Deep Learning

Deep learning is a subset of machine learning that aims to simulate how the human brain works by creating artificial neural networks capable of learning from large amounts of data. It involves training algorithms known as artificial neural networks, composed of many layers of interconnected nodes (neurons). These networks can automatically learn to recognize patterns, extract features, and make predictions or decisions directly from raw data.

This competency area includes an understanding of the concepts of programming languages, linear algebra & calculus, probability & statistics, deep learning frameworks, deep learning algorithms, data management and preprocessing, model evaluation and optimization.

Key Competencies

1. Programming Languages -  Familiarity with Python, Java, or C++.
2. Linear Algebra & Calculus - Understanding matrices, vectors, derivatives, and gradients is essential for working with the underlying algorithms in deep learning.
3. Probability & Statistics - Understanding of probability concepts like Bayes' theorem and statistical methods for data analysis for evaluating and improving deep learning models.
4. Deep Learning Frameworks - Understanding of TensorFlow and PyTorch for building, training, and deploying deep learning models.
5. Deep Learning Algorithms - Understanding different neural network architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for various deep learning tasks like image recognition and natural language processing.
6. Data Management & Preprocessing - Understanding of data acquisition, cleaning, manipulation, and preprocessing.
7. Model Evaluation & Optimization - Ability to assess a model's performance using metrics like accuracy and precision, and techniques for hyperparameter tuning to optimize model performance.