Top Python libraries for generative AI

Generative AI is a branch of artificial intelligence that focuses on the creation of new content, such as text, images, music, and code. This is done by training models on large datasets of existing content, which the model then uses to generate new content.
Python is a popular programming language for generative AI, as it has a wide range of libraries and frameworks available.

Programming Practice Python

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To automate your daily tasks using ChatGPT, you can follow these steps:

1. Identify Repetitive Tasks: Make a list of tasks that you perform regularly and that can potentially be automated.

2. Create ChatGPT Scripts: Use ChatGPT to create scripts or workflows for automating these tasks. You can use the API to interact with ChatGPT programmatically.

3. Integrate with Other Tools: Integrate ChatGPT with other tools and services that you use to streamline your workflow. For example, you can connect ChatGPT with task management tools, calendar apps, or communication platforms.

4. Set up Triggers: Set up triggers that will initiate the automated tasks based on certain conditions or events. This could be a specific time of day, a keyword in a message, or any other criteria you define.

5. Test and Iterate: Test your automated workflows to ensure they work as expected. Make adjustments as needed to improve efficiency and accuracy.

6. Monitor Performance: Keep an eye on how well your automated tasks are performing and make adjustments as necessary to optimize their efficiency.

Several future trends in artificial intelligence (AI) are expected to significantly impact the current job market. Here are some key trends to consider:

1. AI Automation and Robotics: AI-driven automation and robotics are likely to replace certain repetitive and routine tasks across various industries. This can lead to a shift in the types of jobs available and the skills required for the workforce.

2. Augmented Intelligence: Rather than fully replacing human workers, AI is expected to augment human capabilities in many roles, leading to the creation of new types of jobs that require a combination of human and AI skills.

3. AI in Healthcare: The healthcare industry is likely to see significant changes due to AI, with the potential for improved diagnostics, personalized treatment plans, and more efficient healthcare delivery. This could create new opportunities for healthcare professionals with AI expertise.

4. AI in Customer Service: AI-powered chatbots and virtual assistants are already transforming customer service, and this trend is expected to continue. Jobs in customer service may evolve to focus more on complex problem-solving and emotional intelligence, as routine tasks are automated.

5. Data Science and AI: The demand for data scientists, machine learning engineers, and AI specialists is expected to grow as organizations seek to leverage AI for data analysis, predictive modeling, and decision-making.

6. AI Ethics and Governance: As AI becomes more pervasive, there will be an increased need for professionals specializing in AI ethics, governance, and regulation to ensure responsible and ethical use of AI technologies.

7. Reskilling and Upskilling: With the evolving nature of jobs due to AI, there will be a growing need for reskilling and upskilling programs to help workers adapt to new technologies and roles.

8. Cybersecurity and AI: As AI systems become more integrated into critical infrastructure and business operations, there will be a growing demand for cybersecurity professionals with expertise in AI-based threat detection and defense.

Overall, the rise of AI is expected to bring both challenges and opportunities to the job market, requiring individuals and organizations to adapt to the changing landscape of work and skills.

AI is the next biggest skill to learn.

AI experts are earing up to $200000+ per year.

Here are 4 FREE courses from Google and Microsoft that most people don't know:

https://microsoft.github.io/AI-For-Beginners/?

https://www.cloudskillsboost.google/paths/118

https://www.deeplearning.ai/courses/ai-for-everyone/

https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/

More free resources: https://t.me/udacityfreecourse

Future Trends in Artificial Intelligence 👇👇

1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.

2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.

3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.

4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.

5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.

6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.

7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.

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Artificial Intelligence

The first channel in the world of Telegram is dedicated to helping students and programmers of artificial intelligence, machine learning and data science in obtaining data sets for their research.

https://t.me/DataPortfolio

How do you start AI and ML ?

Where do you go to learn these skills? What courses are the best?

There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.

What’s more important than how you start is why you start.

Start with why.

Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.

Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.

Got a why? Good. Time for some hard skills.

I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course

You can completed courses from (in order):

Treehouse / youtube( free) - Introduction to Python

Udacity - Deep Learning & AI Nanodegree

fast.ai - Part 1and Part 2

They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.

If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.

Join for more: https://t.me/machinelearning_deeplearning

👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5

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All the best 👍👍

📚 Machine Learning Mastery with Python
Jason Brownlee, 2016

Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that they can go:

1. Computer vision
2. Natural language processing.

I outlined a roadmap for computer vision I believe many beginners will find helpful.

Artificial Intelligence 👇👇

Before we start, what is computer vision and what do computer vision engineers do?

Computer vision is a field of AI that enables machines to interpret and understand visual data from the world, such as images and videos.

Computer vision engineers develop algorithms and systems to automate tasks like image classification, object detection, and image segmentation, transforming visual data into actionable insights for various applications including healthcare, autonomous driving, and security.

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Introduction to Computer Vision:

Understanding images and pixels.
Grayscale and color images.
Basic image processing operations.
Image formats and conversions.

Mathematics for Computer Vision:
Linear algebra (matrices, vectors, transformations).
Calculus (derivatives, gradients).
Probability and statistics (distributions, Bayes theorem).
Fourier transforms and convolutions.
Eigenvalues and eigenvectors.

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Step 2: Basic Image Processing

Image Manipulation with OpenCV:
Reading, displaying, and saving images.
Basic operations (resizing, cropping, rotating).
Image filtering (blurring, sharpening, edge detection).
Handling image channels and color spaces.

Image Manipulation with PIL and Scikit-Image:
Image enhancement techniques.
Histogram equalization.
Geometric transformations.
Image segmentation (thresholding, watershed).

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Step 3: Feature Extraction

Traditional Feature Detectors:
Edge detection (Sobel, Canny).
Corner detection (Harris, Shi-Tomasi).
Blob detection (LoG, DoG).
SIFT and SURF features.
ORB features.

Image Segmentation:
Thresholding.
Watershed algorithm.
Contours and shape detection.
Region growing.
Graph-based segmentation.

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Step 4: Machine Learning for Computer Vision

Classical Machine Learning Techniques:
K-Nearest Neighbors (KNN).
Support Vector Machines (SVM).
Decision Trees and Random Forests.
Naive Bayes.
Clustering (K-means, DBSCAN).
Dimensionality Reduction:
Principal Component Analysis (PCA).
Linear Discriminant Analysis (LDA).
t-SNE (t-Distributed Stochastic Neighbor Embedding).
Independent Component Analysis (ICA).
Feature selection techniques.

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Step 5: Deep Learning for Computer Vision

Convolutional Neural Networks (CNNs):
Convolutional layers.
Pooling layers.
Fully connected layers.
Activation functions (ReLU, Sigmoid, Tanh).
Batch normalization and dropout.
Advanced CNN Architectures:
AlexNet and VGGNet.
ResNet (Residual Networks).
Inception and GoogLeNet.
DenseNet (Densely Connected Networks).
MobileNet and EfficientNet.

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Step 6: Advanced Topics in Computer Vision

Object Detection:

Region-based methods (R-CNN, Fast R-CNN, Faster R-CNN).
YOLO (You Only Look Once).
SSD (Single Shot MultiBox Detector).
RetinaNet.
Anchor boxes and non-maximum suppression.
Image Segmentation:

Semantic segmentation (U-Net, SegNet).
Instance segmentation (Mask R-CNN).
Panoptic segmentation.
Fully Convolutional Networks (FCNs).
CRFs (Conditional Random Fields).

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Artificial Intelligence for Learning
Donald Clark, 2024

Real-World Natural Language Processing
Masato Hagiwara, 2021

Step 7: Generative Models

Variational Autoencoders (VAEs):

Encoder and decoder networks.
Latent space representation.
Reparameterization trick.
KL divergence loss.
Applications (data generation, anomaly detection).

Generative Adversarial Networks (GANs):

Generator and discriminator networks.
Adversarial training.
Loss functions (minimax, Wasserstein).
DCGANs (Deep Convolutional GANs).
Applications (image generation, style transfer).

Artificial Intelligence