The backbone of classic game-playing AI (like chess or checkers). It teaches you how an agent simulates future turns to maximize its chances of winning while assuming the opponent will play optimally.
The publisher’s website offers the PDF, ePUB, and Kindle versions directly.
A repository exists for Bhargava's book as well at github.com/egonschiele/grokking_algorithms , with community summaries and PDF notes available from various contributors.
For "Grokking Artificial Intelligence Algorithms" by Rishal Hurbans, the primary resources available on GitHub include the and an interactive notebook , while the full book text is generally a commercial product. Official GitHub Resources grokking artificial intelligence algorithms pdf github
: Build a 3-layer neural network from scratch that solves the XOR problem.
Most examples are written in Python due to its dominance in AI.
The examples are predominantly written in Java and Python , making it accessible to a wide audience. The backbone of classic game-playing AI (like chess
For the best experience and to support the author's work, purchasing through Manning or an authorized retailer is strongly recommended.
When a model fails or produces biased results, a developer who doesn't understand the underlying algorithm cannot easily pinpoint the issue.
To truly "grok" AI, one must master several foundational categories of algorithms: 1. Search and Optimization A repository exists for Bhargava's book as well at github
The final chapters demystify the technology powering modern breakthroughs like computer vision and large language models.
Key praise points include the book's ability to teach the .
Calculate the derivative of the loss function with respect to the weights and biases (using the calculus chain rule) and subtract a fraction of that gradient (the learning rate) to optimize your parameters.