Complete Roadmap to learn Generative AI in 2 months ππ
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 ππ
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 LEARNINGππ
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 ππ
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 LEARNINGππ
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Generative AI pinned Β«Complete Roadmap to learn Generative AI in 2 months ππ 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 algebraβ¦Β»
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:
- 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.
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.
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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.
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.
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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.
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.
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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.
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.
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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.
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.
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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.
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.
Machine Learning (17.4%)
Models: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Naive Bayes, Neural Networks (including Deep Learning)
Techniques: Training/testing data splitting, cross-validation, feature scaling, model evaluation metrics (accuracy, precision, recall, F1-score)
Data Manipulation (13.9%)
Techniques: Data cleaning (handling missing values, outliers), data wrangling (sorting, filtering, aggregating), data transformation (scaling, normalization), merging datasets
Programming Skills (11.7%)
Languages: Python (widely used in data science for its libraries like pandas, NumPy, scikit-learn), R (another popular choice for statistical computing), SQL (for querying relational databases)
Statistics and Probability (11.7%)
Concepts: Descriptive statistics (mean, median, standard deviation), hypothesis testing, probability distributions (normal, binomial, Poisson), statistical inference
Big Data Technologies (9.3%)
Tools: Apache Spark, Hadoop, Kafka (for handling large and complex datasets)
Data Visualization (9.3%)
Techniques: Creating charts and graphs (scatter plots, bar charts, heatmaps), storytelling with data, choosing the right visualizations for the data
Model Deployment (9.3%)
Techniques: Cloud platforms (AWS SageMaker, Google Cloud AI Platform, Microsoft Azure Machine Learning), containerization (Docker), model monitoring
Models: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Naive Bayes, Neural Networks (including Deep Learning)
Techniques: Training/testing data splitting, cross-validation, feature scaling, model evaluation metrics (accuracy, precision, recall, F1-score)
Data Manipulation (13.9%)
Techniques: Data cleaning (handling missing values, outliers), data wrangling (sorting, filtering, aggregating), data transformation (scaling, normalization), merging datasets
Programming Skills (11.7%)
Languages: Python (widely used in data science for its libraries like pandas, NumPy, scikit-learn), R (another popular choice for statistical computing), SQL (for querying relational databases)
Statistics and Probability (11.7%)
Concepts: Descriptive statistics (mean, median, standard deviation), hypothesis testing, probability distributions (normal, binomial, Poisson), statistical inference
Big Data Technologies (9.3%)
Tools: Apache Spark, Hadoop, Kafka (for handling large and complex datasets)
Data Visualization (9.3%)
Techniques: Creating charts and graphs (scatter plots, bar charts, heatmaps), storytelling with data, choosing the right visualizations for the data
Model Deployment (9.3%)
Techniques: Cloud platforms (AWS SageMaker, Google Cloud AI Platform, Microsoft Azure Machine Learning), containerization (Docker), model monitoring
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π Featuretools for feature generation
-
Featuretools is a Python library for automated feature development, i.e. defining variables from the data set for training the ML model.
Featuretools excels at converting temporal and relational datasets into feature matrices for machine learning.
π₯ GitHub
π§βπ» Docks
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python -m pip install featuretoolsFeaturetools is a Python library for automated feature development, i.e. defining variables from the data set for training the ML model.
Featuretools excels at converting temporal and relational datasets into feature matrices for machine learning.
π₯ GitHub
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π₯ deepface - Python library for facial recognition and more
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β© deepface is a lightweight Python library that allows you to find faces and analyze various attributes from photographs: age, gender, emotions.
It incorporates the best of the VGG-Face, FaceNet, OpenFace, DeepFace, DeepID, ArcFace, Dlib, SFace and GhostFaceNet models.
β© This is how you can compare the similarity of 2 faces, the result is in the image:
π₯ GitHub
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pip install deepfaceβ© deepface is a lightweight Python library that allows you to find faces and analyze various attributes from photographs: age, gender, emotions.
It incorporates the best of the VGG-Face, FaceNet, OpenFace, DeepFace, DeepID, ArcFace, Dlib, SFace and GhostFaceNet models.
β© This is how you can compare the similarity of 2 faces, the result is in the image:
from deepface import DeepFace
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")
π₯ GitHub
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Google, Harvard, and even OpenAI are offering FREE Generative AI courses (no payment required) π
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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Google introduced updates to the search engine .
β With expanded AI Overviews, more planning and research capabilities, and AI-organized search results, our custom Gemini model can take the legwork out of searching β
β With expanded AI Overviews, more planning and research capabilities, and AI-organized search results, our custom Gemini model can take the legwork out of searching β
Understanding Generative AI: It's Not AGI
What is Generative AI?
Generative AI refers to algorithms designed to generate new content β from text to images β 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:
β’ Specialization: Generative AI excels in specific tasks it's trained for but lacks the ability to perform beyond its training.
β’ 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.
β’ 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 β from text to images β 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:
β’ Specialization: Generative AI excels in specific tasks it's trained for but lacks the ability to perform beyond its training.
β’ 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.
β’ 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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Now here is a list of my personal real world application of generative AI in marketing. I'll dive deeper into each of those with examples in the upcoming posts.
1. Writing Reports No One Reads:
AI excels at drafting those lengthy reports that turn into digital paperweights. Itβs great at fabricating long-winded BS within token limits. I usually draft an outline and ask ChatGPT to generate it section by section, up to 50 pages.
2. Summarizing Reports No One Reads:
Need to digest that tedious 50-page report without actually reading it? AI can condense it to a digestible one-pager. Itβs also handy for summarizing podcasts, videos, and video calls.
3. Customizing Outbound/Nurturing Messages:
AI can tailor your pitches by company or job title, but itβs only as effective as the template you provide. Remember, garbage in, garbage out. Later, I'll share tips on crafting non-garbage ones.
4. Generating Visuals for Banners:
AI can whip up visuals faster than a caffeine-fueled art student. The layout though looks like something more than just caffeine was involved. I typically use a Figma template with swappable visuals, perfect for Dall-E creations.
5. AI as Client Support:
Using AI for customer support is akin to chatting with a tree β an animated FAQ that only frustrates clients in need of serious help.
6. Creating Templates for Documents:
Need a research template or a strategy layout? AI can set these up, letting you focus on filling in the key details.
7. Breaking Down Complex Tasks:
Those projects, that you are supposed to break into subtasks, but will to live drains out of you by just looking at them. AI can slice 'em into more manageable parts and actually help you get started.
Note: I recommend turning to LLM in all those cases you just can't start. Writing or copypasting text into ChatGPT is the easiest thing you can do besides just procrastinating. But once you've sent the first message, things just start moving.
1. Writing Reports No One Reads:
AI excels at drafting those lengthy reports that turn into digital paperweights. Itβs great at fabricating long-winded BS within token limits. I usually draft an outline and ask ChatGPT to generate it section by section, up to 50 pages.
2. Summarizing Reports No One Reads:
Need to digest that tedious 50-page report without actually reading it? AI can condense it to a digestible one-pager. Itβs also handy for summarizing podcasts, videos, and video calls.
3. Customizing Outbound/Nurturing Messages:
AI can tailor your pitches by company or job title, but itβs only as effective as the template you provide. Remember, garbage in, garbage out. Later, I'll share tips on crafting non-garbage ones.
4. Generating Visuals for Banners:
AI can whip up visuals faster than a caffeine-fueled art student. The layout though looks like something more than just caffeine was involved. I typically use a Figma template with swappable visuals, perfect for Dall-E creations.
5. AI as Client Support:
Using AI for customer support is akin to chatting with a tree β an animated FAQ that only frustrates clients in need of serious help.
6. Creating Templates for Documents:
Need a research template or a strategy layout? AI can set these up, letting you focus on filling in the key details.
7. Breaking Down Complex Tasks:
Those projects, that you are supposed to break into subtasks, but will to live drains out of you by just looking at them. AI can slice 'em into more manageable parts and actually help you get started.
Note: I recommend turning to LLM in all those cases you just can't start. Writing or copypasting text into ChatGPT is the easiest thing you can do besides just procrastinating. But once you've sent the first message, things just start moving.
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