If you're into deep learning, then you know that students usually one of the two paths:
- Computer vision
- Natural language processing (NLP)
If you're into NLP, here are 5 fundamental concepts you should know:
Before we start, What is NLP?
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through language.
It enables machines to understand, interpret, and respond to human language in a way that is both meaningful and useful.
Data scientists need NLP to analyze, process, and generate insights from large volumes of textual data, aiding in tasks ranging from sentiment analysis to automated summarization.
Tokenization
Tokenization involves breaking down text into smaller units, such as words or phrases. This is the first step in preprocessing textual data for further analysis or NLP applications.
Part-of-Speech Tagging:
This process involves identifying the part of speech for each word in a sentence (e.g., noun, verb, adjective). It is crucial for various NLP tasks that require understanding the grammatical structure of text.
Stemming and Lemmatization
These techniques reduce words to their base or root form. Stemming cuts off prefixes and suffixes, while lemmatization considers the morphological analysis of the words, leading to more accurate results.
Named Entity Recognition (NER)
NER identifies and classifies named entities in text into predefined categories such as the names of persons, organizations, locations, etc. It's essential for tasks like data extraction from documents and content classification.
Sentiment Analysis
This technique determines the emotional tone behind a body of text. It's widely used in business and social media monitoring to gauge public opinion and customer sentiment.
- Computer vision
- Natural language processing (NLP)
If you're into NLP, here are 5 fundamental concepts you should know:
Before we start, What is NLP?
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through language.
It enables machines to understand, interpret, and respond to human language in a way that is both meaningful and useful.
Data scientists need NLP to analyze, process, and generate insights from large volumes of textual data, aiding in tasks ranging from sentiment analysis to automated summarization.
Tokenization
Tokenization involves breaking down text into smaller units, such as words or phrases. This is the first step in preprocessing textual data for further analysis or NLP applications.
Part-of-Speech Tagging:
This process involves identifying the part of speech for each word in a sentence (e.g., noun, verb, adjective). It is crucial for various NLP tasks that require understanding the grammatical structure of text.
Stemming and Lemmatization
These techniques reduce words to their base or root form. Stemming cuts off prefixes and suffixes, while lemmatization considers the morphological analysis of the words, leading to more accurate results.
Named Entity Recognition (NER)
NER identifies and classifies named entities in text into predefined categories such as the names of persons, organizations, locations, etc. It's essential for tasks like data extraction from documents and content classification.
Sentiment Analysis
This technique determines the emotional tone behind a body of text. It's widely used in business and social media monitoring to gauge public opinion and customer sentiment.
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If you want to Excel in AI and become an expert, master these essential concepts:
Core AI Concepts:
โข Machine Learning (ML) โ Supervised, Unsupervised, and Reinforcement Learning
โข Deep Learning (DL) โ Neural Networks, CNNs, RNNs, Transformers
โข Natural Language Processing (NLP) โ Text processing, LLMs (GPT, BERT)
โข Computer Vision (CV) โ Image classification, Object detection
โข AI Ethics & Bias โ Responsible AI development
Essential AI Tools & Frameworks:
โข Python Libraries โ TensorFlow, PyTorch, Scikit-Learn, Keras
โข Data Processing โ Pandas, NumPy, OpenCV, NLTK, SpaCy
โข Pretrained Models โ OpenAI GPT, Stable Diffusion, DALLยทE, CLIP
โข MLOps & Deployment โ Docker, FastAPI, Hugging Face, Flask, Gradio
Mathematical Foundations:
โข Linear Algebra โ Vectors, Matrices, Tensors
โข Probability & Statistics โ Bayesโ Theorem, Hypothesis Testing
โข Optimization โ Gradient Descent, Backpropagation
AI in Real-World Applications:
โข Chatbots & Virtual Assistants โ Build AI-powered bots
โข Recommendation Systems โ Personalized content suggestions
โข Autonomous Systems โ Self-driving cars, Robotics
โข AI in Healthcare โ Disease prediction, Medical imaging
Future Trends in AI:
โข AGI (Artificial General Intelligence) โ Next-level AI development
โข AI in Business & Automation โ AI-powered decision-making
โข Low-Code/No-Code AI โ Democratizing AI for everyone
Free AI Resources:https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
Like it if you need a complete tutorial on all these topics! ๐โค๏ธ
Core AI Concepts:
โข Machine Learning (ML) โ Supervised, Unsupervised, and Reinforcement Learning
โข Deep Learning (DL) โ Neural Networks, CNNs, RNNs, Transformers
โข Natural Language Processing (NLP) โ Text processing, LLMs (GPT, BERT)
โข Computer Vision (CV) โ Image classification, Object detection
โข AI Ethics & Bias โ Responsible AI development
Essential AI Tools & Frameworks:
โข Python Libraries โ TensorFlow, PyTorch, Scikit-Learn, Keras
โข Data Processing โ Pandas, NumPy, OpenCV, NLTK, SpaCy
โข Pretrained Models โ OpenAI GPT, Stable Diffusion, DALLยทE, CLIP
โข MLOps & Deployment โ Docker, FastAPI, Hugging Face, Flask, Gradio
Mathematical Foundations:
โข Linear Algebra โ Vectors, Matrices, Tensors
โข Probability & Statistics โ Bayesโ Theorem, Hypothesis Testing
โข Optimization โ Gradient Descent, Backpropagation
AI in Real-World Applications:
โข Chatbots & Virtual Assistants โ Build AI-powered bots
โข Recommendation Systems โ Personalized content suggestions
โข Autonomous Systems โ Self-driving cars, Robotics
โข AI in Healthcare โ Disease prediction, Medical imaging
Future Trends in AI:
โข AGI (Artificial General Intelligence) โ Next-level AI development
โข AI in Business & Automation โ AI-powered decision-making
โข Low-Code/No-Code AI โ Democratizing AI for everyone
Free AI Resources:https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
Like it if you need a complete tutorial on all these topics! ๐โค๏ธ
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How to revolutionize Hollywood with AI.
Unlock new possibilities:
1. Voice Cloning
Clone voices of Hollywood icons:
โข Legally clone and use voices with permission.
โข Recreate iconic voices for new projects.
โข Preserve legendary performances for future generations.
2. Custom Voices
Create unique voices for your projects:
โข Generate up to 20 seconds of dialogue.
โข Select from preset voice options or create your own.
3. Lip Sync Tool
Bring still characters to life:
โข Use ElevenLabs's Lip Sync tool.
โข Select a face and add a script.
โข Generate videos with synchronized lip movements.
AI is reshaping the industry, voice cloning is part of a broader trend.
Filmmakers can now recreate voices of iconic actors.
Unlock new possibilities:
1. Voice Cloning
Clone voices of Hollywood icons:
โข Legally clone and use voices with permission.
โข Recreate iconic voices for new projects.
โข Preserve legendary performances for future generations.
2. Custom Voices
Create unique voices for your projects:
โข Generate up to 20 seconds of dialogue.
โข Select from preset voice options or create your own.
3. Lip Sync Tool
Bring still characters to life:
โข Use ElevenLabs's Lip Sync tool.
โข Select a face and add a script.
โข Generate videos with synchronized lip movements.
AI is reshaping the industry, voice cloning is part of a broader trend.
Filmmakers can now recreate voices of iconic actors.
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Want to build your first AI agent?
Join a live hands-on session by GeeksforGeeks & Salesforce for working professionals
- Build with Agent Builder
- Assign real actions
- Get a free certificate of participation
Registeration link:๐
https://gfgcdn.com/tu/V4t/
Join a live hands-on session by GeeksforGeeks & Salesforce for working professionals
- Build with Agent Builder
- Assign real actions
- Get a free certificate of participation
Registeration link:๐
https://gfgcdn.com/tu/V4t/
www.geeksforgeeks.org
Practice | GeeksforGeeks | A computer science portal for geeks
Platform to practice programming problems. Solve company interview questions and improve your coding intellect
Step-by-Step Approach to Learn Python
โ Learn the Basics โ Syntax, Variables, Data Types (int, float, string, boolean)
โ
โ Control Flow โ If-Else, Loops (For, While), List Comprehensions
โ
โ Data Structures โ Lists, Tuples, Sets, Dictionaries
โ
โ Functions & Modules โ Defining Functions, Lambda Functions, Importing Modules
โ
โ File Handling โ Reading/Writing Files, CSV, JSON
โ
โ Object-Oriented Programming (OOP) โ Classes, Objects, Inheritance, Polymorphism
โ
โ Error Handling & Debugging โ Try-Except, Logging, Debugging Techniques
โ
โ Advanced Topics โ Regular Expressions, Multi-threading, Decorators, Generators
Free Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
ENJOY LEARNING ๐๐
โ Learn the Basics โ Syntax, Variables, Data Types (int, float, string, boolean)
โ
โ Control Flow โ If-Else, Loops (For, While), List Comprehensions
โ
โ Data Structures โ Lists, Tuples, Sets, Dictionaries
โ
โ Functions & Modules โ Defining Functions, Lambda Functions, Importing Modules
โ
โ File Handling โ Reading/Writing Files, CSV, JSON
โ
โ Object-Oriented Programming (OOP) โ Classes, Objects, Inheritance, Polymorphism
โ
โ Error Handling & Debugging โ Try-Except, Logging, Debugging Techniques
โ
โ Advanced Topics โ Regular Expressions, Multi-threading, Decorators, Generators
Free Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
ENJOY LEARNING ๐๐
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Google, Harvard, and even OpenAI are offering FREE Generative AI courses (no payment required) ๐
Here are 8 FREE courses to master AI in 2024:
1. Google AI Courses
5 courses covering generative AI from the ground up
https://www.cloudskillsboost.google/paths/118
2. Microsoft AI Course
Basics of AI, neural networks, and deep learning
https://microsoft.github.io/AI-For-Beginners/
3. Introduction to AI with Python (Harvard)
7-week course exploring AI concepts and algorithms
https://www.edx.org/learn/artificial-intelligence/harvard-university-cs50-s-introduction-to-artificial-intelligence-with-python
4. ChatGPT Prompt Engineering for Devs (OpenAI & DeepLearning)
Best practices and hands-on prompting experience
https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
5. LLMOps (Google Cloud & DeepLearning)
Learn the LLMOps pipeline and deploy custom LLMs
https://www.deeplearning.ai/short-courses/llmops/
Here are 8 FREE courses to master AI in 2024:
1. Google AI Courses
5 courses covering generative AI from the ground up
https://www.cloudskillsboost.google/paths/118
2. Microsoft AI Course
Basics of AI, neural networks, and deep learning
https://microsoft.github.io/AI-For-Beginners/
3. Introduction to AI with Python (Harvard)
7-week course exploring AI concepts and algorithms
https://www.edx.org/learn/artificial-intelligence/harvard-university-cs50-s-introduction-to-artificial-intelligence-with-python
4. ChatGPT Prompt Engineering for Devs (OpenAI & DeepLearning)
Best practices and hands-on prompting experience
https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
5. LLMOps (Google Cloud & DeepLearning)
Learn the LLMOps pipeline and deploy custom LLMs
https://www.deeplearning.ai/short-courses/llmops/
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Essential Skills to Master for Using Generative AI
1๏ธโฃ Prompt Engineering
โ๏ธ Learn how to craft clear, detailed prompts to get accurate AI-generated results.
2๏ธโฃ Data Literacy
๐ Understand data sources, biases, and how AI models process information.
3๏ธโฃ AI Ethics & Responsible Usage
โ๏ธ Know the ethical implications of AI, including bias, misinformation, and copyright issues.
4๏ธโฃ Creativity & Critical Thinking
๐ก AI enhances creativity, but human intuition is key for quality content.
5๏ธโฃ AI Tool Familiarity
๐ Get hands-on experience with tools like ChatGPT, DALLยทE, Midjourney, and Runway ML.
6๏ธโฃ Coding Basics (Optional)
๐ป Knowing Python, SQL, or APIs helps customize AI workflows and automation.
7๏ธโฃ Business & Marketing Awareness
๐ข Leverage AI for automation, branding, and customer engagement.
8๏ธโฃ Cybersecurity & Privacy Knowledge
๐ Learn how AI-generated data can be misused and ways to protect sensitive information.
9๏ธโฃ Adaptability & Continuous Learning
๐ AI evolves fastโstay updated with new trends, tools, and regulations.
Master these skills to make the most of AI in your personal and professional life! ๐ฅ
Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
1๏ธโฃ Prompt Engineering
โ๏ธ Learn how to craft clear, detailed prompts to get accurate AI-generated results.
2๏ธโฃ Data Literacy
๐ Understand data sources, biases, and how AI models process information.
3๏ธโฃ AI Ethics & Responsible Usage
โ๏ธ Know the ethical implications of AI, including bias, misinformation, and copyright issues.
4๏ธโฃ Creativity & Critical Thinking
๐ก AI enhances creativity, but human intuition is key for quality content.
5๏ธโฃ AI Tool Familiarity
๐ Get hands-on experience with tools like ChatGPT, DALLยทE, Midjourney, and Runway ML.
6๏ธโฃ Coding Basics (Optional)
๐ป Knowing Python, SQL, or APIs helps customize AI workflows and automation.
7๏ธโฃ Business & Marketing Awareness
๐ข Leverage AI for automation, branding, and customer engagement.
8๏ธโฃ Cybersecurity & Privacy Knowledge
๐ Learn how AI-generated data can be misused and ways to protect sensitive information.
9๏ธโฃ Adaptability & Continuous Learning
๐ AI evolves fastโstay updated with new trends, tools, and regulations.
Master these skills to make the most of AI in your personal and professional life! ๐ฅ
Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
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Rise_of_Generative_AI_and_ChatGPT.pdf
5.2 MB
Rise of Generative AI and ChatGPT
Utpal Chakraborty, 2023
Utpal Chakraborty, 2023
๐งญ Roadmap to Learn Generative AI (2025 Edition)
1. Master Python Programming (1 Month)
Learn basic syntax, data structures, and object-oriented programming.
Practice with libraries like NumPy, pandas, and Matplotlib.
Understand how to build simple applications using Python.
2. Understand Machine Learning Fundamentals (1 Month)
Grasp core concepts like supervised, unsupervised learning, and reinforcement learning.
Study algorithms such as linear regression, decision trees, k-means clustering, etc.
Learn about model evaluation metrics.
3. Dive into Deep Learning (1 Month)
Explore neural networks and architectures such as Feedforward Neural Networks (FNN), CNN, and RNN.
Learn about backpropagation, activation functions, and optimization techniques.
4. Grasp Generative Models (1 Month)
Study Autoencoders (AEs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs).
Understand how these models generate new data by learning from existing data.
5. Explore Natural Language Processing (NLP) (1 Month)
Learn about text preprocessing, embeddings, and sequence models.
Study the transformers architecture and attention mechanisms.
Understand how models like GPT and BERT work.
6. Engage with Generative AI Tools (1 Month)
Get hands-on with frameworks like Hugging Face for pre-trained models.
Learn to fine-tune models and build generative applications using these tools.
7. Work on Real-World Projects (Ongoing)
Apply your skills by developing projects such as chatbots, content generators, or image generators.
Continuously work on open-source projects or participate in competitions to improve your skills.
8. Join the AI Community (Ongoing)
Engage in forums, attend webinars, and follow AI researchers.
๐ Suggested 6-Month Learning Plan
Month 1: Python Programming
Month 2: Machine Learning Fundamentals
Month 3: Deep Learning Basics
Month 4: Generative Models
Month 5: Natural Language Processing
Month 6: Generative AI Tools & Real-World Projects
1. Master Python Programming (1 Month)
Learn basic syntax, data structures, and object-oriented programming.
Practice with libraries like NumPy, pandas, and Matplotlib.
Understand how to build simple applications using Python.
2. Understand Machine Learning Fundamentals (1 Month)
Grasp core concepts like supervised, unsupervised learning, and reinforcement learning.
Study algorithms such as linear regression, decision trees, k-means clustering, etc.
Learn about model evaluation metrics.
3. Dive into Deep Learning (1 Month)
Explore neural networks and architectures such as Feedforward Neural Networks (FNN), CNN, and RNN.
Learn about backpropagation, activation functions, and optimization techniques.
4. Grasp Generative Models (1 Month)
Study Autoencoders (AEs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs).
Understand how these models generate new data by learning from existing data.
5. Explore Natural Language Processing (NLP) (1 Month)
Learn about text preprocessing, embeddings, and sequence models.
Study the transformers architecture and attention mechanisms.
Understand how models like GPT and BERT work.
6. Engage with Generative AI Tools (1 Month)
Get hands-on with frameworks like Hugging Face for pre-trained models.
Learn to fine-tune models and build generative applications using these tools.
7. Work on Real-World Projects (Ongoing)
Apply your skills by developing projects such as chatbots, content generators, or image generators.
Continuously work on open-source projects or participate in competitions to improve your skills.
8. Join the AI Community (Ongoing)
Engage in forums, attend webinars, and follow AI researchers.
๐ Suggested 6-Month Learning Plan
Month 1: Python Programming
Month 2: Machine Learning Fundamentals
Month 3: Deep Learning Basics
Month 4: Generative Models
Month 5: Natural Language Processing
Month 6: Generative AI Tools & Real-World Projects
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๐๐จ๐ฐ ๐ญ๐จ ๐๐๐ ๐ข๐ง ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ ๐๐ ๐๐ง๐ญ๐ฌ
๐น ๐๐๐ฏ๐๐ฅ ๐: ๐ ๐จ๐ฎ๐ง๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐จ๐ ๐๐๐ง๐๐ ๐๐ง๐ ๐๐๐
โช๏ธ Introduction to Generative AI (GenAI): Understand the basics of Generative AI, its key use cases, and why it's important in modern AI development.
โช๏ธ Large Language Models (LLMs): Learn the core principles of large-scale language models like GPT, LLaMA, or PaLM, focusing on their architecture and real-world applications.
โช๏ธ Prompt Engineering Fundamentals: Explore how to design and refine prompts to achieve specific results from LLMs.
โช๏ธ Data Handling and Processing: Gain insights into data cleaning, transformation, and preparation techniques crucial for AI-driven tasks.
๐น ๐๐๐ฏ๐๐ฅ ๐: ๐๐๐ฏ๐๐ง๐๐๐ ๐๐จ๐ง๐๐๐ฉ๐ญ๐ฌ ๐ข๐ง ๐๐ ๐๐ ๐๐ง๐ญ๐ฌ
โช๏ธ API Integration for AI Models: Learn how to interact with AI models through APIs, making it easier to integrate them into various applications.
โช๏ธ Understanding Retrieval-Augmented Generation (RAG): Discover how to enhance LLM performance by leveraging external data for more informed outputs.
โช๏ธ Introduction to AI Agents: Get an overview of AI agentsโautonomous entities that use AI to perform tasks or solve problems.
โช๏ธ Agentic Frameworks: Explore popular tools like LangChain or OpenAIโs API to build and manage AI agents.
โช๏ธ Creating Simple AI Agents: Apply your foundational knowledge to construct a basic AI agent.
โช๏ธ Agentic Workflow Overview: Understand how AI agents operate, focusing on planning, execution, and feedback loops.
โช๏ธ Agentic Memory: Learn how agents retain context across interactions to improve performance and consistency.
โช๏ธ Evaluating AI Agents: Explore methods for assessing and improving the performance of AI agents.
โช๏ธ Multi-Agent Collaboration: Delve into how multiple agents can collaborate to solve complex problems efficiently.
โช๏ธ Agentic RAG: Learn how to integrate Retrieval-Augmented Generation techniques within AI agents, enhancing their ability to use external data sources effectively.
Join for more AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
๐น ๐๐๐ฏ๐๐ฅ ๐: ๐ ๐จ๐ฎ๐ง๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐จ๐ ๐๐๐ง๐๐ ๐๐ง๐ ๐๐๐
โช๏ธ Introduction to Generative AI (GenAI): Understand the basics of Generative AI, its key use cases, and why it's important in modern AI development.
โช๏ธ Large Language Models (LLMs): Learn the core principles of large-scale language models like GPT, LLaMA, or PaLM, focusing on their architecture and real-world applications.
โช๏ธ Prompt Engineering Fundamentals: Explore how to design and refine prompts to achieve specific results from LLMs.
โช๏ธ Data Handling and Processing: Gain insights into data cleaning, transformation, and preparation techniques crucial for AI-driven tasks.
๐น ๐๐๐ฏ๐๐ฅ ๐: ๐๐๐ฏ๐๐ง๐๐๐ ๐๐จ๐ง๐๐๐ฉ๐ญ๐ฌ ๐ข๐ง ๐๐ ๐๐ ๐๐ง๐ญ๐ฌ
โช๏ธ API Integration for AI Models: Learn how to interact with AI models through APIs, making it easier to integrate them into various applications.
โช๏ธ Understanding Retrieval-Augmented Generation (RAG): Discover how to enhance LLM performance by leveraging external data for more informed outputs.
โช๏ธ Introduction to AI Agents: Get an overview of AI agentsโautonomous entities that use AI to perform tasks or solve problems.
โช๏ธ Agentic Frameworks: Explore popular tools like LangChain or OpenAIโs API to build and manage AI agents.
โช๏ธ Creating Simple AI Agents: Apply your foundational knowledge to construct a basic AI agent.
โช๏ธ Agentic Workflow Overview: Understand how AI agents operate, focusing on planning, execution, and feedback loops.
โช๏ธ Agentic Memory: Learn how agents retain context across interactions to improve performance and consistency.
โช๏ธ Evaluating AI Agents: Explore methods for assessing and improving the performance of AI agents.
โช๏ธ Multi-Agent Collaboration: Delve into how multiple agents can collaborate to solve complex problems efficiently.
โช๏ธ Agentic RAG: Learn how to integrate Retrieval-Augmented Generation techniques within AI agents, enhancing their ability to use external data sources effectively.
Join for more AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
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