#Stochastic gradient descent

[aromanrom]

Machine Learning from scratch

Introduction This is the second project I already had when I posted Updates to project. Here is its repository: Machine Learning project on GitHub1. I started it as the Artificial Intelligence hype was going stronger, just to have a project on a domain that’s of big interest nowadays. At that point I was thinking to continue it with convolutional networks and at least recurrent networks, not…

[ingoampt]

Day 8 _ Gradient Decent Types : Batch, Stochastic and Mini-Batch

Understanding Gradient Descent: Batch, Stochastic, and Mini-Batch Understanding Gradient Descent: Batch, Stochastic, and Mini-Batch Learn the key differences between Batch Gradient Descent, Stochastic Gradient Descent, and Mini-Batch Gradient Descent, and how to apply them in your machine learning models. Batch Gradient Descent Batch Gradient Descent calculates the gradient of the cost function…

[xyymath]

Real-Life Uses of Calculus

Calculus isn’t just an abstract, ivory tower concept relegated to textbooks—it’s a powerful tool woven deeply into the fabric of our daily lives, from the precision of medical dosage to the unpredictability of the stock market.

1. Medicine: Optimizing Drug Dosage

Calculus plays a key role in pharmacokinetics, the branch of science that deals with the absorption, distribution, metabolism, and excretion of drugs in the body. When doctors prescribe medication, they need to ensure that drug levels remain within therapeutic bounds, not too high to cause toxicity and not too low to be ineffective. This is where differential equations, a core part of calculus, come into play. The rate of change of drug concentration over time is modeled with calculus to determine optimal dosage and scheduling for sustained, effective drug levels.

Take antibiotics, for example: they must be administered at specific intervals to maintain an effective concentration in the bloodstream while preventing bacterial resistance. Calculus allows for the continuous monitoring of drug levels and the adjustment of dosages based on individual metabolism rates, ensuring maximum therapeutic benefit.

2. Physics and Engineering: Motion and Forces

In classical mechanics, calculus is used to describe motion. Newton's laws of motion and universal gravitation are based on derivatives and integrals, the foundational elements of calculus. The change in velocity (acceleration) is the derivative of position with respect to time, while the area under the velocity-time graph gives us the distance traveled.

For instance, when designing cars, engineers use calculus to model the forces acting on the vehicle, such as friction, air resistance, and engine power. Calculus helps optimize everything from fuel efficiency to safety features, ensuring that a car can handle various conditions without exceeding performance thresholds.

3. Economics and Finance: Predicting Stock Market Trends

In economics, calculus is used to understand and predict market behavior. The concept of marginal analysis—examining the effects of small changes in variables—relies heavily on calculus. For example, marginal cost is the derivative of total cost with respect to quantity, and marginal revenue is the derivative of total revenue with respect to the quantity of goods sold.

In the stock market, calculus is utilized in quantitative finance to model stock prices using stochastic differential equations. Techniques like Black-Scholes for options pricing rely on calculus to determine the fair price of financial derivatives by analyzing how small fluctuations in stock prices impact their expected value. The concept of risk management—how much risk is worth taking for a given return—also uses derivatives to evaluate the rate of change of potential outcomes over time.

4. Environmental Science: Climate Modeling

Climate change models are inherently tied to calculus. Calculus is used to model the flow of energy through the Earth's atmosphere, oceans, and land, and how this energy affects global temperatures. The change in temperature over time is governed by differential equations, accounting for factors like greenhouse gas emissions, solar radiation, and ocean currents. As a result, climate scientists use calculus to predict future climate scenarios under various emission levels, helping inform policy decisions on global warming and sustainability.

5. Computer Science and Machine Learning: Optimization Algorithms

In machine learning, algorithms are designed to optimize a given function—whether it's minimizing the error in predictions or maximizing efficiency in a task. These algorithms often rely on derivatives to find the minimum or maximum of a function. For example, gradient descent, a popular optimization algorithm, uses the derivative of a function to iteratively adjust parameters and reach the optimal solution.

In computer graphics, calculus is essential for creating smooth curves and realistic animations. The mathematical process of curvature, which is the rate of change of direction along a curve, is vital for rendering images in 3D modeling and augmented reality.

6. Astronomy and Space Exploration: Orbital Mechanics

In space travel, calculus is crucial in calculating orbits, trajectories, and spaceship velocity. The path a spacecraft takes through space is influenced by gravitational forces, which can be modeled using calculus. For example, NASA’s mission to Mars relied on calculus to calculate the optimal launch window by accounting for the positions and motions of both Earth and Mars, ensuring the spacecraft would reach its destination efficiently.

[bayesic-bitch]

The more I learn about diffusion models the more I fall in love with them. What an incredibly elegant little mathematical trick.

They're easy to calculate the KL divergence for, and unlike GANs, this is a stationary objective rather than a minimax game that can diverge or collapse

They're incredibly expressive -- they can represent arbitrary probability distributions up to some regularity conditions

The output of the neural net at one step approximates a gaussian convolution over the data. Which means it's easy to understand what exactly the model is learning to predict

They're stochastic differential equations, which means you can basically treat them as linear operators under certain conditions. In particular, if you want to change the distribution to maximize some function F, just add the gradient of F to the predicted noise. Then the forward process is effectively doing gradient descent to find a distribution that maximizes F while remaining similar to the learned distribution

This means you can multiply the probability distributions of learned models just by adding their outputs together. And of course you can take linear combinations of them by just randomly choosing which ones to sample.

On top of all of this, the KL divergence also bounds the Wasserstein distance. Which means you don't have to pick between bounding a divergence or bounding a metric, you just bet both of them for free.

All of this means that there's a ton of things you can do with them that you wouldn't be able to do with most other kinds of model.

[horny-pa-san]

On a more technical side, the problem with AI “art” (once again im using paintings as a reference but the argument should generalize to all forms of arts) is the following. Sure, there a rules that can tell you a picture looks pleasant. Like compositions or brush techniques or (god forbid) color theory. The thing is, many if not most artworks deviate from those rules. That’s what makes Art in the first place. And when those rules are broken it matters to us, the viewers, whether it is intentional. With computer generated works such questions are meaningless. There is no intention, just a bunch of number generated by some algorithm. And the algorithm isn’t even that complicated or interesting, it’s just stochastic gradient descent on a big parameter space to minimize some arbitrary cost function.

[jcmarchi]

Using AI to Predict a Blockbuster Movie

New Post has been published on https://thedigitalinsider.com/using-ai-to-predict-a-blockbuster-movie/

Using AI to Predict a Blockbuster Movie

Although film and television are often seen as creative and open-ended industries, they have long been risk-averse. High production costs (which may soon lose the offsetting advantage of cheaper overseas locations, at least for US projects) and a fragmented production landscape make it difficult for independent companies to absorb a significant loss.

Therefore, over the past decade, the industry has taken a growing interest in whether machine learning can detect trends or patterns in how audiences respond to proposed film and television projects.

The main data sources remain the Nielsen system (which offers scale, though its roots lie in TV and advertising) and sample-based methods such as focus groups, which trade scale for curated demographics. This latter category also includes scorecard feedback from free movie previews – however, by that point, most of a production’s budget is already spent.

The ‘Big Hit’ Theory/Theories

Initially, ML systems leveraged traditional analysis methods such as linear regression, K-Nearest Neighbors, Stochastic Gradient Descent, Decision Tree and Forests, and Neural Networks, usually in various combinations nearer in style to pre-AI statistical analysis, such as a 2019 University of Central Florida initiative to forecast successful TV shows based on combinations of actors and writers (among other factors):

A 2018 study rated the performance of episodes based on combinations of characters and/or writer (most episodes were written by more than one person). Source: https://arxiv.org/pdf/1910.12589

The most relevant related work, at least that which is deployed in the wild (though often criticized) is in the field of recommender systems:

A typical video recommendation pipeline. Videos in the catalog are indexed using features that may be manually annotated or automatically extracted. Recommendations are generated in two stages by first selecting candidate videos and then ranking them according to a user profile inferred from viewing preferences. Source: https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2023.1281614/full

However, these kinds of approaches analyze projects that are already successful. In the case of prospective new shows or movies, it is not clear what kind of ground truth would be most applicable – not least because changes in public taste, combined with improvements and augmentations of data sources, mean that decades of consistent data is usually not available.

This is an instance of the cold start problem, where recommendation systems must evaluate candidates without any prior interaction data. In such cases, traditional collaborative filtering breaks down, because it relies on patterns in user behavior (such as viewing, rating, or sharing) to generate predictions. The problem is that in the case of most new movies or shows, there is not yet enough audience feedback to support these methods.

Comcast Predicts

A new paper from Comcast Technology AI, in association with George Washington University, proposes a solution to this problem by prompting a language model with structured metadata about unreleased movies.

The inputs include cast, genre, synopsis, content rating, mood, and awards, with the model returning a ranked list of likely future hits.

The authors use the model’s output as a stand-in for audience interest when no engagement data is available, hoping to avoid early bias toward titles that are already well known.

The very short (three-page) paper, titled Predicting Movie Hits Before They Happen with LLMs, comes from six researchers at Comcast Technology AI, and one from GWU, and states:

‘Our results show that LLMs, when using movie metadata, can significantly outperform the baselines. This approach could serve as an assisted system for multiple use cases, enabling the automatic scoring of large volumes of new content released daily and weekly.

‘By providing early insights before editorial teams or algorithms have accumulated sufficient interaction data, LLMs can streamline the content review process.

‘With continuous improvements in LLM efficiency and the rise of recommendation agents, the insights from this work are valuable and adaptable to a wide range of domains.’

If the approach proves robust, it could reduce the industry’s reliance on retrospective metrics and heavily-promoted titles by introducing a scalable way to flag promising content prior to release. Thus, rather than waiting for user behavior to signal demand, editorial teams could receive early, metadata-driven forecasts of audience interest, potentially redistributing exposure across a wider range of new releases.

Method and Data

The authors outline a four-stage workflow: construction of a dedicated dataset from unreleased movie metadata; the establishment of a baseline model for comparison; the evaluation of apposite LLMs using both natural language reasoning and embedding-based prediction; and the optimization of outputs through prompt engineering in generative mode, using Meta’s Llama 3.1 and 3.3 language models.

Since, the authors state, no publicly available dataset offered a direct way to test their hypothesis (because most existing collections predate LLMs, and lack detailed metadata), they built a benchmark dataset from the Comcast entertainment platform, which serves tens of millions of users across direct and third-party interfaces.

The dataset tracks newly-released movies, and whether they later became popular, with popularity defined through user interactions.

The collection focuses on movies rather than series, and the authors state:

‘We focused on movies because they are less influenced by external knowledge than TV series, improving the reliability of experiments.’

Labels were assigned by analyzing the time it took for a title to become popular across different time windows and list sizes. The LLM was prompted with metadata fields such as genre, synopsis, rating, era, cast, crew, mood, awards, and character types.

For comparison, the authors used two baselines: a random ordering; and a Popular Embedding (PE) model (which we will come to shortly).

The project used large language models as the primary ranking method, generating ordered lists of movies with predicted popularity scores and accompanying justifications – and these outputs were shaped by prompt engineering strategies designed to guide the model’s predictions using structured metadata.

The prompting strategy framed the model as an ‘editorial assistant’ assigned with identifying which upcoming movies were most likely to become popular, based solely on structured metadata, and then tasked with reordering a fixed list of titles without introducing new items, and to return the output in JSON format.

Each response consisted of a ranked list, assigned popularity scores, justifications for the rankings, and references to any prior examples that influenced the outcome. These multiple levels of metadata were intended to improve the model’s contextual grasp, and its ability to anticipate future audience trends.

Tests

The experiment followed two main stages: initially, the authors tested several model variants to establish a baseline, involving the identification of the version which performed better than a random-ordering approach.

Second, they tested large language models in generative mode, by comparing their output to a stronger baseline, rather than a random ranking, raising the difficulty of the task.

This meant the models had to do better than a system that already showed some ability to predict which movies would become popular. As a result, the authors assert, the evaluation better reflected real-world conditions, where editorial teams and recommender systems are rarely choosing between a model and chance, but between competing systems with varying levels of predictive ability.

The Advantage of Ignorance

A key constraint in this setup was the time gap between the models’ knowledge cutoff and the actual release dates of the movies. Because the language models were trained on data that ended six to twelve months before the movies became available, they had no access to post-release information, ensuring that the predictions were based entirely on metadata, and not on any learned audience response.

Baseline Evaluation

To construct a baseline, the authors generated semantic representations of movie metadata using three embedding models: BERT V4; Linq-Embed-Mistral 7B; and Llama 3.3 70B, quantized to 8-bit precision to meet the constraints of the experimental environment.

Linq-Embed-Mistral was selected for inclusion due to its top position on the MTEB (Massive Text Embedding Benchmark) leaderboard.

Each model produced vector embeddings of candidate movies, which were then compared to the average embedding of the top one hundred most popular titles from the weeks preceding each movie’s release.

Popularity was inferred using cosine similarity between these embeddings, with higher similarity scores indicating higher predicted appeal. The ranking accuracy of each model was evaluated by measuring performance against a random ordering baseline.

Performance improvement of Popular Embedding models compared to a random baseline. Each model was tested using four metadata configurations: V1 includes only genre; V2 includes only synopsis; V3 combines genre, synopsis, content rating, character types, mood, and release era; V4 adds cast, crew, and awards to the V3 configuration. Results show how richer metadata inputs affect ranking accuracy. Source: https://arxiv.org/pdf/2505.02693

The results (shown above), demonstrate that BERT V4 and Linq-Embed-Mistral 7B delivered the strongest improvements in identifying the top three most popular titles, although both fell slightly short in predicting the single most popular item.

BERT was ultimately selected as the baseline model for comparison with the LLMs, as its efficiency and overall gains outweighed its limitations.

LLM Evaluation

The researchers assessed performance using two ranking approaches: pairwise and listwise. Pairwise ranking evaluates whether the model correctly orders one item relative to another; and listwise ranking considers the accuracy of the entire ordered list of candidates.

This combination made it possible to evaluate not only whether individual movie pairs were ranked correctly (local accuracy), but also how well the full list of candidates reflected the true popularity order (global accuracy).

Full, non-quantized models were employed to prevent performance loss, ensuring a consistent and reproducible comparison between LLM-based predictions and embedding-based baselines.

Metrics

To assess how effectively the language models predicted movie popularity, both ranking-based and classification-based metrics were used, with particular attention to identifying the top three most popular titles.

Four metrics were applied: Accuracy@1 measured how often the most popular item appeared in the first position; Reciprocal Rank captured how high the top actual item ranked in the predicted list by taking the inverse of its position; Normalized Discounted Cumulative Gain (NDCG@k) evaluated how well the entire ranking matched actual popularity, with higher scores indicating better alignment; and Recall@3 measured the proportion of truly popular titles that appeared in the model’s top three predictions.

Since most user engagement happens near the top of ranked menus, the evaluation focused on lower values of k, to reflect practical use cases.

Performance improvement of large language models over BERT V4, measured as percentage gains across ranking metrics. Results were averaged over ten runs per model-prompt combination, with the top two values highlighted. Reported figures reflect the average percentage improvement across all metrics.

The performance of Llama model 3.1 (8B), 3.1 (405B), and 3.3 (70B) was evaluated by measuring metric improvements relative to the earlier-established BERT V4 baseline. Each model was tested using a series of prompts, ranging from minimal to information-rich, to examine the effect of input detail on prediction quality.

The authors state:

‘The best performance is achieved when using Llama 3.1 (405B) with the most informative prompt, followed by Llama 3.3 (70B). Based on the observed trend, when using a complex and lengthy prompt (MD V4), a more complex language model generally leads to improved performance across various metrics. However, it is sensitive to the type of information added.’

Performance improved when cast awards were included as part of the prompt – in this case, the number of major awards received by the top five billed actors in each film. This richer metadata was part of the most detailed prompt configuration, outperforming a simpler version that excluded cast recognition. The benefit was most evident in the larger models, Llama 3.1 (405B) and 3.3 (70B), both of which showed stronger predictive accuracy when given this additional signal of prestige and audience familiarity.

By contrast, the smallest model, Llama 3.1 (8B), showed improved performance as prompts became slightly more detailed, progressing from genre to synopsis, but declined when more fields were added, suggesting that the model lacked the capacity to integrate complex prompts effectively, leading to weaker generalization.

When prompts were restricted to genre alone, all models under-performed against the baseline, demonstrating that limited metadata was insufficient to support meaningful predictions.

Conclusion

LLMs have become the poster child for generative AI, which might explain why they’re being put to work in areas where other methods could be a better fit. Even so, there’s still a lot we don’t know about what they can do across different industries, so it makes sense to give them a shot.

In this particular case, as with stock markets and weather forecasting, there is only a limited extent to which historical data can serve as the foundation of future predictions. In the case of movies and TV shows, the very delivery method is now a moving target, in contrast to the period between 1978-2011, when cable, satellite and portable media (VHS, DVD, et al.) represented a series of transitory or evolving historical disruptions.

Neither can any prediction method account for the extent to which the success or failure of other productions may influence the viability of a proposed property – and yet this is frequently the case in the movie and TV industry, which loves to ride a trend.

Nonetheless, when used thoughtfully, LLMs could help strengthen recommendation systems during the cold-start phase, offering useful support across a range of predictive methods.

First published Tuesday, May 6, 2025

[roryparis]

stochastic gradient descent is literally my number one opp

[ixnai]

AI is not omnipotent. In the realm of artificial intelligence, the allure of a panacea is a persistent mirage. The multifarious nature of AI systems, built upon intricate layers of algorithms and data, often leads to misconceptions about their capabilities. These systems, though advanced, are not infallible solutions to every problem.

At the core of AI lies the convolutional neural network (CNN), a sophisticated architecture designed to mimic the human brain’s visual cortex. While CNNs excel at image recognition, their prowess is limited by the quality and diversity of their training data. A CNN trained on a narrow dataset will falter when faced with unfamiliar inputs, much like a linguist fluent in only one dialect.

Moreover, the stochastic gradient descent (SGD) algorithm, a cornerstone of machine learning, optimizes AI models by iteratively adjusting parameters to minimize error. However, SGD is susceptible to local minima, where it may converge on suboptimal solutions. This is akin to a hiker mistaking a hill for the peak of a mountain, unaware of the higher summits beyond.

The complexity of AI systems is further compounded by the black-box nature of deep learning models. These models, with their labyrinthine layers of neurons, often defy human interpretability. This opacity poses significant challenges in critical applications such as healthcare, where understanding the rationale behind a diagnosis is as crucial as the diagnosis itself.

Furthermore, AI’s reliance on vast computational resources and energy consumption raises concerns about sustainability. The carbon footprint of training a single large-scale model can rival that of multiple transatlantic flights, highlighting the environmental cost of AI’s computational hunger.

In the realm of natural language processing, transformer models like GPT-3 demonstrate remarkable fluency in generating human-like text. Yet, they are not devoid of biases, as they inherit the prejudices present in their training data. This is akin to a parrot, eloquent yet uncomprehending, echoing the sentiments of its environment without discernment.

AI’s limitations are not merely technical but also ethical. The deployment of AI in surveillance, for instance, raises profound questions about privacy and autonomy. The specter of algorithmic bias looms large, threatening to perpetuate systemic inequalities under the guise of objectivity.

In conclusion, while AI is a powerful tool, it is not a magic bullet. Its multifaceted complexity demands a nuanced understanding of its capabilities and limitations. As we navigate the frontier of artificial intelligence, we must temper our expectations and approach its deployment with caution and critical scrutiny. AI, in its current form, is not a panacea, but rather a sophisticated instrument that requires judicious application and oversight.

[thousandflowerscampaign]

The Paradox of Probabilistic and Deterministic Worlds in AI and Quantum Computing

In the fascinating realms of artificial intelligence (AI) and quantum computing, a curious paradox emerges when we examine the interplay between algorithms and hardware. AI algorithms are inherently probabilistic, while the hardware they run on is deterministic. Conversely, quantum algorithms are deterministic, yet the hardware they rely on is probabilistic. This duality highlights the unique challenges and opportunities in these cutting-edge fields.

AI: Probabilistic Algorithms on Deterministic Hardware

AI algorithms, particularly those in machine learning, often rely on probabilistic methods to make predictions or decisions. Techniques like Bayesian inference, stochastic gradient descent, and Monte Carlo simulations are rooted in probability theory. These algorithms embrace uncertainty, using statistical models to approximate solutions where exact answers are computationally infeasible.

However, the hardware that executes these algorithms—traditional CPUs and GPUs—is deterministic. These processors follow precise instructions and produce predictable outcomes for given inputs. The deterministic nature of classical hardware ensures reliability and reproducibility, which are crucial for debugging and scaling AI systems. Yet, this mismatch between probabilistic algorithms and deterministic hardware can lead to inefficiencies, as the hardware isn't inherently designed to handle uncertainty.

Quantum Computing: Deterministic Algorithms on Probabilistic Hardware

In contrast, quantum computing presents an inverse scenario. Quantum algorithms, such as Shor's algorithm for factoring integers or Grover's algorithm for search problems, are deterministic. They are designed to produce specific, correct outcomes when executed correctly. However, the quantum hardware that runs these algorithms is inherently probabilistic.

Quantum bits (qubits) exist in superpositions of states, and their measurements yield probabilistic results. This probabilistic nature arises from the fundamental principles of quantum mechanics, such as superposition and entanglement. While quantum algorithms are designed to harness these phenomena to solve problems more efficiently than classical algorithms, the hardware's probabilistic behavior introduces challenges in error correction and result verification.

Bridging the Gap

The dichotomy between probabilistic algorithms and deterministic hardware in AI, and deterministic algorithms and probabilistic hardware in quantum computing, underscores the need for innovative approaches to bridge these gaps. In AI, researchers are exploring neuromorphic and probabilistic computing architectures that better align with the probabilistic nature of AI algorithms. These hardware innovations aim to improve efficiency and performance by embracing uncertainty at the hardware level.

In quantum computing, advancements in error correction and fault-tolerant designs are crucial to mitigate the probabilistic nature of quantum hardware. Techniques like quantum error correction codes and surface codes are being developed to ensure reliable and deterministic outcomes from quantum algorithms.

Conclusion

The interplay between probabilistic and deterministic elements in AI and quantum computing reveals the intricate balance required to harness the full potential of these technologies. As we continue to push the boundaries of computation, understanding and addressing these paradoxes will be key to unlocking new possibilities and driving innovation in both fields. Whether it's designing hardware that aligns with the probabilistic nature of AI or developing methods to tame the probabilistic behavior of quantum hardware, the journey promises to be as exciting as the destination.

[programmingandengineering]

ECE421 - Assignment 1: Logistic Regression

Objectives: In this assignment, you will rst implement a simple logistic regression classi er using Numpy and train your model by applying (Stochastic) Gradient Descent algorithm. Next, you will implement the same model, this time in TensorFlow and use Stochastic Gradient Descent and ADAM to train your model. You are encouraged to look up TensorFlow APIs for useful utility functions, at:…

[codeshive]

BME646 and ECE60146: Homework 3

The goal of this homework is for you to develop a greater appreciation for the step-size optimization logic that is ubiquitous in training deep neural networks. To that end, this homework will first ask you to execute the scripts in the Examples directory of your instructor’s CGP class that are based on a vanilla implementation of SGD (Stochastic Gradient Descent). Subsequently, you will be asked…

[ingoampt]

Day 6 _ Why the Normal Equation Works Without Gradient Descent

Understanding Linear Regression: The Normal Equation and Matrix Multiplications Explained Understanding Linear Regression: The Normal Equation and Matrix Multiplications Explained Linear regression is a fundamental concept in machine learning and statistics, used to predict a target variable based on one or more input features. While gradient descent is a popular method for finding the…

[learning-code-ficusoft]

Common Pitfalls in Machine Learning and How to Avoid Them

Selecting and training algorithms is a key step in building machine learning models. 

Here’s a brief overview of the process: 

Selecting the Right Algorithm The choice of algorithm depends on the type of problem you’re solving (e.g., classification, regression, clustering, etc.), the size and quality of your data, and the computational resources available. 

Common algorithm choices include: 

For Classification: Logistic Regression Decision Trees Random Forests Support Vector Machines (SVM) k-Nearest Neighbors (k-NN) Neural 

Networks For Regression: Linear Regression Decision Trees Random Forests Support Vector Regression (SVR) Neural Networks For Clustering: 

k-Means DBSCAN Hierarchical Clustering For Dimensionality Reduction: Principal Component Analysis (PCA) t-Distributed Stochastic Neighbor Embedding (t-SNE) 

Considerations when selecting an algorithm: 

Size of data: 

Some algorithms scale better with large datasets (e.g., Random Forests, Gradient Boosting). 

Interpretability: 

If understanding the model is important, simpler models (like Logistic Regression or Decision Trees) might be preferred. 

Performance:

 Test different algorithms and use cross-validation to compare performance (accuracy, precision, recall, etc.). 

2. Training the Algorithm After selecting an appropriate algorithm, you need to train it on your dataset. 

Here’s how you can train an algorithm:

 Preprocess the data: 

Clean the data (handle missing values, outliers, etc.). Normalize/scale the features (especially important for algorithms like SVM or k-NN). 

Encode categorical variables if necessary (e.g., using one-hot encoding). 

Split the data:

 Divide the data into training and test sets (typically 80–20 or 70–30 split). 

Train the model: 

Fit the model to the training data using the chosen algorithm and its hyperparameters. Optimize the hyperparameters using techniques like Grid Search or Random Search. 

Evaluate the model: Use the test data to evaluate the model’s performance using metrics like accuracy, precision, recall, F1 score (for classification), mean squared error (for regression), etc. 

Perform cross-validation to get a more reliable performance estimate. 

3. Model Tuning and Hyperparameter Optimization Hyperparameter tuning: Many algorithms come with hyperparameters that affect their performance (e.g., the depth of a decision tree, learning rate for gradient descent). 

You can use methods like: Grid Search: 

Try all possible combinations of hyperparameters within a given range. 

Random Search: 

Randomly sample hyperparameters from a range, which is often more efficient for large search spaces. 

Cross-validation: 

Use k-fold cross-validation to get a better understanding of how the model generalizes to unseen data. 

4. Model Evaluation and Fine-tuning Once you have trained the model, fine-tune it by adjusting hyperparameters or using advanced techniques like regularization to avoid overfitting. 

If the model isn’t performing well, try:

 Selecting different features. 

Trying more advanced models (e.g., ensemble methods like Random Forest or Gradient Boosting).

Gathering more data if possible. 

By iterating through these steps and refining the model based on evaluation, you can build a robust machine learning model for your problem.

WEBSITE: https://www.ficusoft.in/data-science-course-in-chennai/

[myprogrammingsolver]

ECE421 - Assignment 1: Logistic Regression

Objectives: In this assignment, you will rst implement a simple logistic regression classi er using Numpy and train your model by applying (Stochastic) Gradient Descent algorithm. Next, you will implement the same model, this time in TensorFlow and use Stochastic Gradient Descent and ADAM to train your model. You are encouraged to look up TensorFlow APIs for useful utility functions, at:…

[granthjain]

Neural Networks and Deep Learning: Transforming the Digital World

Neural Networks and Deep Learning: Revolutionizing the Digital World

In the past decade or so, neural networks and deep learning have revolutionized the field of artificial intelligence (AI), making possible machines that can recognize images, translate languages, diagnose diseases, or even drive cars. These two technologies are at the backbone of modern AI systems: powering what was previously considered pure science fiction.

In this blog, we will dive deep into the world of neural networks and deep learning, unraveling their intricacies, exploring their applications, and understanding why they have become pivotal in shaping the future of technology.

What Are Neural Networks?

At its heart, a neural network is a computation model that draws inspiration from the human brain's structure and function. It is composed of nodes or neurons that are linked in layers. These networks operate on data by allowing it to pass through layers where patterns are learned, and decisions or predictions are made based on the input.

Structure of a Neural Network

A typical neural network is composed of three types of layers:

Input Layer: The raw input is given to the network at this stage. Every neuron in this layer signifies a feature of the input data.

Hidden Layers: These layers do most of the computation. Each neuron in a hidden layer applies a mathematical function to the inputs and passes the result to the next layer. The complexity and depth of these layers determine the network's ability to model intricate patterns.

Output Layer: The final layer produces the network's prediction or decision, such as classifying an image or predicting a number.

Connections between neurons have weights. These weights are the objects of training to make sure predictions become less erroneous.

What is Deep Learning?

Deep learning refers to a subset of machine learning that uses artificial neural networks with many layers, called hidden layers. It has "deep" referring to this multiplicity of layers so as to learn hierarchical representations of the data. For example:

In image recognition, the initial layers may detect edges and textures while deeper layers of recognition happen for shapes and objects as well as sophisticated patterns.

In the natural language processing, learning grammar, syntax, semantics, and even context may occur in layers overtime.

Deep learning flourishes on great datasets and computational power thus perfecting the solution where traditional algorithms fail.

The steps of a neural network operation can be described as follows:

1. Forward Propagation

Input data flows through the network, layer by layer, and performs calculations at each neuron. Calculations include:

Weighted Sum: ( z = \sum (w \cdot x) + b ), where ( w ) denotes weights, ( x ) denotes inputs, and ( b ) is the bias term.

Activation Function: Non-linear function like ReLU, sigmoid, or tanh to introduce non-linearity to allow the network to model complex patterns.

The output of this process is the prediction made by the network.

Loss Calculation The prediction made by the network is compared to the actual target by means of a loss function that calculates the error between the prediction and the actual target. The most commonly used loss functions are the Mean Squared Error for regression problems and Cross-Entropy Loss for classification problems.

3. Backpropagation

To improve predictions, the network adjusts its weights and biases through backpropagation. This involves:

Calculating the gradient of the loss function with respect to each weight.

Updating the weights using optimization algorithms like Stochastic Gradient Descent (SGD) or Adam Optimizer.

4. Iteration

The process of forward propagation, loss calculation, and backpropagation repeats over multiple iterations (or epochs) until the network achieves acceptable performance.

Key Components of Deep Learning

Deep learning involves several key components that make it effective:

1. Activation Functions

Activation functions determine the output of neurons. Popular choices include:

ReLU (Rectified Linear Unit): Outputs zero for negative inputs and the input value for positive inputs.

Sigmoid: Maps inputs to a range between 0 and 1, often used in binary classification.

Tanh: Maps inputs to a range between -1 and 1, useful for certain regression tasks.

2. Optimization Algorithms Optimization algorithms adjust the weights in a manner to reduce the loss. A few widely used algorithms include:

Gradient Descent: Iterative updating of the weights along the steepest gradient descent. Adam Optimizer: Combines the best features of SGD and RMSProp to achieve faster convergence.

**3. Regularization Techniques To avoid overfitting-the model performs well on training data but poorly on unseen data-techniques such as dropout, L2 regularization, and data augmentation are utilized.

4. Loss Functions

Loss functions control the training procedure by measuring errors. Some common ones are:

Mean Squared Error (MSE) in regression tasks.

Binary Cross-Entropy in binary classification.

Categorical Cross-Entropy in multi-class classification.

The versatility of neural networks and deep learning has led to their adoption in numerous domains. Let's explore some of their most impactful applications:

1. Computer Vision

Deep learning has transformed computer vision, enabling machines to interpret visual data with remarkable accuracy. Applications include:

Image Recognition: Identifying objects, faces, or animals in images.

Medical Imaging: Diagnosing diseases from X-rays, MRIs, and CT scans.

Autonomous Vehicles: Cameras, sensors to detect and understand the layout of roads

2. Natural Language Processing (NLP)

In the NLP application, the deep learning powering these systems and enabling them to understand or generate human language:

Language Translation: Using Neural Networks of Google Translate Chatbots: These conversational AI systems using NLP systems to talk with users, in their preferred language of choice Sentiment Analysis: Ability to analyze and identify any emotions and opinions in written text.

3. **Speech Recognition

Voice assistants like Siri, Alexa, and Google Assistant rely on deep learning for tasks like speech-to-text conversion and natural language understanding.

4. Healthcare

Deep learning has made significant strides in healthcare, with applications such as:

Drug Discovery: Accelerating the identification of potential drug candidates.

Predictive Analytics: Forecasting patient outcomes and detecting early signs of diseases.

5. Gaming and Entertainment

Neural networks create better gaming experiences with realistic graphics, intelligent NPC behavior, and procedural content generation.

6. Finance

In finance, deep learning is applied in fraud detection, algorithmic trading, and credit scoring.

Challenges in Neural Networks and Deep Learning

Despite the great potential for change, neural networks and deep learning are plagued by the following challenges:

1. **Data Requirements

Deep learning models need a huge amount of labeled data to be trained. In many instances, obtaining and labeling that data is expensive and time-consuming.

2. Computational Cost

Training deep networks is highly demanding in terms of computational requirements: GPUs and TPUs can be expensive.

3. Interpretability

Neural networks are known as "black boxes" because their decision-making mechanisms are not easy to understand.

4. Overfitting

Deep models can overfit training data, especially with small or imbalanced datasets.

5. Ethical Concerns

Facial recognition and autonomous weapons are applications of deep learning that raise ethical and privacy concerns.

The Future of Neural Networks and Deep Learning

The future is bright for neural networks and deep learning. Some promising trends include:

1. Federated Learning

This will allow training models on decentralized data, such as that found on users' devices, with privacy preserved.

2. Explainable AI (XAI)

Research is ongoing to make neural networks more transparent and interpretable so that trust can be developed in AI systems.

3. Energy Efficiency

Research is now underway to reduce the energy consumed by deep learning models to make AI more sustainable.

4. **Integration with Other Technologies

Integrating deep learning with things like quantum computing and IoT unlocks new possibilities.

Conclusion

Neural networks and deep learning mark a whole new era in technological innovation. Problems once considered unsolvable were, through these technologies and their ability to mimic the learning curves and adaptation of the human brain, enabled machines to perceive the world, understand it, and then interact within it.

As we continue to develop these systems, their applications will go further to transform industries and improve lives. But along with that progress comes the challenges and ethical implications of this technology. We need to ensure that its benefits are harnessed responsibly and equitably.

These concepts open up endless possibilities; with this rapidly changing technology, we are still scraping off the surface of potential possibilities in neural networks and deep learning.

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