TensorTonic

Part 2/30 of the LLM Series: RoPE (Rotary Position Embedding) How does a transformer know the difference between - "the dog bit the man" and "the man bit the dog"? The words are almost identical, but the meaning changes completely. RoPE encodes position as rotation, allowing transformers to understand relative order through geometry. Read more: tensortonic.com/llm-internals

@Spectator_seekr We don't offer courses.

@TensorTonic Do you have hand ons course in LLM fine tuning covering methods mentioned above?

We're starting a new series of LLM internals which will be interactive, cool visualizations into the techniques behind modern LLMs: attention variants, Flashattention, LoRA, RLHF, quantization and more. We just released our first concept: KV caching in LLMs. Go check out: tensortonic.com/llm-internals/…

@dprophecyguy Noted, will improve on the upcoming ones.

@TensorTonic the language can use some improvement, the writing quality is abysmal.

100-day streak unlocked. 🔓at @TensorTonic Slowly but surely climbing the leaderboard (Currently at Rank 73). @TensorTonic Profile - tensortonic.com/profile/valarm…

While its good to implement GPT2, LLaMA, DeepSeek but you should be able to implement classic ML algorithms as well such as linear regression, KNN, decision trees, random forests, PCA, K-means, Naive Bayes, and ridge regression from scratch. These are the algorithms that show up in every single ML interview. We shipped a full Classic ML track on TensorTonic with interactive visualizations where you can actually see your linear regression fitting in real time, watch KNN voting with draggable test points, and visualize PCA finding the direction of maximum spread. You implement the math, we show you what it looks like. Code your first problem - tensortonic.com

GRPO, DPO, PPO, RLHF are the algorithms behind every major LLM alignment pipeline and if you really want to understand how a base model becomes ChatGPT, you need to implement them yourself. We just shipped a full RL track on TensorTonic covering all of it. RLHF with KL penalties, DPO, GRPO, RLOO, PPO clipped surrogate, Actor-Critic, REINFORCE, GAE, all the way down to the fundamentals like DQN variants, Q-Learning, SARSA, Monte Carlo methods, and multi-armed bandits. tensortonic.com

Every ML framework hides optimizers behind one line of code and nobody questions what’s actually happening when you call optimizer.step(). We have coding questions where you implement every optimization algorithm from scratch, starting from vanilla gradient descent all the way to AdamW. SGD, momentum, Nesterov, Adagrad, RMSprop, Adam, and AdamW, each one building on the last so you actually understand why Adam exists and what the W in AdamW fixes. tensortonic.com

New updates - 🚨 You can now bookmark any problem to revisit later ⚡️ More light themes on code editor 🔥 You can now share your profile URL with recruiters 🥳 Badges are now shareable on social media 📱 Cleaner experience on mobile across problems tensortonic.com

NumPy is the backbone of every ML pipeline. Linear algebra for neural networks, matrix decompositions for PCA, vectorized gradient for backprop, feature engineering on tabular data. We've released 25 problems on array creation, slicing, indexing, broadcasting across mismatched dimensions, and replacing slow Python loops with vectorized ops. The stuff that separates people who use NumPy from people who actually think in NumPy. tensortonic.com

Stop reading. Start building @TensorTonic is pure fire 🔥 Built for developers who prefer writing real code over just reading theory. Practical. Focused. Implementation-first Code > Theory: tensortonic.com #AI #MachineLearning #DeepLearning #Python #LearnByBuilding

PyTorch is probably the most important framework used to train LLMs. You’re expected to know autograd, optimizers and nn.Modules inside out for any ML role. We’ve released a PyTorch sheet which takes you from basics to applying attention mechanism. tensortonic.com

We've added 300+ problems on TensorTonic. This includes questions on - > Pytorch, Numpy, Pandas, SQL > Algebra, Probability, Optimization, Stats > ML, DL, NLP sheets - We've also added papers such as DeepSeekV3, GPT2, LLaMA to implement from scratch tensortonic.com

ml öğrenmek veya bilgilerini tazelemek isteyeneler için çok iyi bi websitesine denk geldim bikaç gündür taa 2012 alexnet'ten beri çıkan paperları implement etmeye çalışıyorum. vgg, resnet, rnn vs. leetcode'ın çok daha öğretici versiyonu gibi düşünün ml için. örneğin baştan sonra transformers'ı implement ettiriyor: 1) tokenization 2) embedding 3) positional encoding 4) scaled dot-product attention 5) multi-head attention 6) feed-forward network 7) layer norm 8) encoder 9) decoder vs. ayrıca ml için linear cebir, olasılık, calculus vs konularını da çalışıp pratik yapabiliyorsunuz. sonuç olarak ml algoritmalarını sıfırdan implement etmek, arkalarındaki matematiği anlamak ve açıklayabilmek için çok iyi bi kaynak. afied.

Link - tally.so/r/rjKyOX

🚨 TensorTonic Pro is releasing soon! Last 24 hours to register for discount coupons! Everything you love about TensorTonic with a deeper curriculum, more features, and a stronger interview prep focus. Register now. link below 👇

I am not done with the ResNet ML paper on @TensorTonic just yet maybe in the next couple of days. Next I think I will tackle Vision Transformers! I have already completed the Attention Is All You Need ML paper

You don't understand transformers until you've built one from scratch. Tokenization → Embedding → Positional Encoding → Scaled Dot-Product Attention → Multi-Head Attention → Feed-Forward Network → Layer Norm → Encoder → Decoder → Full Transformer. > Each block is a coding problem. > Each one runs against real test cases. > No IDE setup, no environment issues, just open and code. We broke "Attention Is All You Need" into subset of problems so you can build the entire architecture one piece at a time. Try it free: tensortonic.com

The 15 papers that built modern AI, and you can implement all of them from scratch: Computer Vision Foundations > AlexNet (2012): Won ImageNet by a landslide. This is where deep learning started being taken seriously. > VGG (2014): The "what if we just stack more layers" experiment. Turns out, it worked. > ResNet (2015): Skip connections let networks go 152 layers deep without breaking. Most cited CV paper ever. Sequence Modeling > RNN: Networks that remember. Hidden states carry information across time steps, built for sequential data. > LSTM (1997): Added gates to control what to remember and what to forget. Made long sequences actually trainable. > GRU (2014): LSTM's simpler, faster sibling. Fewer gates, similar results. The Attention Revolution > Transformer (2017): Killed RNNs. Self-attention + parallelization changed everything. GPT, BERT, ViT: all start here. > BERT (2018): Reads text in both directions at once. Rewrote how search engines understand language. Advanced Vision > U-Net (2015): The U-shaped architecture that dominated medical imaging and later powered diffusion models. > ViT (2020): Took the transformer and applied it to images. 16x16 patches treated as tokens. It worked. Generative AI > VAE (2013): Encode data into a latent space, sample from it, decode back. The math is dense but the idea is elegant. > GAN (2014): Two networks competing: one generates fakes, the other catches them. The rivalry produces stunning outputs. > DDPM (2020): Generate images from pure noise, one denoising step at a time. This is the tech behind Midjourney and Stable Diffusion. Every single one of these is on TensorTonic as a set of coding problems. You implement the core architecture block by block, with interactive visualizations that show you what each layer actually does. tensortonic.com/research