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Basics of NumPy: a powerful array instead of lists
NumPy is a fundamental library for scientific computing in Python. Its core is the
Briefly about the essence:
NumPy arrays store data of one type in a continuous block of memory. This allows operations to be performed on entire arrays without loops (vectorization), which gives a huge speed boost.
Where it's really useful:
An important nuance:
Example in Python:
β‘οΈ Comparison of approaches:
When to use:
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NumPy is a fundamental library for scientific computing in Python. Its core is the
ndarray (N-dimensional array), which works much faster than ordinary lists.Briefly about the essence:
NumPy arrays store data of one type in a continuous block of memory. This allows operations to be performed on entire arrays without loops (vectorization), which gives a huge speed boost.
Where it's really useful:
π΅ Mathematical and statistical calculationsπ΅ Processing large amounts of dataπ΅ Machine learning and computer visionπ΅ Scientific modeling and engineering
An important nuance:
import numpy as np
arr = np.array([1, 2, 3]) # Creating an array
arr * 2 # Vectorization: [2, 4, 6]
list = [1, 2, 3]
list * 2 # Repetition: [1, 2, 3, 1, 2, 3]
Example in Python:
# Fast operations on the entire array
import numpy as np
# Creating an array
data = np.array([1, 4, 9, 16, 25])
# Vectorized operations
sqrt_data = np.sqrt(data) # Square root of each element
mean_value = np.mean(data) # Average value
filtered = data[data > 10] # Filtering (boolean indexing)
print(sqrt_data) # [1. 2. 3. 4. 5.]
print(mean_value) # 11.0
print(filtered) # [16 25]
π’ NumPy array vs Python list:π’ NumPy - operations on the entire array at once (vectorization)π’ List - cycles are required for element-by-element operations
η»Ώθ² NumPy - efficient storage in memory
η»Ώθ² List - stores references to objects
η»Ώθ² NumPy - built-in mathematics (linear algebra, statistics)
η»Ώθ² List - basic functionality
When to use:
π΄ Working with numerical dataπ΄ High performance is requiredπ΄ Mathematics is needed: matrices, statistics, algebraπ΄ Processing images or audioπ΄ Integration with ML libraries (SciPy, Pandas, TensorFlow)
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This repository collects everything you need to use AI and LLM in your projects.
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Repo: https://github.com/KalyanKS-NLP/llm-engineer-toolkit
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120+ libraries, organized by development stages:
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β Deploying applications with LLM and RAG
β Fast and scalable model launch
β Data extraction, crawlers, and scrapers
β Creating autonomous LLM agents
β Prompt optimization and security
Repo: https://github.com/KalyanKS-NLP/llm-engineer-toolkit
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These Google Colab-notebooks help to implement all machine learning algorithms from scratch π€―
Repo: https://udlbook.github.io/udlbook/
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I. Core Functions: Fully Automated "Lead Generation - Interaction - Conversion"
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High Conversion Rate Across Multiple Scenarios: Ant AI intelligently recommends high-conversion-rate products based on chat content, guiding customers to complete purchases through platforms such as iFood, Shopee, and Amazon. It also supports other transaction scenarios such as movie ticket purchases and utility bill payments.
24/7 Operation: Ant AI continuously searches for customers and recommends products. You only need to monitor progress via your mobile phone, requiring no additional management time.
II. Your Profit Guarantee: Low Risk, High Transparency, Zero Inventory Pressure, Stable Commission Sharing
We have established partnerships with platforms such as Shopee and Amazon, which directly provide abundant product sourcing. You don't need to worry about inventory or logistics. After each successful order, the company will charge the merchant a commission and share all profits with you. Earnings are predictable and withdrawals are convenient. Member data shows that each bot can generate $30 to $100 in profit per day. Commission income can be withdrawn to your account at any time, and the settlement process is transparent and open.
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