?
Answer: There are three main types of Machine Learning: Supervised Learning (where the algorithm is trained on labeled data), Unsupervised Learning (where the algorithm discovers patterns and relationships in unlabeled data), and Reinforcement Learning (where the algorithm learns through trial and error to maximize a reward).

3. What is overfitting in Machine Learning, and how can it be prevented?
Answer: Overfitting occurs when an algorithm is too complex and performs well on training data but poorly on new, unseen data. This can be prevented by using techniques such as regularization, early stopping, and cross-validation.

*Deep Learning (DL)*

Concept: Deep Learning is a subset of Machine Learning that involves training neural networks with multiple layers to learn complex patterns and representations in data.

Simple Explanation: Imagine you want to build a system that can recognize images of objects in different environments and lighting conditions. You use a neural network with multiple layers to learn features and patterns in the images, and the system becomes increasingly good at recognizing objects.

Example: Image recognition, natural language processing, speech recognition

Interview Questions:

1. What is Deep Learning, and how does it differ from traditional Machine Learning?
Answer: Deep Learning is a type of Machine Learning that uses neural networks with multiple layers to learn complex patterns and representations in data. Traditional Machine Learning uses simpler algorithms and models to learn from data.

2. What are the advantages of Deep Learning over traditional Machine Learning?
Answer: Deep Learning can learn complex patterns and representations in data, handle large amounts of data, and achieve state-of-the-art performance in many applications. However, it requires large amounts of data and computational resources.

3. What is a convolutional neural network (CNN), and how is it used in Deep Learning?
Answer: A CNN is a type of neural network that uses convolutional and pooling layers to extract features from images. It is commonly used in image recognition, object detection, and image segmentation applications.

*Artificial Intelligence (AI)*

Concept: Artificial Intelligence refers to the broader field of research and development aimed at creating machines that can perform tasks that typically require human intelligence, such as understanding language, recognizing images, and making decisions.

Simple Explanation: Imagine you want to build a personal assistant that can understand your voice, recognize your face, and make recommendations based on your preferences. This is an example of Artificial Intelligence, which encompasses Machine Learning, Deep Learning, and other techniques to create intelligent machines.

Example: Virtual assistants, robotics, expert systems

Interview Questions:

1. What is Artificial Intelligence, and how does it differ from Machine Learning and Deep Learning?
Answer: Artificial Intelligence is the broader field of research and development aimed at creating machines that can perform tasks that typically require human intelligence. Machine Learning and Deep Learning are subsets of AI that involve training algorithms to learn from data and make predictions or decisions.

2. What are the types of Artificial Intelligence?
Answer: There are several types of AI, including Narrow or Weak AI (which performs a specific task), General or Strong AI (which performs any intellectual task), and Superintelligence (which significantly surpasses human intelligence).

3. What are the ethical considerations in Artificial Intelligence?
Answer: There are several ethical considerations in AI, including bias and discrimination, privacy and security, and job displacement and economic impact. It is essential to address these considerations to ensure that AI is developed and used responsibly.

10/04/2024

Relu:
It is an activation function...
When data is not separable using linear line then activation function comes into mind...

Relu converts .If number is negative it assign 0 if number is positive it assign the given value...

Image ko pixels ma divide keya fr uski convolution ki using feature map.... In result another picture generated and each pixels have a specific number if number is -ve assign 0 if 0 it remains... If positive number if assignm the same number

01/04/2024

Diffusion models:
Clear image ma noise add krna forward diffusion kehlata ha...
Noisy image ko saaf krny ko backward/ reverse diffusion kehlata ha...

Real world scenario:

Imagine ap na notes bnaya.... Notes ko pencil k sath bachy K hath ma parka di ab socho notes ka kia hashar hoga😁 means noise add on saaf picture called forward diffusion... And then raser sa bachy ki machay hvi gand ko erace kr den to hoga saaf shafaaf notes means remove noise from picture... Called backward/ reverse diffusion...

Diffusion models are special type of encoders-decoders

30/03/2024

What is a neural network?
A neural network is a machine learning program, or model, that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions.

Every neural network consists of layers of nodes, or artificial neurons—an input layer, one or more hidden layers, and an output layer. Each node connects to others, and has its own associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.
Neural networks rely on training data to learn and improve their accuracy over time. Once they are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the best-known examples of a neural network is Google’s search algorithm.

Neural networks are sometimes called artificial neural networks (ANNs) or simulated neural networks (SNNs). They are a subset of machine learning, and at the heart of deep learning models.

Source:

25/03/2024

تیری اک پتنگ کے کٹنے سے
میری متاع حیات لٹ گئی

24/03/2024

*اسرائیل کی فنڈنگ میں ملوث پراڈکٹس اور ان کا متبادل*

*سرف*
Areil ❌
Surf Excel❌
Rin❌
Tide ❌
Express Powe❌
Brite❌
Sunlight❌
Bonus❌
Number✅
Bree_O✅
Brillo✅
Tidy✅
Sufi✅
*مصالحہ جات*
Knorr❌
Shan✅
National✅
Mehran✅
Ahmad✅

*چائے پتی*
Lipton❌
Supream❌
TAPAL✅
Vital✅
Qamar✅
Islamabad✅

*کیچپ*
Knorr❌
National✅
Shangrilla✅

*شیمپو*
Clear❌
Loreal❌
Lifebuay❌
Pomolive❌
Sunsilk❌
Head & Shoulder❌
Paintein❌
Hello Hair✅
Set & Touch✅
Hemani✅

*نیل*
Robin❌
Ujala✅
Power Plus✅

*ڈش واش*
Vim❌
LemonMax❌
Ujala✅
Safoon✅
Brillo✅

*بلچن*
Robin❌
Ujala✅

*ٹائلٹ کلینر*
Harpic❌
Ujala✅
Brillo✅
PowerPlus✅

*بوتل*
Pepsi❌
7up❌
Marinda❌
Due❌
Sting❌
Slice❌
Roar❌
Coca Cola❌
Sprite❌
Fanta❌
Cappy❌
Next Cola✅
Wizy Cola✅
Star Cola✅

*جوس*
Nestle❌
Shezan❌
Frutian✅
Smile✅
Fruti_O✅

*پانی*
Aquiafina❌
Nestle❌
Desani❌
Sufi✅

*پالش*
Charry❌
Bee & Flower✅
Power Plus✅

*ٹوتھ پیسٹ*
Colgate❌
Close_Up❌
Sansodine❌
Medicam✅
Nexra✅
Doctor✅

*ڈش واش صابن*
Vim❌
LemonMax❌
Safoon✅
Brillo✅

*مچھر مار*
Mortein❌
KingTox✅
Tiger✅
Razicam✅

*روم سپرے*
Airwiks❌
Paradise✅

*ریمور کریم*
Veet❌
Anifrench❌
EU✅
Care✅
White Rose✅

*ڈائپر*
Pamper❌
Molfix✅
Rocket✅
Nana✅
Shield✅

*برش*
Sansodine❌
Colgate❌
Ezigrip✅
Doctor✅

*دودھ*
Olperal❌
Milk Pack❌
Dairy Omag❌
Asli Milk✅
Haleeb✅

*فریش کریم*
Olpeal❌
Milk Pack❌
Dubala❌
Haleeb✅
hama✅

*صابن*
Lux❌
Lifebuay❌
Dettole❌
Capri✅
Tibit✅
Himani✅
Sufi Classic✅
Brillo✅

*سکول سامان*
Dollor✅
ORO✅
Bahdur✅
Dux✅

*آئیل اینڈ گھی*
Dalda✅
Sufi✅
Shahtaj✅
Mujahid✅

*کسٹرڈ*
Rafan❌
Natinal✅

*کریم*
Fair and Lovely❌
Loreal❌
Golden Pearl✅
Sandal✅
Bio_Cos✅

*پیٹرولیم جیلی*
Vasline❌
Care✅

*بسکٹ*
LU❌
Oreo❌
English✅
Bisconni✅
Halal✅
Gibs✅

*بے بی پراڈکس*
Johnsons❌
Loreal❌
Care✅
Bimani✅

*باڈی سپرے*
Axe❌
Himani✅

*لوشن*
Vasline❌
Pound❌
NAVIA❌
Fair And Lovly❌
Care✅

*سنیکس*
Lays❌
Cheetos❌
Doritos❌
Kurkura❌
Fritos❌
Kurleez✅
Super Clips✅
Filz✅
Chipso✅

*نوڈل*
Meggie❌
Knoor❌
Shan✅

*فروزن فوڈ*
K&ns❌
Sabroso❌
Salwa❌
Sufi✅

*آئس کریم*
Omore❌
Walls❌
Thicko❌
Yummy❌
*یمی کوالٹی ایشو*
Home Made✅

*کھانسی ٹافی*
Stapsale❌
Max Cool✅
Havet✅

*یہ عام روٹین کی ہر گھر میں استعمال ہونے والی اشیاء ہیں اگر آپ انہیں خریدتے ہیں تو جان لیں کہ آپ اسرائیل کو اس گولی کے پیسے دے رہے ہیں جو ہمارے فلسطینی بہن بھائیوں ماؤں بیٹیوں کو لگ رہی ہیں لہٰذا ہماری ایمانی غیرت کا تقاضا ہے کہ ہم میں سے ہر ایک اسرائیلی پراڈکٹس کا بائکاٹ کرے شکریہ*

●▬▬▬▬▬▬▬▬▬▬●

24/03/2024

.

🌐 Transforming Industries: Real-World NLP Projects That Are Shaping the Future 🌐

As we dive deeper into the digital era, Natural Language Processing (NLP) stands out not just as a technological advancement, but as a cornerstone in transforming how businesses and societies operate. Here are a few groundbreaking real-world NLP projects making significant impacts across various sectors:

Healthcare Revolution: AI-powered NLP tools are decoding complex medical records to offer personalized patient care. Projects like IBM Watson are analyzing unstructured data to assist in diagnosis, treatment plans, and even in the discovery of new drugs.

Financial Foresight: NLP is revolutionizing financial services by enabling smarter fraud detection systems and automating customer service interactions. Beyond customer-facing applications, it's also being used for sentiment analysis to predict market trends by analyzing news articles and social media.

Legal Assistance: AI and NLP are streamlining legal operations, from automating contract reviews to predicting legal outcomes. Tools like ROSS Intelligence are assisting lawyers by quickly finding relevant case law to support their cases.

Enhancing Education: Tailored learning experiences are being crafted using NLP to analyze students' responses and adapt materials accordingly. Platforms like Duolingo use NLP to improve language learning, making education more accessible and personalized.

Sustainable Agriculture: NLP projects are analyzing social media, weather reports, and other textual data to provide farmers with actionable insights, from crop management to market trends, contributing to more sustainable farming practices.

These projects illustrate just the tip of the iceberg. NLP is not only advancing technological capabilities but is also creating more intuitive, efficient, and inclusive services and solutions.

As we continue to explore the vast potential of NLP, it's clear that its impact will be felt across every aspect of our lives, redefining the way we work, communicate, and make decisions.

24/03/2024

🚀 Unlocking the Power of Language: An Introduction to NLP 🚀

In the fast-paced world of technology, one acronym has been making waves for its incredible potential to transform how we interact with machines: NLP, or Natural Language Processing.

NLP stands at the intersection of computer science, artificial intelligence, and linguistics, empowering computers to understand, interpret, and generate human language in a meaningful way. From simplifying customer service with chatbots to enabling real-time translations and enhancing search engine efficiency, NLP is revolutionizing industries across the globe.

But NLP's potential doesn't stop at improving current systems. It's paving the way for innovations that were once the realm of science fiction—think intelligent personal assistants that understand context, sentiment, and nuance in our speech, or advanced analytics tools that can sift through vast amounts of textual data to identify trends and insights.

As we continue to refine these technologies, the possibilities are endless. Whether you're a developer, a business leader, or just someone fascinated by the intersection of technology and language, NLP represents a thrilling frontier of innovation and opportunity.

23/03/2024

Lala ❤️
محسن پاکستان ❤️
Shahid Afridi

26/02/2024

?😉

21/02/2024

CONFUSION MATRIX:
Noted:
Our desired prediction is Covid+
Actual value is Covid+ and model predicts Covid+... So called TP

Actual value is Covid + and model predicts Covid- so called FN... false es lea q ki model na ghalat prediction ki or negative es lea q ki Covid - is not our desired prediction....

Actual value is Covid- and model predicts Covid+ FP .... False es lea q ki model predicts wrong that is Covid+ but positive es lea q ki model predicts Covid+ which is our desired prediction..

Actual value is Covid- and model predicts Covid- ....

21/02/2024

Under fitting:
Training and testing accuracy are low....
May b algorithm is very simple for example k-N ... Eska real life example ye hoga... For example kal Apki english ka exam ha or poems book ma poem ki last ma summary likhi hoti ha ap wai parh k jain gy or agr exam ma details ma question ay to ap uski answer ni kr pao gy q ki ap na to sirf summary Parh li thi yaani algorithms ya data set Apki boht kam thy...
If you are using neural network then add multiple layers if you are using random forest add more decision tress,, if using decision tree then increase depth of decision tree eska mtlb yai ha ap apny algorithms ko complex karen