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AI Concepts for Software Engineers

1. Machine Learning

Machine Learning (ML) is the study of algorithms that improve automatically through experience. Core components include:

Example: Linear Regression

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)

2. Deep Learning

Deep Learning is a subset of ML that uses neural networks with multiple layers (deep architectures) to model complex representations.

Example: Simple Deep Neural Network with Keras

from keras.models import Sequential
from keras.layers import Dense

model = Sequential([
    Dense(64, activation='relu', input_shape=(input_dim,)),
    Dense(64, activation='relu'),
    Dense(1)
])
model.compile(optimizer='adam', loss='mse')
model.fit(X_train, y_train, epochs=10)

3. Neural Networks

Artificial Neural Networks (ANNs) consist of layers of neurons that transform input data using weights and activation functions.

Example: Manual forward pass

import numpy as np

def relu(x):
    return np.maximum(0, x)

def forward_pass(x, weights):
    return relu(np.dot(x, weights))

4. Natural Language Processing (NLP)

NLP focuses on enabling machines to understand and generate human language.

Example: Tokenizing and stemming

from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer

tokens = word_tokenize("ChatGPT is amazing!")
stems = [PorterStemmer().stem(word) for word in tokens]

5. Computer Vision

Computer Vision (CV) enables machines to interpret and process visual data like images and video.

Example: Image classification with OpenCV and CNN

import cv2
image = cv2.imread('cat.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

6. Reinforcement Learning

RL involves agents learning optimal behavior through rewards and penalties by interacting with environments.

Example: Q-Learning algorithm sketch

Q[state, action] = reward + gamma * max(Q[next_state])

7. Generative Models

Generative models learn data distributions and can create new, similar data.

Example: GAN architecture outline

# Generator and Discriminator models are trained in a loop
# Generator tries to fool the Discriminator

8. Large Language Models (LLMs)

LLMs are deep neural networks trained on massive text corpora to generate human-like language.

Example: Generate text with HuggingFace Transformers

from transformers import pipeline
generator = pipeline("text-generation", model="gpt2")
print(generator("Once upon a time", max_length=30))

9. Transformers

Transformers use self-attention to weigh importance of input tokens, allowing for parallel processing.

Example: Self-attention mechanism

Attention(Q, K, V) = softmax(QK^T / sqrt(d_k)) * V

10. Feature Engineering

The process of selecting, transforming, and creating variables that help ML models perform better.

Example: Feature scaling

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

11. Supervised Learning

Training models using labeled data to learn input-output mappings.

Example: Classification with Decision Tree

from sklearn.tree import DecisionTreeClassifier

clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)

12. Bayesian Learning

Incorporates probability distributions and uncertainty into modeling.

Example: Naive Bayes classifier

from sklearn.naive_bayes import GaussianNB

model = GaussianNB()
model.fit(X_train, y_train)

13. Prompt Engineering

Designing effective inputs (prompts) to guide generative AI models.

Example: Prompt template

prompt = "Translate the following English text to French: '{}'"

14. AI Agents

Autonomous software systems that perceive environments, make decisions, and act.

Example: Simple rule-based agent

def agent(state):
    if state == "hungry":
        return "eat"
    return "wait"

15. Fine-Tuning Models

Customizing a pre-trained model on domain-specific data to improve accuracy.

Example: Fine-tuning BERT for classification

from transformers import BertForSequenceClassification

model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)

16. Multimodal Models

Models that process multiple data types simultaneously (e.g., text + image).

Example: Multimodal input sketch

# Inputs: image and caption
# Output: relevant tag or response

17. Embeddings

Dense vector representations of data (text, image, audio) for ML models.

Example: Sentence embedding

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')
embedding = model.encode("AI is transforming software engineering.")

18. Vector Search

Search using vector embeddings to find semantically similar items.

Example: FAISS search

import faiss
index = faiss.IndexFlatL2(embedding_dim)
index.add(vectors)
D, I = index.search(query_vector, k)

19. Model Evaluation

Assess model performance using metrics and validation techniques.

Example: Evaluation metrics

from sklearn.metrics import accuracy_score, classification_report

print(accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))

20. AI Infrastructure

Systems and tools that support development, deployment, and scaling of AI models.

Example: Model deployment with Flask

from flask import Flask, request
app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.json
    prediction = model.predict([data['features']])
    return {'prediction': prediction.tolist()}
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