TYPES OF INTELLIGENCE

4 types of Intelligence:

1) Intelligence Quotient (IQ)
2) Emotional Quotient (EQ)
3) Social Quotient (SQ)
4) Adversity Quotient (AQ)

1. Intelligence Quotient (IQ): this is the measure of your level of comprehension. You need IQ to solve maths, memorize things, and recall lessons.

2. Emotional Quotient (EQ): this is the measure of your ability to maintain peace with others, keep to time, be responsible, be honest, respect boundaries, be humble, genuine and considerate.
3. Social Quotient (SQ): this is the measure of your ability to build a network of friends and maintain it over a long period of time.

People that have higher EQ and SQ tend to go further in life than those with a high IQ but low EQ and SQ. Most schools capitalize on improving IQ levels while EQ and SQ are played down.

Develop their IQ, as well as their EQ, SQ and AQ. They should become multifaceted human beings able to do things independently of their parents.
4. The Adversity Quotient (AQ): The measure of your ability to go through a rough patch in life, and come out of it without losing your mind.

When faced with troubles, AQ determines who will give up, who will abandon their family, and who will consider suicide.

Parents please expose your children to other areas of life than just Academics. They should adore manual labour (never use work as a form of punishment), Sports and Arts.

Data Science vs Data Engineering vs AI Explained in a song 😂👇
https://youtu.be/WQOzBawrTsQ?si=8wVYA3Me_SGM2GDs

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

Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:

1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.

Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.

Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.

2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.

These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.

Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.

3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.

Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.

4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.

LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.

5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.

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

ChatGPT is a profit powerhouse for designers.

10 ChatGPT / ClaudeAI prompts that help you make 6000$ / month.

( Start selling in 7 days or less )
⬇👇
ChatGPT Prompt Master

AI as a life saver:
1. ChatGPT - thesis, essay, writing
2. Scite and perplexity - literature review
3. Consesus - latest research paper
4. Gemini - coding and technical
5. Claude AI - Analysis data, comparison data, literature review

Here's a simple but powerful test to see the intelligence of an AI model. (The answer is the strawberry is still on the table)

Go ahead and ask any model this:

Assume the laws of physics on Earth. A small
strawberry is put into a normal cup and the cup is
placed upside down on a table. Someone then takes
the cup and puts it inside the microwave. Where is the
strawberry now? Explain your reasoning step by step.

Free ML crash course by Google
👇👇
https://developers.google.com/machine-learning/crash-course/

You can use ChatGPT to make money online.

Here are 10 prompts by ChatGPT

1. Develop Email Newsletters:

Make interesting email newsletters to keep audience updated and engaged.

Prompt: "I run a local community news website. Can you help me create a weekly email newsletter that highlights key local events, stories, and updates in a compelling way?"

2. Create Online Course Material:

Make detailed and educational online course content.

Prompt: "I'm creating an online course about basic programming for beginners. Can you help me generate a syllabus and detailed lesson plans that cover fundamental concepts in an easy-to-understand manner?"

Read more......

Build an LLM app with Mixture of AI Agents using small Open Source LLMs that can beat GPT-4o in just 40 lines of Python Code (step-by-step instructions):

⬇️

1. Install the necessary Python Libraries

Run the following commands from your terminal to install the required libraries:

2. Import necessary libraries

• Streamlit for the web interface
• asyncio for asynchronous operations
• Together AI for LLM interactions

3. Set up the Streamlit app and API key input.

• Creates a title for the app
• Adds a secure input field for the Together API key

4. Initialize Together AI clients.

• Sets up Together API key as an environment variable
• Initializes both synchronous and asynchronous Together clients

5. Define the models and aggregator system prompt.

• Specifies the LLMs to be used for generating responses
• Defines the aggregator model and its system prompt

6. Implement the LLM call function.

• Asynchronously calls the LLM with the user's prompt
• Returns the model name and its response

7. Define the main function to run all LLMs and aggregate results.

• Runs all reference models asynchronously
• Displays individual responses in expandable sections
• Aggregates responses using the aggregator model
• Streams the aggregated response.

8. Set up the user interface and trigger the main function.

• Provides an input field for the user's question
• Triggers the main function when the user clicks "Get Answer"