Each ML technique has its own strengths and weaknesses. Depending on the type of data that's available and what's needed to solve the particular problem, AI designers may use one, two, or all three of these techniques to produce an AI-powered solution.

Generative AI
Advancements in machine learning have helped pave the way for generative AI—AI that can generate new content, like text, images, or other media. This type of AI often uses a combination of supervised, unsupervised, and reinforcement learning to create original content.

For instance, all three approaches play distinct roles in conversational AI tools. Supervised learning equips conversational AI tools with foundational dialogue data, enabling them to respond to common conversational cues appropriately. Unsupervised learning enables them to interpret nuances in language, like colloquialisms, that occur naturally in conversation. Reinforcement learning further strengthens these tools by allowing them to improve their responses in real-time based on user feedback. This enables them to adapt to the conversational context and engage in natural conversations.

Generative AI's ability to create and innovate offers a range of benefits to all sorts of workplaces and professions, such as marketing, product development, engineering, education, manufacturing, and research and development. These benefits include:

Greater efficiency: Generative AI can automate or augment routine tasks, allowing workers to focus on other work priorities.

Personalized experiences: Generative AI can tailor its interactions to individual preferences and needs.

Better decisions: Generative AI can quickly analyze vast amounts of data to uncover useful insights.

These are just some of the ways that generative AI can enhance your work.

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The Process Of Training AI Models

AI designers and engineers develop AI models through a process called training. Here’s an example of the typical steps a designer might take in this process, in this case for building a model that predicts rainfall:

Define the problem to be solved.
AI designers and engineers want to predict rain to help people stay dry when commuting to and from work. They start by considering AI’s capabilities and limitations before identifying an AI solution.

Collect relevant data to train the model.
AI designers and engineers gather historical data of days when it rained and days when it didn't rain over the past 50 years.

Prepare the data for training.
AI designers and engineers prepare the data by labeling important features, such as outdoor temperature, humidity, and air pressure, and then noting whether it rained. It's also common to separate the data into two distinct sets: a training set and a validation set to test with later.

Train the model.
AI designers and engineers apply machine learning (ML) programs to their rain prediction model, which helps it recognize patterns in its training data that indicate the likelihood of rainfall. Those patterns might include high temperatures, low air pressure, and high humidity.

Evaluate the model.
AI designers and engineers use the validation set they prepared earlier to assess their model's ability to predict rainfall accurately and reliably. Analyzing a model's performance can uncover potential issues impacting the model, such as insufficient or biased training data. If any issues exist, the AI designers and engineers may revisit an earlier step in this process to try a different approach. Once the model performs well with its validation set, the process continues to the next step.

Deploy the model.
When the AI designers and engineers are satisfied with their model's performance, they deploy it in an AI tool—helping people in their city stay dry on their way to work!

Model training is an iterative process. AI designers and engineers can repeat each step as many times as necessary and make adjustments until they create the best model possible.

But the process doesn't stop at deployment. Once users interact with a model in practical situations, the model might be exposed to new challenges. AI designers and engineers should continuously monitor and collect feedback on their models, ensuring their models continue to perform reliably and to identify areas for improvement. It's this iterative process of continual refinement that makes AI models precise and versatile, which ultimately leads to effective, reliable AI tools. When you understand how AI models are developed, you can make informed decisions about when and how to use an AI tool to accomplish your goals.

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AI Engineers can be quite successful in this role without ever training anything.

This is how:

1/ Leveraging pre-trained LLMs: Select and tune existing LLMs for specific tasks. Don't start from scratch

2/ Prompt engineering: Craft effective prompts to optimize LLM performance without model modifications

3/ Implement Modern AI Solution Architectures: Design systems like RAG to enhance LLMs with external knowledge

Developers: The barrier to entry is lower than ever.

Focus on the solution's VALUE and connect AI components like you were assembling Lego! (Credits: Unknown)

There are several AI tools and libraries available to assist with coding in Python. Here are some of the most popular ones:

1. GitHub Copilot: An AI-powered code completion tool developed by GitHub and OpenAI. It can suggest entire lines or blocks of code based on the context of what you're writing.

2. Tabnine: An AI code completion tool that supports various IDEs and code editors. It uses deep learning models to predict and suggest code completions.

3. Kite: An AI-powered code completion and documentation tool that integrates with many popular IDEs. It offers in-line code completions and documentation for Python.

4. PyCharm's Code Completion: JetBrains' PyCharm IDE comes with advanced code completion features, which are enhanced by AI to provide context-aware suggestions.

5. Jupyter Notebooks with AI Integration: Jupyter notebooks can integrate with various AI tools and libraries for code completion and suggestions, like JupyterLab Code Formatter or extensions that integrate with AI models.

6. DeepCode: An AI-based code review tool that helps identify and fix bugs, security vulnerabilities, and code quality issues.

7. IntelliCode: An extension for Visual Studio Code that uses AI to provide code suggestions and improve productivity.

8. Codota: An AI-powered code suggestion tool that integrates with many IDEs and provides context-aware code completions.

9. Repl.it Ghostwriter: An AI-powered code completion tool available in the Repl.it online coding environment.

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How to revolutionize Hollywood with AI.

Unlock new possibilities:

1. Voice Cloning

Clone voices of Hollywood icons:

• Legally clone and use voices with permission.
• Recreate iconic voices for new projects.
• Preserve legendary performances for future generations.

2. Custom Voices

Create unique voices for your projects:

• Generate up to 20 seconds of dialogue.
• Select from preset voice options or create your own.

3. Lip Sync Tool

Bring still characters to life:

• Use ElevenLabs's Lip Sync tool.
• Select a face and add a script.
• 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.

AI is transforming healthcare through various applications that enhance patient care, streamline operations, and improve diagnostics and treatment outcomes. Here are some key applications of AI in healthcare:

1. Medical Imaging and Diagnostics:
- Image Analysis: AI algorithms analyze medical images (X-rays, MRIs, CT scans) to detect abnormalities such as tumors, fractures, and infections.
- Disease Detection: AI systems help in early detection of diseases like cancer, diabetic retinopathy, and cardiovascular conditions.

2. Predictive Analytics:
- Patient Risk Assessment: AI models predict patient risks for conditions like sepsis, heart attacks, and hospital readmissions based on electronic health records (EHRs) and other data.
- Population Health Management: AI analyzes large datasets to identify public health trends and predict outbreaks.

3. Personalized Medicine:
- Treatment Recommendations: AI helps tailor treatment plans based on individual patient data, including genetics, lifestyle, and response to previous treatments.
- Drug Discovery: AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy and safety.

4. Virtual Health Assistants and Chatbots:
- Symptom Checking: AI-powered chatbots provide preliminary diagnosis and advice based on reported symptoms.
- Patient Engagement: Virtual assistants remind patients to take medications, schedule appointments, and follow post-treatment care plans.

5. Robotic Surgery:
- Surgical Assistance: AI-driven robots assist surgeons with precise and minimally invasive procedures, enhancing accuracy and reducing recovery times.
- Autonomous Surgery: Research is ongoing into fully autonomous surgical robots for specific procedures.

6. Administrative Workflow Automation:
- Medical Coding and Billing: AI automates coding and billing processes, reducing errors and administrative burdens.
- EHR Management: AI helps manage and update electronic health records, ensuring accurate and up-to-date patient information.

7. Clinical Decision Support Systems (CDSS):
- Decision Making: AI supports healthcare providers with evidence-based recommendations, improving diagnosis and treatment decisions.
- Error Reduction: CDSS helps reduce medical errors by cross-referencing patient data with clinical guidelines.

8. Remote Monitoring and Telehealth:
- Wearable Devices: AI analyzes data from wearable devices to monitor patient health in real-time, alerting healthcare providers to potential issues.
- Telemedicine: AI enhances telehealth platforms, providing real-time analysis and support during virtual consultations.

9. Natural Language Processing (NLP):
- Clinical Documentation: AI-powered NLP systems transcribe and analyze clinical notes, making it easier to extract relevant information.
- Voice Assistants: AI voice assistants help doctors with hands-free data entry and information retrieval during patient consultations.

10. Mental Health Support:
- Chatbots for Therapy: AI chatbots provide cognitive behavioral therapy (CBT) and other support to individuals with mental health conditions.
- Emotion Detection: AI analyzes speech and text to detect emotional states, providing insights for mental health professionals.

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How to master ChatGPT-4o....

The secret? Prompt engineering.

These 9 frameworks will help you!

APE
↳ Action, Purpose, Expectation

Action: Define the job or activity.
Purpose: Discuss the goal.
Expectation: State the desired outcome.


RACE
↳ Role, Action, Context, Expectation

Role: Specify ChatGPT's role.
Action: Detail the necessary action.
Context: Provide situational details.
Expectation: Describe the expected outcome.


COAST
↳ Context, Objective, Actions, Scenario, Task

Context: Set the stage.
Objective: Describe the goal.
Actions: Explain needed steps.
Scenario: Describe the situation.
Task: Outline the task.


TAG
↳ Task, Action, Goal

Task: Define the task.
Action: Describe the steps.
Goal: Explain the end goal.


RISE
↳ Role, Input, Steps, Expectation

Role: Specify ChatGPT's role.
Input: Provide necessary information.
Steps: Detail the steps.
Expectation: Describe the result.


TRACE
↳ Task, Request, Action, Context, Example

Task: Define the task.
Request: Describe the need.
Action: State the required action.
Context: Provide the situation.
Example: Illustrate with an example.


ERA
↳ Expectation, Role, Action

Expectation: Describe the desired result.
Role: Specify ChatGPT's role.
Action: Specify needed actions.


CARE
↳ Context, Action, Result, Example

Context: Set the stage.
Action: Describe the task.
Result: Describe the outcome.
Example: Give an illustration.


ROSES
↳ Role, Objective, Scenario, Expected Solution, Steps

Role: Specify ChatGPT's role.
Objective: State the goal or aim.
Scenario: Describe the situation.
Expected Solution: Define the outcome.
Steps: Ask for necessary actions to reach solution.


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ChatGPT Prompt to learn any skill
👇👇
I am seeking to become an expert professional in [Making ChatGPT prompts perfectly]. I would like ChatGPT to provide me with a complete course on this subject, following the principles of Pareto principle and simulating the complexity, structure, duration, and quality of the information found in a college degree program at a prestigious university. The course should cover the following aspects: Course Duration: The course should be structured as a comprehensive program, spanning a duration equivalent to a full-time college degree program, typically four years. Curriculum Structure: The curriculum should be well-organized and divided into semesters or modules, progressing from beginner to advanced levels of proficiency. Each semester/module should have a logical flow and build upon the previous knowledge. Relevant and Accurate Information: The course should provide all the necessary and up-to-date information required to master the skill or knowledge area. It should cover both theoretical concepts and practical applications. Projects and Assignments: The course should include a series of hands-on projects and assignments that allow me to apply the knowledge gained. These projects should range in complexity, starting from basic exercises and gradually advancing to more challenging real-world applications. Learning Resources: ChatGPT should share a variety of learning resources, including textbooks, research papers, online tutorials, video lectures, practice exams, and any other relevant materials that can enhance the learning experience. Expert Guidance: ChatGPT should provide expert guidance throughout the course, answering questions, providing clarifications, and offering additional insights to deepen understanding. I understand that ChatGPT's responses will be generated based on the information it has been trained on and the knowledge it has up until September 2021. However, I expect the course to be as complete and accurate as possible within these limitations. Please provide the course syllabus, including a breakdown of topics to be covered in each semester/module, recommended learning resources, and any other relevant information

(Tap on above text to copy)

Want to use ChatGPT at lightning speed?

You must tap in to ChatGPT's short cuts.

1. Go to ChatGPT
2. Bottom right '?' mark
3. Access keyboard shortcuts

Keyboard Shortcuts:

1. Show shortcuts: Ctrl + /
2. Focus chat input: Shift + Esc
3. Toggle sidebar: Ctrl + Shift + S
4. Open new chat: Ctrl + Shift + O
5. Copy last response: Ctrl + Shift + C

For example:

"Write a paper from ChatGPT's output."

1. Copy output: Ctrl + Shift + C
2. Open new chat: Ctrl + Shift + O
3. Ask it to write a paper on the info.
4. Ctrl V to paste in new information.
5. Press enter. Then paper completed.

(without ever touching your mouse)

Now THIS is ChatGPT mastery.

Move fast. Save time.

AI/ML (Daily Schedule) 👨🏻‍💻

Morning:
- 9:00 AM - 10:30 AM: ML Algorithms Practice
- 10:30 AM - 11:00 AM: Break
- 11:00 AM - 12:30 PM: AI/ML Theory Study

Lunch:
- 12:30 PM - 1:30 PM: Lunch and Rest

Afternoon:
- 1:30 PM - 3:00 PM: Project Development
- 3:00 PM - 3:30 PM: Break
- 3:30 PM - 5:00 PM: Model Training/Testing

Evening:
- 5:00 PM - 6:00 PM: Review and Debug
- 6:00 PM - 7:00 PM: Dinner and Rest

Late Evening:
- 7:00 PM - 8:00 PM: Research and Reading
- 8:00 PM - 9:00 PM: Reflect and Plan

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

ENJOY LEARNING 👍👍

Are you looking to become a machine learning engineer? The algorithm brought you to the right place! 📌

I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer:

Math & Statistics

Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.

Here are the probability units you will need to focus on:

Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra

Python:

You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.

Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking

Machine Learning Prerequisites:

Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data

Machine Learning Fundamentals

Using scikit-learn library in combination with other Python libraries for:

Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)

Solving two types of problems:
Regression
Classification

Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.

Types of Neural Networks:

Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.

In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.

Deep Learning:

Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.

Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models

Machine Learning Project Deployment

Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:

Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs

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

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