AI In a Nutshell: Your Quick Cheat Sheet

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Generative AI

• Text generation
• Language modeling
• Summarization

Deep Learning

• CNN (Convolutional Neural Networks)
• RNN (Recurrent Neural Networks)
• DBN (Deep Belief Networks)
• GAN (Generative Adversarial Networks)

Machine Learning

• Decision Trees
• K-Nearest Neighbor
• Support Vector Machine
• Principal Component Analysis
• Random Forest
• K-Means
• Linear Regression

Neural Network

• GRU (Gated Recurrent Unit)
• MLP (Multi-Layer Perceptron)
• Perceptrons
• LSTM (Long Short-Term Memory)
• Boltzmann Neural Networks

Artificial Intelligence

• Automatic programming
• Knowledge representation
• Intelligent robotics
• Natural Language Processing

Features of AI

• Machine Learning—Systems improve through experience
• NLP—Machines understand and create human language
• Computer Vision—Analyzes and interprets images
• Robotics—Autonomous machine operations
• Deep Learning—Complex pattern recognition via neural nets

Top Use Cases

• Healthcare: AI assists in diagnosis and custom treatment
• Finance: Powers fraud detection and trading
• Retail: Suggests products, manages stock
• Manufacturing: Predicts maintenance needs
• Automotive: Enables self-driving technologies

Popular Frameworks

• PyTorch
• Keras
• Scikit-Learn
• TensorFlow
• Apache MXNet

You Should Know:

Hands-on AI Commands & Code Snippets

1. Running GPT-4o via OpenAI API (Python)

import openai

response = openai.ChatCompletion.create( 
model="gpt-4", 
messages=[{"role": "user", "content": "Explain neural networks in simple terms."}] 
) 
print(response.choices[bash].message.content) 

2. Training a CNN in TensorFlow

import tensorflow as tf 
from tensorflow.keras import layers

model = tf.keras.Sequential([ 
layers.Conv2D(32, (3,3), activation='relu', input_shape=(28, 28, 1)), 
layers.MaxPooling2D(), 
layers.Flatten(), 
layers.Dense(10, activation='softmax') 
]) 
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy') 
model.fit(train_images, train_labels, epochs=5) 

3. Linux Command for AI Model Serving

 Start a FastAPI AI server 
uvicorn main:app --reload --host 0.0.0.0 --port 8000 

4. Windows PowerShell for AI Automation

 Install Python AI packages 
pip install torch tensorflow scikit-learn 

5. Dockerizing an AI Model

FROM python:3.9 
COPY . /app 
WORKDIR /app 
RUN pip install -r requirements.txt 
CMD ["python", "app.py"] 

6. Federated Learning Setup (Privacy-Preserving AI)

 Run a federated learning node 
python -m flwr.server --rounds=10 --sample_fraction=0.5 

What Undercode Say

AI is rapidly evolving, and mastering frameworks like PyTorch and TensorFlow is essential. Automation through scripting (Bash/Python) and deploying models via Docker ensures scalability. Federated learning is the next big shift for privacy-aware AI.

Expected Output:

• A functional AI model API endpoint (`http://localhost:8000/docs`).
• Trained CNN model (model.h5).
• Docker container running AI service.

Prediction

By 2026, AI will automate 40% of repetitive tasks in IT, healthcare, and finance, with federated learning becoming standard for data privacy.

IT/Security Reporter URL:

Reported By: Vishnunallani Ai – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

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