Vizuara

Most of us grew up thinking of AI as either computer vision or natural language processing. But what happens when you combine the two? You get Vision-Language Models (VLMs) - systems that can look at an image and understand it in natural language, or read a caption and imagine what it is describing. This is the foundation behind powerful models like CLIP, Flamingo, and GPT-4V. The architecture is actually elegant: Start with web-scale datasets of image-text pairs. Feed the text through a deep neural network. Feed the image through a separate deep neural network. And then align the two representations using a contrastive loss - encouraging the model to bring matching pairs closer, and push non-matching ones apart. That is how CLIP learns - by contrasting every image-text pair with every other pair in the batch. No bounding boxes. No manual annotations. Just tons of data and a clever objective. The result is a single model that can classify images, generate captions, retrieve similar visuals, and even serve as the backbone for multimodal agents. At Vizuara, we are now incorporating VLMs and vision-language reasoning into our upcoming bootcamps and live workshops. We believe the future of AI is not in isolated modalities, but in powerful combinations: https://3-in-1.vizuara.ai/ Check out the diagram to see how it all fits together. A few months ago, we had published our paper on NanoVLM. The paper was titled "NanoVLMs: How small can we go and still make coherent Vision Language Models?". If you are interested, you can read the paper here: https://lnkd.in/d7Xd3mNb

𝗚𝗣𝗧-𝟱 𝗶𝘀 𝗵𝗲𝗿𝗲, 𝗯𝘂𝘁 𝘄𝗵𝗶𝗰𝗵 𝗺𝗼𝗱𝗲𝗹 𝘀𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘂𝘀𝗲? OpenAI just launched 𝗚𝗣𝗧-𝟱 alongside several powerful variants. The big question for developers isn’t just how fast or smart they are; it’s which one gives the 𝗯𝗲𝘀𝘁 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗰𝗼𝘀𝘁. Whether you’re building AI into your app or optimizing an existing workflow, understanding the 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲-𝘁𝗼-𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳 is critical. This chart breaks down GPT-5 and its variants against previous models on leading benchmarks such as MMLU Pro, GPQA Diamond, and SciCoding so you can see exactly where each one stands and choose the right fit for your use case. To make this process simpler we’re introducing 𝗗𝘆𝗻𝗮𝗿𝗼𝘂𝘁𝗲, a universal LLM model router that’s fully compatible with 𝗚𝗣𝗧-𝟱 and other top models. Dynaroute automatically picks the best LLM for your task, balancing performance and cost so you can ship faster without the integration headache. The future is multi-model. We’re making it effortless. To keep updated with the post, subscribe to 𝗩𝗶𝘇𝘂𝗿𝗮’𝘀 𝗔𝗜 𝗡𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dk9sZC4a

How exactly is RL used in reasoning models? First, we must understand that language models are initially trained in a standard way, using supervised learning on massive datasets of text. This is called "pre-training". The model learns to predict the next word given previous words. But this phase alone does not teach the model how to reason well. So, after pretraining, models like ChatGPT, Claude, Gemini, or any reasoning-oriented model go through a post-training phase. This is where RL enters the picture. Unlike supervised learning there are no fixed labels. Instead, models are refined by giving them feedback. This feedback might come from humans or from other models. One popular approach is Reinforcement Learning from Human Feedback (RLHF). Here, the model generates multiple completions for a given prompt. Humans then rank these completions. The model learns a reward function that tries to mimic human preferences. Once this reward function is ready, RL is applied using a variant of Proximal Policy Optimization (PPO) which helps the model learn a policy that is more aligned with human expectations. Now here comes the interesting part. In reasoning models, we do not just want fluent language. We want the model to think in steps, verify answers, and revise its reasoning. Some research teams have used reward functions that explicitly favor multi-step reasoning chains, or penalize hallucinations. For example, f a model solves a math problem correctly in multiple steps, it is given a higher reward than just giving the final answer. In others, if the model catches its own mistake, it is rewarded for that. Instead of physical environments like a robot would use, these models interact with textual or symbolic environments. For instance, a reasoning model may be asked to solve puzzles, play logic games, or operate in a text-based world where each move is a logical inference. And it is not just human feedback anymore. Many recent models use AI feedback to scale things up. Here, an assistant model critiques the response of a student model. This whole pipeline can also be framed as a reinforcement learning loop. In short, RL is being repurposed in a very powerful way to shape the behavior and reasoning style of language models. It helps move the models away from just parroting data, and towards becoming more helpful, truthful, and safe. It does not replace the need for massive pretraining, but it adds a critical outer loop that helps align the model with goals that are harder to define using fixed labels. If you find this area fascinating, especially the role of RL in aligning and improving reasoning LLMs, then you will enjoy our bootcamp on RL at Vizuara, led by Dr. Rajat Dandekar: https://lnkd.in/emdJsJZx We have a 3-in-1 bundled bootcamp set too for you to check out: https://3-in-1.vizuara.ai/

"Hands-on RL Bootcamp" Register now: https://lnkd.in/emdJsJZx Reasoning LLMs are the future. Reinforcement Learning is the key. If you want to understand how models like ChatGPT are fine-tuned to reason rather than just autocomplete, you need to go beyond basic machine learning. You need to learn Reinforcement Learning. At Vizuara AI Labs, we have launched something we truly care about: Hands-on Reinforcement Learning Bootcamp -> 8-week intensive program -> Started August 1, every Friday from 2:00 to 3:30 PM IST This is not another course filled with recorded lectures and passive videos. This is a live, code-first, project-driven bootcamp that takes you through every core idea in modern reinforcement learning. You will go from: -> Fundamentals like Markov Decision Processes and Q-learning -> To deep RL methods like DQN on Atari and Cross-Entropy on CartPole -> And finally to RLHF, GRPO, and training reasoning-capable LLMs from scratch Instructor: Dr. Rajat Dandekar [PhD from Purdue University, BTech + MTech from IIT Madras]. He is the creator of the popular “Reasoning LLMs from Scratch” course. What makes this bootcamp different -> Full code walkthroughs, not just concepts -> 5 hands-on projects across finance, robotics, LLMs, web agents, and trading -> Assignments with real feedback -> Live office hours for support -> Lifetime access to all content -> The Vizuara RL Handbook included There is also a free plan for those who want to explore before committing. If you are serious about RL, this is your starting point.

OpenAI has just launched its most advanced model, 𝗚𝗣𝗧-𝟱, available in multiple variants to fit different performance and budget needs. For those building AI-powered products, GPT-5 offers:  • 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗪𝗶𝗻𝗱𝗼𝘄𝘀: Up to 𝟰𝟬𝟬𝗞 𝘁𝗼𝗸𝗲𝗻𝘀 for enterprise-level reasoning and 128K tokens for faster, cost-effective use cases.  • Improved reasoning, speed, and accuracy across all variants.  • Flexible pricing for different workloads. (See capability & pricing table below) But here’s the challenge: every time a new model drops like GPT-5, Gemini 2.5, GPT-OSS, LLaMA, Claude, and more, testing and switching can be time-consuming. That’s why we’re introducing 𝗗𝘆𝗻𝗮𝗿𝗼𝘂𝘁𝗲, a universal LLM model router that’s fully compatible with 𝗚𝗣𝗧-𝟱 and other top models. Dynaroute automatically picks the best LLM for your task, balancing performance and cost so you can ship faster without the integration headache. The future is multi-model. We’re making it effortless. To keep updated with the post, subscribe to 𝗩𝗶𝘇𝘂𝗿𝗮’𝘀 𝗔𝗜 𝗡𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dk9sZC4a  

This August, we are going all in on Language, Reasoning, and Vision. Three pillars of modern AI, taught in one powerful bootcamp bundle. At Vizuara, we have brought together some of the best minds - PhDs from MIT, Purdue, and IIT Madras - to take you on a hands-on journey through the most exciting areas of AI today. 1) Start with Language: Build your own small language models from scratch in our 3-day SLM workshop. [By Dr. Raj Abhijit Dandekar] 2) Master Reasoning: Learn how to train intelligent agents using Reinforcement Learning in an 8-week bootcamp. [By Dr. Rajat Dandekar] 3) And, dive into Vision: Build real-time object detection and segmentation systems in our 5-week Computer Vision program. [By me] All sessions are live. You will learn by building real things. And yes 0 there will be mentorship, projects, and a solid community backing you throughout. More than 500 learners have already gone through our various programs and seen real career impact. Limited seats. Everything kicks off this August. 🔗 Join us at https://3-in-1.vizuara.ai/ Let us build the future of AI. Together.

Kimi K2: A solid open-source model worth looking into Repo: https://lnkd.in/gwWeivf5 [7.3k stars] Paper: https://lnkd.in/dM8qWByB These days, there are too many LLMs coming out, and most of them feel like the same thing with minor changes. But Kimi K2, built by Moonshot AI, felt a bit different. It is not just about size or benchmarks. There is solid engineering behind it. It is a Mixture-of-Experts model with a total of 1 trillion parameters, but it only activates 32 billion at a time. That keeps things efficient. They also came up with a new optimizer called MuonClip, which helped them train on 15.5 trillion tokens without any training issues. That is not a small deal. After the pre-training, they added more layers of post-training using agentic data and reinforcement learning, which makes the model better at real-world tasks, especially those involving reasoning and exploration. What is another great thing is that they have shared the model weights publicly. And it is doing really well on coding, math, and reasoning benchmarks like LiveCodeBench, AIME, and GPQA. If you are into building LLM applications or exploring agent-based workflows, it is worth checking out. Also, if you want to actually understand how reinforcement learning works behind these kinds of models, we are running a Hands-on RL Bootcamp at Vizuara, started on August 1st, taught by Dr. Rajat Dandekar. All live, all project-based. Here is the link: https://lnkd.in/emdJsJZx

Reasoning is the future of LLMs. RL is the future of reasoning. When Dr. Sreedath first came across how DeepSeek R1-Zero trains its models, he was fascinated. Unlike traditional supervised learning where we show the model thousands of examples with correct answers, here the model is left to figure things out on its own. You give it a question, say a coding challenge from LeetCode, and it tries out multiple possible answers. Each answer is then tested for accuracy, and based on how well it performs, the model receives a reward signal. This reward is used to calculate how good that decision was, and gradually, the model updates its own parameters using gradient ascent. Over time, it becomes better at choosing the right answers, not because someone told it what the right answer is, but because it learned from trial, error, and reward. This is the heart of reinforcement learning, and it is becoming more and more important in modern AI, especially in reasoning agents and LLMs that need to do more than just generate text. Reinforcement learning gives these models a way to interact with the world, make decisions, receive feedback, and improve their actions over time. It is used in training systems that plan, explore, solve problems, and even use tools. And the best part is that it closely mimics how humans learn - we do something, see what happens, and get better next time. That is why RL is so valuable, and it is being used not only in research but also in real-world applications across robotics, finance, games, healthcare, and now even code generation and AI agents. If you are someone who wants to get into this fascinating area and learn by actually building things yourself, I would highly recommend checking out the Hands-on Reinforcement Learning Bootcamp at Vizuara, taught by Dr. Rajat Dandekar. The bootcamp started on August 1st, and it is fully live and project-driven. You will start from the fundamentals and build all the important algorithms from scratch - including policy gradients, Q-learning, actor-critic, and more. It is not just about watching lectures; you will be coding, experimenting, and understanding how RL really works under the hood. Here is the link if you want to know more: https://lnkd.in/emdJsJZx.

The heart of ML is optimization. The heart of optimization is mathematics. In any machine learning model, the optimizer plays a central role. It decides how the model learns from data by updating the parameters during training. Without the right optimizer, even a well-designed model can struggle to converge or perform poorly. Here are six important optimization methods that form the foundation of most modern training setups: 1) Gradient Descent This is the standard method that uses the full dataset to compute the gradient and update the parameters. Update rule: θ = θ − η * ∇J(θ) where θ is the parameter, η is the learning rate, and ∇J(θ) is the gradient of the loss with respect to θ. 2) Stochastic Gradient Descent (SGD) Instead of the whole dataset, this method uses just one data point at a time to update the weights. Update rule: θ = θ − η * ∇J(θ; xᵢ, yᵢ) where (xᵢ, yᵢ) is a single training sample. 3) Mini-batch Gradient Descent A compromise between batch and stochastic methods, this uses a small batch of data points at each step. Update rule: θ = θ − η * (1/m) * Σ ∇J(θ; xᵢ, yᵢ) where m is the batch size. 4) Momentum This method adds a velocity term to accelerate learning in the relevant direction and reduce oscillations. Update rules: v = β * v − η * ∇J(θ) θ = θ + v Here, β is the momentum factor (commonly 0.9), and v is the velocity vector. 5) RMSprop This method adjusts the learning rate for each parameter based on a moving average of the squared gradients. Update rules: s = β * s + (1 − β) * (∇J(θ))² θ = θ − η * ∇J(θ) / (√s + ε) s is the accumulated squared gradient, and ε is a small constant to avoid division by zero. 6) Adam A widely used optimizer that combines momentum and RMSprop. It maintains running averages of both gradients and squared gradients. Update rules: m = β₁ * m + (1 − β₁) * ∇J(θ) v = β₂ * v + (1 − β₂) * (∇J(θ))² m̂ = m / (1 − β₁ᵗ) v̂ = v / (1 − β₂ᵗ) θ = θ − η * m̂ / (√v̂ + ε) Each of these methods has a clear mathematical basis and specific use cases. Together, they offer a solid toolkit for effective model training. To explore these optimizers in more detail, Dr. Sreedath has created a complete YouTube playlist that covers them one by one with visual intuition and examples on Vizuara's YouTube channel: https://lnkd.in/d27YRS4Z If you are serious about mastering machine learning, Vizuara’s Minor in AI offers a deep, structured program taught by experienced instructors from research and industry: https://minor.vizuara.ai/

What can you really do with OpenCV? If you are just starting out with computer vision, you will hear this name almost immediately: OpenCV. And for good reason. It is light, fast, open-source, and incredibly versatile. You can read images, capture live webcam feeds, draw on frames, apply filters, detect edges, track movement, and even perform object detection. Even though OpenCV is good for beginners even today, most production pipelines still rely on it somewhere. You want to quickly resize a batch of images? OpenCV. Want to overlay a mask on a frame? OpenCV. Want to process a live video stream and apply transformations in real time? Again, OpenCV. You can also go very deep with OpenCV. For example, you can: -Detect and track multiple moving objects across frames using background subtraction and optical flow -Build your own face detection app using Haar cascades or DNN modules -Use morphology operations to clean up noisy masks in medical images -Segment objects using GrabCut or Watershed -Calibrate a stereo camera setup to estimate depth -Extract features like SIFT, ORB, or AKAZE and use them for matching images across views Even before you bring in any deep learning, OpenCV allows you to explore and implement dozens of real-world vision applications. And if you combine it with Python, it becomes an incredibly powerful prototyping tool. You write ten lines of code and suddenly you are drawing bounding boxes around pedestrians or detecting lane lines on a road. For anyone trying to break into computer vision – OpenCV is your foundation. Of course, once you go deeper into object detection or segmentation, you start using models like YOLO, U-Net, or Faster R-CNN. But even then, OpenCV plays a vital role in preprocessing your inputs, visualizing outputs, and deploying your models. If you want to learn how to use OpenCV effectively [not simply reading images but actually building things with it] Dr. Sreedath is teaching a 5-week live, hands-on Computer Vision Bootcamp at Vizuara. You will start with OpenCV, but you will also build object detectors using R-CNN and YOLO, segmentation models using U-Net, and even deploy your models as APIs. Everything is project-based and taught live. We start August 4. Details and free plan here: 👉 https://lnkd.in/duWwXVNV