Published:
Scaling laws describe the relationship between a model’s performance and the scale of three key ingredients: the number of model parameters, the size of the dataset, and the amount of computational power used for training. The core finding is that as you increase these resources, the model’s performance improves in a predictable, power-law fashion. Read more
Published:
Training Read more
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Training Read more
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Training Read more
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Training Read more
Published:
In this blog post, we’ll explore LeetCode’s “Generate Parentheses” problem and present two clean backtracking solutions—one in Python and one in C++. This classic problem is an excellent demonstration of how to use recursion to systematically explore all valid possibilities. Read more
Published:
In many programming interviews, candidates encounter challenges that test their ability to manipulate strings efficiently. One such problem involves reversing a string with a twist: only the letters should be reversed, while non-letter characters remain in their original positions. In this blog post, we’ll explore this problem and present an optimized Python solution. Read more
Published:
President Donald Trump is set to announce a monumental private sector initiative aimed at bolstering the United States’ artificial intelligence (AI) infrastructure with an investment of up to $500 billion. This ambitious plan involves leading tech companies like OpenAI, SoftBank, and Oracle, under a collaborative venture named “Stargate.” Read more
Published:
Meta just released a new collection of Llama 3.1 models in 8B, 70B, and 405B parameter sizes. Read more
Published:
I remember one of the experiences I had duing my MS in Computer Science at Georgia Tech while working on a CNN for protein data. I was feeding raw protein data as an image, with pixel values in the standard 0-255 range, directly into the network. My model’s accuracy was stuck below 20%, and the loss was oscillating wildly. After hours of debugging, I traced the issue to its source: I had neglected to normalize my input data, leading to a classic case of “exploding gradients.” Read more
Published:
Backpropagation is the cornerstone algorithm powering much of the deep learning revolution. Coupled with gradient descent, it allows us to train incredibly complex neural networks on vast datasets. However, it’s not a silver bullet. One of the fundamental challenges that can prevent backpropagation from finding the best possible solution is the presence of local minima in the optimization landscape. Read more
Published:
In this blog post, we’ll explore LeetCode’s “Generate Parentheses” problem and present two clean backtracking solutions—one in Python and one in C++. This classic problem is an excellent demonstration of how to use recursion to systematically explore all valid possibilities. Read more
Published:
I remember one of the experiences I had duing my MS in Computer Science at Georgia Tech while working on a CNN for protein data. I was feeding raw protein data as an image, with pixel values in the standard 0-255 range, directly into the network. My model’s accuracy was stuck below 20%, and the loss was oscillating wildly. After hours of debugging, I traced the issue to its source: I had neglected to normalize my input data, leading to a classic case of “exploding gradients.” Read more
Published:
In this post, we dive into the world of supervised learning, comparing the performance of four popular algorithms: k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Neural Networks (NN), and Decision Trees with Boosting (specifically, AdaBoost). We’ll analyze their effectiveness on two distinct datasets, highlighting their strengths and weaknesses. Read more
Published:
Scaling laws describe the relationship between a model’s performance and the scale of three key ingredients: the number of model parameters, the size of the dataset, and the amount of computational power used for training. The core finding is that as you increase these resources, the model’s performance improves in a predictable, power-law fashion. Read more
Published:
I remember one of the experiences I had duing my MS in Computer Science at Georgia Tech while working on a CNN for protein data. I was feeding raw protein data as an image, with pixel values in the standard 0-255 range, directly into the network. My model’s accuracy was stuck below 20%, and the loss was oscillating wildly. After hours of debugging, I traced the issue to its source: I had neglected to normalize my input data, leading to a classic case of “exploding gradients.” Read more
Published:
Backpropagation is the cornerstone algorithm powering much of the deep learning revolution. Coupled with gradient descent, it allows us to train incredibly complex neural networks on vast datasets. However, it’s not a silver bullet. One of the fundamental challenges that can prevent backpropagation from finding the best possible solution is the presence of local minima in the optimization landscape. Read more
Published:
The development of reasoning capabilities in large language models (LLMs) is a complex yet pivotal frontier in AI research. DeepSeek R1 represents a major leap in this space, introducing innovative methodologies for reasoning-oriented model training. In this post, we’ll explore what makes DeepSeek R1 significant, its architectural innovations, and its implications for the future of AI. Read more
Published:
The development of reasoning capabilities in large language models (LLMs) is a complex yet pivotal frontier in AI research. DeepSeek R1 represents a major leap in this space, introducing innovative methodologies for reasoning-oriented model training. In this post, we’ll explore what makes DeepSeek R1 significant, its architectural innovations, and its implications for the future of AI. Read more
Published:
President Donald Trump is set to announce a monumental private sector initiative aimed at bolstering the United States’ artificial intelligence (AI) infrastructure with an investment of up to $500 billion. This ambitious plan involves leading tech companies like OpenAI, SoftBank, and Oracle, under a collaborative venture named “Stargate.” Read more
Published:
I remember one of the experiences I had duing my MS in Computer Science at Georgia Tech while working on a CNN for protein data. I was feeding raw protein data as an image, with pixel values in the standard 0-255 range, directly into the network. My model’s accuracy was stuck below 20%, and the loss was oscillating wildly. After hours of debugging, I traced the issue to its source: I had neglected to normalize my input data, leading to a classic case of “exploding gradients.” Read more
Published:
Randomized optimization algorithms like Genetic Algorithms (GA), Simulated Annealing (SA), and Randomized Hill Climbing (RHC) are powerful tools for solving problems where traditional gradient-based methods fail. These “black-box” problems are common in fields like logistics, engineering design, and machine learning, where the optimization landscape is complex, non-differentiable, or riddled with local minima. Read more
Published:
Backpropagation is the cornerstone algorithm powering much of the deep learning revolution. Coupled with gradient descent, it allows us to train incredibly complex neural networks on vast datasets. However, it’s not a silver bullet. One of the fundamental challenges that can prevent backpropagation from finding the best possible solution is the presence of local minima in the optimization landscape. Read more
Published:
President Donald Trump is set to announce a monumental private sector initiative aimed at bolstering the United States’ artificial intelligence (AI) infrastructure with an investment of up to $500 billion. This ambitious plan involves leading tech companies like OpenAI, SoftBank, and Oracle, under a collaborative venture named “Stargate.” Read more
Published:
In this post, we dive into the world of supervised learning, comparing the performance of four popular algorithms: k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Neural Networks (NN), and Decision Trees with Boosting (specifically, AdaBoost). We’ll analyze their effectiveness on two distinct datasets, highlighting their strengths and weaknesses. Read more
Published:
Scaling laws describe the relationship between a model’s performance and the scale of three key ingredients: the number of model parameters, the size of the dataset, and the amount of computational power used for training. The core finding is that as you increase these resources, the model’s performance improves in a predictable, power-law fashion. Read more
Published:
The development of reasoning capabilities in large language models (LLMs) is a complex yet pivotal frontier in AI research. DeepSeek R1 represents a major leap in this space, introducing innovative methodologies for reasoning-oriented model training. In this post, we’ll explore what makes DeepSeek R1 significant, its architectural innovations, and its implications for the future of AI. Read more
Published:
In this blog post, we’ll explore LeetCode’s “Generate Parentheses” problem and present two clean backtracking solutions—one in Python and one in C++. This classic problem is an excellent demonstration of how to use recursion to systematically explore all valid possibilities. Read more
Published:
In many programming interviews, candidates encounter challenges that test their ability to manipulate strings efficiently. One such problem involves reversing a string with a twist: only the letters should be reversed, while non-letter characters remain in their original positions. In this blog post, we’ll explore this problem and present an optimized Python solution. Read more
Published:
Meta just released a new collection of Llama 3.1 models in 8B, 70B, and 405B parameter sizes. Read more
Published:
Backpropagation is the cornerstone algorithm powering much of the deep learning revolution. Coupled with gradient descent, it allows us to train incredibly complex neural networks on vast datasets. However, it’s not a silver bullet. One of the fundamental challenges that can prevent backpropagation from finding the best possible solution is the presence of local minima in the optimization landscape. Read more
Published:
Scaling laws describe the relationship between a model’s performance and the scale of three key ingredients: the number of model parameters, the size of the dataset, and the amount of computational power used for training. The core finding is that as you increase these resources, the model’s performance improves in a predictable, power-law fashion. Read more
Published:
I remember one of the experiences I had duing my MS in Computer Science at Georgia Tech while working on a CNN for protein data. I was feeding raw protein data as an image, with pixel values in the standard 0-255 range, directly into the network. My model’s accuracy was stuck below 20%, and the loss was oscillating wildly. After hours of debugging, I traced the issue to its source: I had neglected to normalize my input data, leading to a classic case of “exploding gradients.” Read more
Published:
Randomized optimization algorithms like Genetic Algorithms (GA), Simulated Annealing (SA), and Randomized Hill Climbing (RHC) are powerful tools for solving problems where traditional gradient-based methods fail. These “black-box” problems are common in fields like logistics, engineering design, and machine learning, where the optimization landscape is complex, non-differentiable, or riddled with local minima. Read more
Published:
Backpropagation is the cornerstone algorithm powering much of the deep learning revolution. Coupled with gradient descent, it allows us to train incredibly complex neural networks on vast datasets. However, it’s not a silver bullet. One of the fundamental challenges that can prevent backpropagation from finding the best possible solution is the presence of local minima in the optimization landscape. Read more
Published:
In this post, we dive into the world of supervised learning, comparing the performance of four popular algorithms: k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Neural Networks (NN), and Decision Trees with Boosting (specifically, AdaBoost). We’ll analyze their effectiveness on two distinct datasets, highlighting their strengths and weaknesses. Read more
Published:
Meta just released a new collection of Llama 3.1 models in 8B, 70B, and 405B parameter sizes. Read more
Published:
In this post, we dive into the world of supervised learning, comparing the performance of four popular algorithms: k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Neural Networks (NN), and Decision Trees with Boosting (specifically, AdaBoost). We’ll analyze their effectiveness on two distinct datasets, highlighting their strengths and weaknesses. Read more
Published:
I remember one of the experiences I had duing my MS in Computer Science at Georgia Tech while working on a CNN for protein data. I was feeding raw protein data as an image, with pixel values in the standard 0-255 range, directly into the network. My model’s accuracy was stuck below 20%, and the loss was oscillating wildly. After hours of debugging, I traced the issue to its source: I had neglected to normalize my input data, leading to a classic case of “exploding gradients.” Read more
Published:
Backpropagation is the cornerstone algorithm powering much of the deep learning revolution. Coupled with gradient descent, it allows us to train incredibly complex neural networks on vast datasets. However, it’s not a silver bullet. One of the fundamental challenges that can prevent backpropagation from finding the best possible solution is the presence of local minima in the optimization landscape. Read more
Published:
Meta just released a new collection of Llama 3.1 models in 8B, 70B, and 405B parameter sizes. Read more
Published:
Randomized optimization algorithms like Genetic Algorithms (GA), Simulated Annealing (SA), and Randomized Hill Climbing (RHC) are powerful tools for solving problems where traditional gradient-based methods fail. These “black-box” problems are common in fields like logistics, engineering design, and machine learning, where the optimization landscape is complex, non-differentiable, or riddled with local minima. Read more
Published:
Backpropagation is the cornerstone algorithm powering much of the deep learning revolution. Coupled with gradient descent, it allows us to train incredibly complex neural networks on vast datasets. However, it’s not a silver bullet. One of the fundamental challenges that can prevent backpropagation from finding the best possible solution is the presence of local minima in the optimization landscape. Read more
Published:
President Donald Trump is set to announce a monumental private sector initiative aimed at bolstering the United States’ artificial intelligence (AI) infrastructure with an investment of up to $500 billion. This ambitious plan involves leading tech companies like OpenAI, SoftBank, and Oracle, under a collaborative venture named “Stargate.” Read more
Published:
In this post, we dive into the world of supervised learning, comparing the performance of four popular algorithms: k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Neural Networks (NN), and Decision Trees with Boosting (specifically, AdaBoost). We’ll analyze their effectiveness on two distinct datasets, highlighting their strengths and weaknesses. Read more
Published:
In this blog post, we’ll explore LeetCode’s “Generate Parentheses” problem and present two clean backtracking solutions—one in Python and one in C++. This classic problem is an excellent demonstration of how to use recursion to systematically explore all valid possibilities. Read more
Published:
In many programming interviews, candidates encounter challenges that test their ability to manipulate strings efficiently. One such problem involves reversing a string with a twist: only the letters should be reversed, while non-letter characters remain in their original positions. In this blog post, we’ll explore this problem and present an optimized Python solution. Read more
Published:
Randomized optimization algorithms like Genetic Algorithms (GA), Simulated Annealing (SA), and Randomized Hill Climbing (RHC) are powerful tools for solving problems where traditional gradient-based methods fail. These “black-box” problems are common in fields like logistics, engineering design, and machine learning, where the optimization landscape is complex, non-differentiable, or riddled with local minima. Read more
Published:
Randomized optimization algorithms like Genetic Algorithms (GA), Simulated Annealing (SA), and Randomized Hill Climbing (RHC) are powerful tools for solving problems where traditional gradient-based methods fail. These “black-box” problems are common in fields like logistics, engineering design, and machine learning, where the optimization landscape is complex, non-differentiable, or riddled with local minima. Read more
Published:
The development of reasoning capabilities in large language models (LLMs) is a complex yet pivotal frontier in AI research. DeepSeek R1 represents a major leap in this space, introducing innovative methodologies for reasoning-oriented model training. In this post, we’ll explore what makes DeepSeek R1 significant, its architectural innovations, and its implications for the future of AI. Read more
Published:
The development of reasoning capabilities in large language models (LLMs) is a complex yet pivotal frontier in AI research. DeepSeek R1 represents a major leap in this space, introducing innovative methodologies for reasoning-oriented model training. In this post, we’ll explore what makes DeepSeek R1 significant, its architectural innovations, and its implications for the future of AI. Read more
Published:
Scaling laws describe the relationship between a model’s performance and the scale of three key ingredients: the number of model parameters, the size of the dataset, and the amount of computational power used for training. The core finding is that as you increase these resources, the model’s performance improves in a predictable, power-law fashion. Read more
Published:
Randomized optimization algorithms like Genetic Algorithms (GA), Simulated Annealing (SA), and Randomized Hill Climbing (RHC) are powerful tools for solving problems where traditional gradient-based methods fail. These “black-box” problems are common in fields like logistics, engineering design, and machine learning, where the optimization landscape is complex, non-differentiable, or riddled with local minima. Read more
Published:
In many programming interviews, candidates encounter challenges that test their ability to manipulate strings efficiently. One such problem involves reversing a string with a twist: only the letters should be reversed, while non-letter characters remain in their original positions. In this blog post, we’ll explore this problem and present an optimized Python solution. Read more
Published:
In this post, we dive into the world of supervised learning, comparing the performance of four popular algorithms: k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Neural Networks (NN), and Decision Trees with Boosting (specifically, AdaBoost). We’ll analyze their effectiveness on two distinct datasets, highlighting their strengths and weaknesses. Read more
Published:
In this post, we dive into the world of supervised learning, comparing the performance of four popular algorithms: k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Neural Networks (NN), and Decision Trees with Boosting (specifically, AdaBoost). We’ll analyze their effectiveness on two distinct datasets, highlighting their strengths and weaknesses. Read more
Published:
President Donald Trump is set to announce a monumental private sector initiative aimed at bolstering the United States’ artificial intelligence (AI) infrastructure with an investment of up to $500 billion. This ambitious plan involves leading tech companies like OpenAI, SoftBank, and Oracle, under a collaborative venture named “Stargate.” Read more