Math for Deep Learning: What You Need to Know to Understand Neural Networks

Price: $49.99 – $29.30
(as of Nov 26,2024 12:09:22 UTC – Details)

Publisher ‏ : ‎ No Starch Press (December 7, 2021)
Language ‏ : ‎ English
Paperback ‏ : ‎ 344 pages
ISBN-10 ‏ : ‎ 1718501900
ISBN-13 ‏ : ‎ 978-1718501904
Item Weight ‏ : ‎ 1.43 pounds
Dimensions ‏ : ‎ 7 x 0.75 x 9.25 inches

Math for Deep Learning: What You Need to Know to Understand Neural Networks

Deep learning is a powerful subset of artificial intelligence that has revolutionized many industries, from healthcare to finance to entertainment. At the core of deep learning are neural networks, complex algorithms inspired by the structure and function of the human brain. To truly understand how neural networks work, it’s essential to have a solid foundation in mathematics. Here are some key mathematical concepts you need to grasp in order to delve into the world of deep learning:

1. Linear Algebra: Linear algebra is the foundation of many deep learning algorithms. You need to understand concepts such as matrices, vectors, matrix multiplication, and matrix inversion. These concepts are crucial for processing and manipulating the data that flows through a neural network.

2. Calculus: Calculus is used to optimize neural networks by minimizing a loss function. You should be familiar with derivatives and gradients, which are used to update the weights and biases of a neural network during the training process.

3. Probability and Statistics: Probability theory and statistics play a key role in deep learning, especially in areas such as Bayesian inference and probabilistic graphical models. You need to understand concepts like probability distributions, expectation, variance, and statistical hypothesis testing.

4. Optimization: Optimization algorithms are used to train neural networks by minimizing a loss function. You should be familiar with algorithms such as stochastic gradient descent, Adam, and RMSprop, as well as techniques like momentum and learning rate scheduling.

5. Convex Optimization: Convex optimization is used to find the optimal solution to many deep learning problems. You need to understand concepts like convex sets, convex functions, and convex optimization algorithms.

By mastering these mathematical concepts, you’ll be better equipped to understand the inner workings of neural networks and tackle complex deep learning problems. With a strong mathematical foundation, you’ll be able to push the boundaries of what is possible with deep learning and make significant contributions to the field.
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