Sorry about my earlier message.

To get into neural networks, you should have a solid understanding of the following mathematical concepts:

    - Linear Algebra: Matrix operations (multiplication, transpose, determinant), eigenvalues and eigenvectors, and singular value decomposition (SVD).
    - Calculus: Derivatives, gradients, and optimization algorithms (such as gradient descent).
    - Probability and Statistics: Concepts of probability distribution, random variables, Bayes' theorem, and hypothesis testing.
    - Numerical Methods: Numerical optimization techniques such as gradient descent, numerical integration, and numerical optimization.
    - Matrix Decomposition Techniques: Techniques such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD).

Having a strong foundation in these mathematical concepts will give you the necessary tools to understand and implement various algorithms used in neural networks, such as feedforward networks, recurrent networks, and convolutional neural networks.
In addition to these mathematical concepts, it is also important to have a good understanding of computer programming and data structures. A knowledge of programming languages such as Python and an understanding of data structures such as arrays, matrices, and graphs will be extremely helpful.

Tensors are a mathematical object that can be thought of as a multi-dimensional array. They are the central data structure used in most deep learning frameworks and play a critical role in many machine learning algorithms, including neural networks.
To understand tensors, it is important to have a good foundation in linear algebra, including matrix operations (such as matrix multiplication, transposition, and inversion) and vector calculus. Familiarity with basic calculus and probability theory is also helpful.
Additionally, having a good understanding of the geometry of high-dimensional spaces and the ability to visualize multi-dimensional arrays can greatly aid in understanding and working with tensors in the context of neural networks.
It's worth noting that some advanced topics in neural network research, such as Generative Adversarial Networks (GANs), may require additional mathematical knowledge beyond linear algebra and calculus.