GGSIPU > DU, no further questions

MIT's "Advanced Topics in Cryptography" Youtube Playlist: piped.video/playlist?list=PL…

Part 1: The Basics Part 2: Graph Algorithms and Data Structures Part 3: Greedy Algorithms and Dynamic Programming Part 4: Algorithms for NP-Hard Problems Check book page for TOC, videos, and more! algorithmsilluminated.org/

Test Linux and DevOps troubleshooting skills on real servers

GitHub - imthenachoman/How-To-Secure-A-Linux-Server: An evolving how-to guide for securing a Linux server. github.com/imthenachoman/How…

writing C in 2025? this is the book. > this book covers the new C23 standard and teaches how to write safe, fast, and modern C code that actually scales. > perfect for embedded devs, systems engineers, and anyone who wants to master the language that built everything

To finally book aa gyi h market me. Live stream me giveaway ka program bnaya ja rha h. Jo afford kr skte h, please support by buying it. If you are not going to show love to value providers, to kaise chalega ji. Baaki intezar kro stream ka, kuch msst la rhe. cc @techie_piyush

When talking about data structures, heaps never get the credit they deserve. Most of the attention is on arrays, linked lists, and hash tables. But many critical algorithms use heaps. Here are some examples: 1. Finding the K Largest Elements. Let's say you have a massive dataset with millions of elements, and you only need the top 100. You could sort the entire dataset and pick out the last k items. But this requires O(n log n) comparisons. Using a heap structure, you can solve the problem with only O(n + k*log k) comparisons and swaps. This is a significant improvement if k is much smaller than n, which is typically the case. 2. Dijkstra is a popular algorithm that finds the fastest path between two vertices in a weighted graph. The algorithm must repeatedly find the vertex with the shortest distance and also keep track of new distances for other vertices. A heap is efficient at both tasks: it finds the minimum in O(log V) time and updates the distances in O(log V) time as well, where V is the number of vertices. Prim's algorithm that finds a graph's minimum spanning tree uses heaps in a similar way. 3. Huffman coding is a greedy algorithm that encodes a text by assigning shorter bit sequences to characters that appear more often. The algorithm does multiple iterations where it needs to quickly find the two elements with the smallest frequencies and add a new combined element back to the collection. This is a perfect job for a min-heap. You can read more about the heap data structure and its real-world use cases in the latest issue of the Polymathic Engineer.

A great resource that has helped me a lot was when @MartinShkreli used to livestream finance and investing lessons on YouTube. I would watch these streams in college, following along learning how to build financial models. Pretty incredible how this is 100% free! Same with @AswathDamodaran YouTube channel, many of his classes at NYU are uploaded there for free. You can even take his final exams! I would binge watch these and often go back and review once or twice a year. I have no formal degree in finance, I went to a CUNY college for Criminal Justice and Economics and build a career mostly in technology/software sales. Everything I know has essentially been self taught from reading the following books 📕 📍HTMMIS 📍 Reminiscences of a Stock Operator 📍 Common Stocks and Uncommon Profits 📍Margin of Saftey In addition, cold emailing and DM’ing HF managers and sharing my own work since college. I’ve learned if you reach out to people and provide value they will help you in whatever you are doing. Thank you to everyone who has helped me out over the years, especially @Upticken @mwebster1971 and even the much younger up and coming money managers that I’ve learned a lot from n like @TedHZhang The internet is an amazing place if you just apply yourself and use it to your advantage. I will share the links below 👇🏻 to what I believe to be Invaluable information. Hope these resources help you as much as they have helped me.

ETH's "Advanced Algorithms" by Mohsen Ghaffari PDF: people.inf.ethz.ch/aroeyskoe…

Anti virus program in C piped.video/Y-deZnSwD1M JB #cybersecurity #cprogramming

Oh look, a new book by @ericlippert ! Should be a fabulous adventure indeed. manning.com/books/fabulous-a…

Machine Learning Systems. Principles and Practices of Engineering Artificially Intelligent Systems mlsysbook.ai/

Harvard just dropped a book on ~ML systems engineering~ and it's 100% free in PDF (it will be published in print by MIT Press). systems engineering is the hot skill companies die to see in candidates, but few books explain it in the context of ML. What it covers: → ML system foundations → a primer on DL → DNN architectures ⃗→ design principles and AI workflows ⃗→ data engineering → AI training (distributed, mathematics, etc.) ⃗→ performance engineering and model optimization ⃗→ AGI systems and what to expect!!! ⃗→ how to work with SLMs and VLMs the subjects cover 100% of what you need to know as a mid-level ML engineer, and the best part is that it is updated to cover what matters in 2025. get the book here: mlsysbook.ai/

Learn assembly by doing what you always wanted as a kid: playing with your Game Boy. Available now at major retailers. nostarch.com/game-boy-coding…

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I love discussing Binary Search. From my 5 years of experience taking multiple interviews at big tech, it is an underrated topic to gauge deeper algorithms understanding. Most people fail in understanding that one can search for an answer in a problem that seems to have nothing to do with search. 🧵

Get closer to the system. From processes to memory, understand how your code interacts with Linux itself. Available now at major retailers. bit.ly/47ssZo0