Complete Roadmap to learn Generative AI in 2 months 👇👇
Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.
Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.
Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.
Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn Generative AI 👇👇
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Deep Learning Nanodegree Program with Real-world Projects
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.
Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.
Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.
Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn Generative AI 👇👇
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Deep Learning Nanodegree Program with Real-world Projects
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
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David Baum - Generative AI and LLMs for Dummies (2024).pdf
1.9 MB
Generative AI and LLMs for Dummies
David Baum, 2024
David Baum, 2024
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Why Generative AI is trending these days?
1. Technological Advancements: Recent breakthroughs in AI architectures, such as GPT-4 and other large language models, have significantly improved the capabilities of generative AI, making it more powerful and versatile.
2. Wide Range of Applications: Generative AI can be used for diverse tasks, including content creation (text, images, music), code generation, chatbots, personalized recommendations, and more, which broadens its appeal across various industries.
3. Increased Accessibility: Cloud services and AI platforms have made advanced AI tools more accessible to developers, businesses, and even hobbyists, lowering the barrier to entry.
4. Business Value: Companies are recognizing the potential for generative AI to drive innovation, improve efficiency, and create new products and services, leading to increased investment and adoption.
5. Enhanced User Experience: Generative AI can provide highly personalized and engaging user experiences, which is highly valued in areas like marketing, customer service, and entertainment.
6. Media and Public Interest: The impressive capabilities of generative AI, such as creating human-like text and realistic images, capture public imagination and media attention, contributing to its trendiness.
7. Open Source and Community Efforts: The open-source movement and collaborative research communities have accelerated the development and dissemination of generative AI technologies.
1. Technological Advancements: Recent breakthroughs in AI architectures, such as GPT-4 and other large language models, have significantly improved the capabilities of generative AI, making it more powerful and versatile.
2. Wide Range of Applications: Generative AI can be used for diverse tasks, including content creation (text, images, music), code generation, chatbots, personalized recommendations, and more, which broadens its appeal across various industries.
3. Increased Accessibility: Cloud services and AI platforms have made advanced AI tools more accessible to developers, businesses, and even hobbyists, lowering the barrier to entry.
4. Business Value: Companies are recognizing the potential for generative AI to drive innovation, improve efficiency, and create new products and services, leading to increased investment and adoption.
5. Enhanced User Experience: Generative AI can provide highly personalized and engaging user experiences, which is highly valued in areas like marketing, customer service, and entertainment.
6. Media and Public Interest: The impressive capabilities of generative AI, such as creating human-like text and realistic images, capture public imagination and media attention, contributing to its trendiness.
7. Open Source and Community Efforts: The open-source movement and collaborative research communities have accelerated the development and dissemination of generative AI technologies.
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Scientists use generative AI to answer complex questions in physics
Researchers from MIT and the University of Basel in Switzerland applied generative artificial intelligence models to this problem, developing a new machine-learning framework that can automatically map out phase diagrams for novel physical systems.
Their physics-informed machine-learning approach is more efficient than laborious, manual techniques which rely on theoretical expertise. Importantly, because their approach leverages generative models, it does not require huge, labeled training datasets used in other machine-learning techniques.
Such a framework could help scientists investigate the thermodynamic properties of novel materials or detect entanglement in quantum systems, for instance. Ultimately, this technique could make it possible for scientists to discover unknown phases of matter autonomously.
Source-Link: MIT
Researchers from MIT and the University of Basel in Switzerland applied generative artificial intelligence models to this problem, developing a new machine-learning framework that can automatically map out phase diagrams for novel physical systems.
Their physics-informed machine-learning approach is more efficient than laborious, manual techniques which rely on theoretical expertise. Importantly, because their approach leverages generative models, it does not require huge, labeled training datasets used in other machine-learning techniques.
Such a framework could help scientists investigate the thermodynamic properties of novel materials or detect entanglement in quantum systems, for instance. Ultimately, this technique could make it possible for scientists to discover unknown phases of matter autonomously.
Source-Link: MIT
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David Baum - Generative AI and LLMs for Dummies (2024).pdf
1.9 MB
Generative AI and LLMs for Dummies
David Baum, 2024
David Baum, 2024
❤5
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Andrew karpathy launched its llm course 👇👇
https://github.com/karpathy/LLM101n
https://github.com/karpathy/LLM101n
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GEN AI Course for free:
https://github.com/aishwaryanr/awesome-generative-ai-guide?tab=readme-ov-file#ongoing-applied-llms-mastery-2024
https://github.com/aishwaryanr/awesome-generative-ai-guide?tab=readme-ov-file#ongoing-applied-llms-mastery-2024
GitHub
GitHub - aishwaryanr/awesome-generative-ai-guide: A one stop repository for generative AI research updates, interview resources…
A one stop repository for generative AI research updates, interview resources, notebooks and much more! - aishwaryanr/awesome-generative-ai-guide
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