- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
A
C
D
G
L
M
N
P
R
S
T
X
- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
AI Engineer
What is an AI Engineer?
An artificial intelligence engineer is a person who uses traditional machine learning techniques, such as natural language processing and neural networks, to build models that power AI-based applications.
Why are AI Engineers important?
AI engineers help address the complex design challenges that result from combining the logic found in traditional applications with the learned logic from machine learning models.
How does someone become an AI Engineer?
A certification course in Data Science, Machine Learning, or Artificial Intelligence is highly recommended for those interested in becoming AI engineers. With these certifications, you will be able to enhance your resume and gain in-depth knowledge of AI topics, as well as increase your pay.
AI Engineers vs Other Careers
AI Engineers VS Data Scientists
The main difference between data science and artificial intelligence (AI) is that data science is a broad discipline that includes the study of AI. AI is just one aspect of data science.
AI Engineers VS Software Engineers
Programmers create mobile apps, operating systems, websites, and proprietary software for businesses and companies. AI engineers use traditional machine learning techniques and tricks, such as neural networks and natural language processing to implement and build models that run and require AI-based apps. In contrast, software engineers are responsible for maintaining and creating end-user systems. They are involved in both the hardware and software development process.
AI Engineers VS Machine Learning Engineers
AI engineers have a big-picture view of an organization's AI operations. They are in charge of the AI platform, which provides services for customers while evolving the product according to the company's changing requirements.
However, machine learning engineers are expected to be subject matter experts in their sub-field, devise AI solutions, and apply the right solution to a problem. It's a more generalist approach that requires knowledge of several fields in AI.