Lets go through Paper of DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning – Day 80

Lets First Go Through its official paper of : DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning What Is DeepSeek-R1? DeepSeek-R1 is a new method for training large language models (LLMs) so they can solvetough reasoning problems (like math and coding challenges) more reliably. It starts with a base model(“DeepSeek-V3”) and then applies Reinforcement Learning (RL) in a way thatmakes the model teach itself to reason step by step, without relying on a huge amount of labeled examples. In simpler terms: They take an existing language model. They let it practice solving problems on its own, rewarding it when...

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104

Mathematical Explanation behind SGD Algorithm in Machine Learning _ day 5

In our previous blog post – on day 4 – we have talked about using the SGD algorithm for the MNIST dataset. But what is Stochastic Gradient Descent? Stochastic Gradient Descent (SGD) is an iterative method for optimizing an objective function that is written as a sum of differentiable functions. It’s a variant of the traditional gradient descent algorithm but with a twist: instead of computing the gradient of the whole dataset, it approximates the gradient using a single data point or a small batch of data points. This makes SGD much faster and more scalable, especially for large datasets....

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41

Deep Learning _ Perceptrons – day 9

Hello Introduction to Deep Learning and Neural Networks with a Focus on Perceptrons Deep Learning is a subset of machine learning that uses neural networks with many layers (hence “deep”) to model and understand complex patterns in data. These networks are inspired by the human brain and are particularly powerful for tasks like image and speech recognition. Neural Networks consist of interconnected layers of nodes, or neurons. Each neuron receives input, processes it, and passes it to the next layer. The simplest form of a neural network is the Perceptron, which is a single-layer neural network used for binary classification...

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man using smartphone with chat gpt

DeepNet – What Happens by Scaling Transformers to 1,000 Layers ? – Day 79

DeepNet – Scaling Transformers to 1,000 Layers: The Next Frontier in Deep Learning Introduction In recent years, Transformers have become the backbone of state-of-the-art models in both NLP and computer vision, powering systems like BERT, GPT, and LLaMA. However, as these models grow deeper, stability becomes a significant hurdle. Traditional Transformers struggle to remain stable beyond a few dozen layers. DeepNet, a new Transformer architecture, addresses this challenge by using a technique called DeepNorm, which stabilizes training up to 1,000 layers. To address this, DeepNet introduced the DeepNorm technique, which modifies residual connections to stabilize training for Transformers up to...

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61

Reinforcement Learning: An Evolution from Games to Real-World Impact – Day 78

Reinforcement Learning: An Evolution from Games to Real-World Impact Reinforcement Learning: An Evolution from Games to Real-World Impact Reinforcement Learning (RL) is a fascinating branch of machine learning, with its roots stretching back to the 1950s. Although not always in the limelight, RL made a significant impact in various domains, especially in gaming and machine control. In 2013, a team from DeepMind, a British startup, built a system capable of learning and excelling at Atari games using only raw pixels as input—without any knowledge of the game’s rules. This breakthrough led to DeepMind’s famous system, AlphaGo, defeating world Go champions...

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How Dalle Image Generator works ? – Day 77

Understanding DALL-E 3: Advanced Text-to-Image Generation Understanding DALL-E 3: Advanced Text-to-Image Generation DALL-E, developed by OpenAI, is a groundbreaking model that translates text prompts into detailed images using a sophisticated, layered architecture. The latest version, DALL-E 3, introduces enhanced capabilities, such as improved image fidelity, prompt-specific adjustments, and a system to identify AI-generated images. This article explores DALL-E’s architecture and workflow, providing updated information to simplify the technical aspects. 1. Core Components of DALL-E DALL-E integrates multiple components to process text and generate images. Each part has a unique role, as shown in Table 1. Component Purpose Description Transformer Text...

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Breaking Down Diffusion Models in Deep Learning – Day 75

Unveiling Diffusion Models: From Denoising to Generative Art The field of generative modeling has witnessed remarkable advancements over the past few years, with diffusion models emerging as a powerful class capable of generating high-quality, diverse images and other data types. Rooted in concepts from thermodynamics and stochastic processes, diffusion models have not only matched but, in some aspects, surpassed the performance of traditional generative models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). In this blog post, we’ll delve deep into the evolution of diffusion models, understand their underlying mechanisms, and explore their wide-ranging applications and future prospects. Table...

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132

Generative Adversarial Network (GANs) Deep Learning – Day 76

  Exploring the Evolution of GANs: From DCGANs to StyleGANs Generative Adversarial Networks (GANs) have revolutionized the field of image generation by allowing us to create realistic images from random noise. Over the years, the basic architecture of GANs has undergone significant enhancements, resulting in more stable and higher-quality image generation. In this post, we will dive deep into three key stages of GAN development: Deep Convolutional GANs (DCGANs), Progressive Growing of GANs, and StyleGANs. Deep Convolutional GANs (DCGANs) The introduction of Deep Convolutional GANs (DCGANs) in 2015 by Alec Radford and colleagues marked a major breakthrough in stabilizing GAN...

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Understanding Unsupervised Pretraining Using Stacked Autoencoders – Day 74

 Understanding Unsupervised Pretraining Using Stacked Autoencoders Introduction: Tackling Complex Tasks with Limited Labeled Data When dealing with complex supervised tasks but lacking sufficient labeled data, one effective solution is unsupervised pretraining. In this approach, a neural network is first trained to perform a similar task using a large, mostly unlabeled dataset. The pretrained layers from this network are then reused for the final model, allowing it to learn efficiently even with limited labeled data. The Role of Stacked Autoencoders A stacked autoencoder is a neural network architecture used for unsupervised learning. It consists of multiple layers that are trained to...

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Unlock the Secrets of Autoencoders, GANs, and Diffusion Models – Why You Must Know Them? -Day 73

 Understanding Autoencoders, GANs, and Diffusion Models – A Deep Dive In this post, we’ll explore three key models in machine learning: Autoencoders, GANs (Generative Adversarial Networks), and Diffusion Models. These models, used for unsupervised learning, play a crucial role in tasks such as dimensionality reduction, feature extraction, and generating realistic data. We’ll look at how each model works, their architecture, and practical examples. What Are Autoencoders? Autoencoders are neural networks designed to compress input data into dense representations (known as latent representations) and then reconstruct it back to the original form. The goal is to minimize the difference between the...

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