Linear Algebra in Deep Learning

- Linear Algebra in Deep Learning

Linear algebra is a fundamental part of deep learning. It is a continuous mathematical form used to represent data and perform operations on it to train deep networks. Linear algebra is used in many industries, including: Statistics, chemical physics, genomics, word embedding, robotics, image processing, quantum physics.

Linear algebra is also used to describe deep learning methods using matrix notation. For example, Google's TensorFlow Python library uses linear algebra.

Linear algebra is important for understanding the theory behind machine learning, especially deep learning. It gives you a better understanding of how algorithms work, helping you make better decisions. 

However, if you just want to know how to use the algorithm, you only need a basic knowledge of linear algebra. If you want to understand how things work behind the scenes, you'll need a broader background in algebra and calculus.

- Linear Algebra in Neural Networks

Neural networks are mathematical models that combine linear algebra, biology, and statistics to solve problems. They take inputs and calculate outputs to achieve actual results. 

Linear algebra objects, such as matrices and vectors, are used to represent the inputs, outputs, and weights of neural networks. These networks are based on a series of matrix operations such as dot products, matrix-vector multiplication, and matrix inversion.

Linear algebra concepts are essential for analyzing neural networks. They include:

• Projection of vector
• Eigenvalues and singular value decomposition

Linear algebra is also used in other applications such as:

• Sentiment analysis of social media posts
• Detecting lung infections from X-ray images
• Speech to text bot

[More to come ...]