#hyperparameters

[chronomally]

I think my favorite part of learning AI/ML stuff is that teaching you about it requires admitting that some of this shit is just a very elaborate shell game

[fuerst-von-plan1]

Revolution der Datenanalyse: Maschinelles Lernen im Fokus

Die Datenanalyse hat sich in den letzten Jahren zu einer der Schlüsseltechnologien in der modernen Wirtschaft entwickelt. Mit der zunehmenden Menge an verfügbaren Daten wird die Fähigkeit, diese effektiv zu analysieren und wertvolle Erkenntnisse daraus zu gewinnen, immer wichtiger. Im Zentrum dieser Entwicklung steht das maschinelle Lernen, ein Bereich der künstlichen Intelligenz, der es…

[ingoampt]

Hyperparameter Tuning and Neural Network Architectures, eg : Bayesian Optimization _ day 19

In-Depth Exploration of Hyperparameter Tuning and Neural Network Architectures In-Depth Exploration of Hyperparameter Tuning and Neural Network Architectures 1. Bayesian Optimization for Hyperparameter Tuning What is Bayesian Optimization? Bayesian Optimization is an advanced method used to optimize hyperparameters by creating a probabilistic model, typically a Gaussian Process, of the function…

[veris-studies]

21/06/2025 | Saturday

✓ finished annotating another hundred images. this ended up taking most of the morning and afternoon

✓ trained the model a couple more times with various hyperparameters

✓ did a 40ish min workout

✓ made some changes to my portfolio website

i didn't study for the exam 🫠 but im going out to grab lunch with some friends tomorrow, so i wanted to finish working on the object detection model tonight. since my project supervisor hasn't told me my next steps yet, ill study for the exam tomorrow morning, for sureee.

[aorish]

whelp, hyperparameter tuning on some free cloud time, guess it's time to watch anime

[compneuropapers]

Interesting Papers for Week 5, 2025

Weak overcomes strong in sensory integration: shading warps the disparity field. Aubuchon, C., Kemp, J., Vishwanath, D., & Domini, F. (2024). Proceedings of the Royal Society B: Biological Sciences, 291(2033).

Functional networks of inhibitory neurons orchestrate synchrony in the hippocampus. Bocchio, M., Vorobyev, A., Sadeh, S., Brustlein, S., Dard, R., Reichinnek, S., … Cossart, R. (2024). PLOS Biology, 22(10), e3002837.

Time-dependent neural arbitration between cue associative and episodic fear memories. Cortese, A., Ohata, R., Alemany-González, M., Kitagawa, N., Imamizu, H., & Koizumi, A. (2024). Nature Communications, 15, 8706.

Neural correlates of memory in a naturalistic spatiotemporal context. Dougherty, M. R., Chang, W., Rudoler, J. H., Katerman, B. S., Halpern, D. J., Bruska, J. P., … Kahana, M. J. (2024). Journal of Experimental Psychology: Learning, Memory, and Cognition, 50(9), 1404–1420.

Massive perturbation of sound representations by anesthesia in the auditory brainstem. Gosselin, E., Bagur, S., & Bathellier, B. (2024). Science Advances, 10(42).

Between-area communication through the lens of within-area neuronal dynamics. Gozel, O., & Doiron, B. (2024). Science Advances, 10(42).

Brainstem inhibitory neurons enhance behavioral feature selectivity by sharpening the tuning of excitatory neurons. He, Y., Chou, X., Lavoie, A., Liu, J., Russo, M., & Liu, B. (2024). Current Biology, 34(20), 4623-4638.e8.

Human motor learning dynamics in high-dimensional tasks. Kamboj, A., Ranganathan, R., Tan, X., & Srivastava, V. (2024). PLOS Computational Biology, 20(10), e1012455.

Distinct functions for beta and alpha bursts in gating of human working memory. Liljefors, J., Almeida, R., Rane, G., Lundström, J. N., Herman, P., & Lundqvist, M. (2024). Nature Communications, 15, 8950.

Regularizing hyperparameters of interacting neural signals in the mouse cortex reflect states of arousal. Lyamzin, D. R., Alamia, A., Abdolrahmani, M., Aoki, R., & Benucci, A. (2024). PLOS Computational Biology, 20(10), e1012478.

Differential role of NMDA receptors in hippocampal‐dependent spatial memory and plasticity in juvenile male and female rats. Narattil, N. R., & Maroun, M. (2024). Hippocampus, 34(11), 564–574.

Dynamic patterns of functional connectivity in the human brain underlie individual memory formation. Phan, A. T., Xie, W., Chapeton, J. I., Inati, S. K., & Zaghloul, K. A. (2024). Nature Communications, 15, 8969.

Computational processes of simultaneous learning of stochasticity and volatility in humans. Piray, P., & Daw, N. D. (2024). Nature Communications, 15, 9073.

Ordinal information, but not metric information, matters in binding feature with depth location in three-dimensional contexts. Qian, J., Zheng, T., & Li, B. (2024). Journal of Experimental Psychology: Human Perception and Performance, 50(11), 1083–1099.

Hippocampal storage and recall of neocortical “What”–“Where” representations. Rolls, E. T., Zhang, C., & Feng, J. (2024). Hippocampus, 34(11), 608–624.

Roles and interplay of reinforcement-based and error-based processes during reaching and gait in neurotypical adults and individuals with Parkinson’s disease. Roth, A. M., Buggeln, J. H., Hoh, J. E., Wood, J. M., Sullivan, S. R., Ngo, T. T., … Cashaback, J. G. A. (2024). PLOS Computational Biology, 20(10), e1012474.

Integration of rate and phase codes by hippocampal cell-assemblies supports flexible encoding of spatiotemporal context. Russo, E., Becker, N., Domanski, A. P. F., Howe, T., Freud, K., Durstewitz, D., & Jones, M. W. (2024). Nature Communications, 15, 8880.

The one exception: The impact of statistical regularities on explicit sense of agency. Seubert, O., van der Wel, R., Reis, M., Pfister, R., & Schwarz, K. A. (2024). Journal of Experimental Psychology: Human Perception and Performance, 50(11), 1067–1082.

The brain hierarchically represents the past and future during multistep anticipation. Tarder-Stoll, H., Baldassano, C., & Aly, M. (2024). Nature Communications, 15, 9094.

Expectancy-related changes in firing of dopamine neurons depend on hippocampus. Zhang, Z., Takahashi, Y. K., Montesinos-Cartegena, M., Kahnt, T., Langdon, A. J., & Schoenbaum, G. (2024). Nature Communications, 15, 8911.

[phaeton-flier]

Guys, it'll be fine, Trump just set the learning step size hyperparameter too high so it's jumping around a lot. Give it a few million iterations and we'll be fine

[jbird-the-manwich]

Thoughts on AI?

I've written at least a grimoire and a half in the last two years in the form of a dataset to make my grimoire into a bot that talks without the need for retrieval augmented generation. Also falcon4 3b is more impressive than deepseekr1 for the way it infers against complex logic more accurately than some 7b+ param models (looking at you, gemma 9b) but no one listens to witches. Also if you decide to finetune one don't overlook gradient accumulation as a tunable hyperparameter, and don't assume a higher learning rate is always better, with a 2b parameter SLM and ~22,000,000 tuneable neurons in peft, I get better fits from 2e-5 than anything, training for 3 epochs. lowering the learning rate a bit and boosting gradient accumulation if you have none of it can sometimes mean the difference between a run that won't converge at all and one that more or less slides over the curves like a goddamn silk nighty. Also dataset design and function engineering is actually everything but no one listens to witches. Prune and heal is *also* everything but no one listens to witches. Chatgpt has never been state of the art in optimization or resource usage, and does not represent the entire field of machine learning as such, name a problem with AI, there's an open model somewhere that's already fixed it, but no one listens to witches. Biggest problem with people is they're afraid of anything they don't already perfectly understand, and they're too freaked out to engage the part of the brain that reads documentation, but no one. listens. to witches.

Mostly I just wish people that burn a hole through the planet lipsyncing in their kitchens on tiktok would stop showing up to tell people programming machine learning models we're destroying the ecosystem when we've had single gpu, zero gpu training for like... two years now? We can literally finetune on desktops. or a single gpu. They think programming an AI means giving their fanfic to chatgpt. Meanwhile they act like puritans walking in on a fisting orgy if you so much as mention a language model cuz they think "AI" means openai and base all their opinions (on what the fuck *I'm* doing? For some reason?) on poorly researched headlines from 2 years ago. 30 gallons of water per question, they'll say. Doesn't matter that that was never accurate, and even if it was for chatgpt, (wasnt tho) it sure as fuck isn't for an SLM.

I really wish I was a bastard,tbh, they're so dumb. somebody should really be scamming them. I don't have time, but, someone should. I would like for them to suffer in exchange for making the discourse about ai 99% superstitious virtue signaling autofellatio and 0.0001 percent things that are actually true because some of us are nerds and we program for fun and that means when a system drops we fingerbang that. they're posting on tiktok literally heating entire server farms for algorithmically assured likes. I'll tell you one thing, my carbon footprint is a damn sight smaller than these mfkrs that buzz around piling on to spit long disproven "facts" about a technology they fear too much to correct themselves about. "watch the video to the end" they say. "it helps the algorithm" they say. "AI is destroying the planet" they say, like their aren't levels to the shit. Cuz we all know high definition video streaming doesn't even require a gpu, you can totally encode 4k with like two stanley cups and a pair of entangled photons. or so they seem to think. dumbasses.

[deepdrearn]

The author’s lone goal is to show that the entire field might have evolved a different direction if we had instead been obsessed with a slightly different acronym and slightly different result. We take a previously strong language model based only on boring LSTMs and get it to within a stone’s throw of a stone’s throw of state-of-the-art byte level language model results on enwik8. This work has undergone no intensive hyperparameter optimization and lived entirely on a commodity desktop machine that made the author’s small studio apartment far too warm in the midst of a San Franciscan summer. The final results are achievable in plus or minus 24 hours on a single GPU as the author is impatient.

[govindhtech]

AI Frameworks Help Data Scientists For GenAI Survival

AI Frameworks: Crucial to the Success of GenAI

Develop Your AI Capabilities Now

You play a crucial part in the quickly growing field of generative artificial intelligence (GenAI) as a data scientist. Your proficiency in data analysis, modeling, and interpretation is still essential, even though platforms like Hugging Face and LangChain are at the forefront of AI research.

Although GenAI systems are capable of producing remarkable outcomes, they still mostly depend on clear, organized data and perceptive interpretation areas in which data scientists are highly skilled. You can direct GenAI models to produce more precise, useful predictions by applying your in-depth knowledge of data and statistical techniques. In order to ensure that GenAI systems are based on strong, data-driven foundations and can realize their full potential, your job as a data scientist is crucial. Here’s how to take the lead:

Data Quality Is Crucial

The effectiveness of even the most sophisticated GenAI models depends on the quality of the data they use. By guaranteeing that the data is relevant, AI tools like Pandas and Modin enable you to clean, preprocess, and manipulate large datasets.

Analysis and Interpretation of Exploratory Data

It is essential to comprehend the features and trends of the data before creating the models. Data and model outputs are visualized via a variety of data science frameworks, like Matplotlib and Seaborn, which aid developers in comprehending the data, selecting features, and interpreting the models.

Model Optimization and Evaluation

A variety of algorithms for model construction are offered by AI frameworks like scikit-learn, PyTorch, and TensorFlow. To improve models and their performance, they provide a range of techniques for cross-validation, hyperparameter optimization, and performance evaluation.

Model Deployment and Integration

Tools such as ONNX Runtime and MLflow help with cross-platform deployment and experimentation tracking. By guaranteeing that the models continue to function successfully in production, this helps the developers oversee their projects from start to finish.

Intel’s Optimized AI Frameworks and Tools

The technologies that developers are already familiar with in data analytics, machine learning, and deep learning (such as Modin, NumPy, scikit-learn, and PyTorch) can be used. For the many phases of the AI process, such as data preparation, model training, inference, and deployment, Intel has optimized the current AI tools and AI frameworks, which are based on a single, open, multiarchitecture, multivendor software platform called oneAPI programming model.

Data Engineering and Model Development:

To speed up end-to-end data science pipelines on Intel architecture, use Intel’s AI Tools, which include Python tools and frameworks like Modin, Intel Optimization for TensorFlow Optimizations, PyTorch Optimizations, IntelExtension for Scikit-learn, and XGBoost.

Optimization and Deployment

For CPU or GPU deployment, Intel Neural Compressor speeds up deep learning inference and minimizes model size. Models are optimized and deployed across several hardware platforms including Intel CPUs using the OpenVINO toolbox.

You may improve the performance of your Intel hardware platforms with the aid of these AI tools.

Library of Resources

Discover collection of excellent, professionally created, and thoughtfully selected resources that are centered on the core data science competencies that developers need. Exploring machine and deep learning AI frameworks.

What you will discover:

Use Modin to expedite the extract, transform, and load (ETL) process for enormous DataFrames and analyze massive datasets.

To improve speed on Intel hardware, use Intel’s optimized AI frameworks (such as Intel Optimization for XGBoost, Intel Extension for Scikit-learn, Intel Optimization for PyTorch, and Intel Optimization for TensorFlow).

Use Intel-optimized software on the most recent Intel platforms to implement and deploy AI workloads on Intel Tiber AI Cloud.

How to Begin

Frameworks for Data Engineering and Machine Learning

Step 1: View the Modin, Intel Extension for Scikit-learn, and Intel Optimization for XGBoost videos and read the introductory papers.

Modin: To achieve a quicker turnaround time overall, the video explains when to utilize Modin and how to apply Modin and Pandas judiciously. A quick start guide for Modin is also available for more in-depth information.

Scikit-learn Intel Extension: This tutorial gives you an overview of the extension, walks you through the code step-by-step, and explains how utilizing it might improve performance. A movie on accelerating silhouette machine learning techniques, PCA, and K-means clustering is also available.

Intel Optimization for XGBoost: This straightforward tutorial explains Intel Optimization for XGBoost and how to use Intel optimizations to enhance training and inference performance.

Step 2: Use Intel Tiber AI Cloud to create and develop machine learning workloads.

On Intel Tiber AI Cloud, this tutorial runs machine learning workloads with Modin, scikit-learn, and XGBoost.

Step 3: Use Modin and scikit-learn to create an end-to-end machine learning process using census data.

Run an end-to-end machine learning task using 1970–2010 US census data with this code sample. The code sample uses the Intel Extension for Scikit-learn module to analyze exploratory data using ridge regression and the Intel Distribution of Modin.

Deep Learning Frameworks

Step 4: Begin by watching the videos and reading the introduction papers for Intel’s PyTorch and TensorFlow optimizations.

Intel PyTorch Optimizations: Read the article to learn how to use the Intel Extension for PyTorch to accelerate your workloads for inference and training. Additionally, a brief video demonstrates how to use the addon to run PyTorch inference on an Intel Data Center GPU Flex Series.

Intel’s TensorFlow Optimizations: The article and video provide an overview of the Intel Extension for TensorFlow and demonstrate how to utilize it to accelerate your AI tasks.

Step 5: Use TensorFlow and PyTorch for AI on the Intel Tiber AI Cloud.

In this article, it show how to use PyTorch and TensorFlow on Intel Tiber AI Cloud to create and execute complicated AI workloads.

Step 6: Speed up LSTM text creation with Intel Extension for TensorFlow.

The Intel Extension for TensorFlow can speed up LSTM model training for text production.

Step 7: Use PyTorch and DialoGPT to create an interactive chat-generation model.

Discover how to use Hugging Face’s pretrained DialoGPT model to create an interactive chat model and how to use the Intel Extension for PyTorch to dynamically quantize the model.

Read more on Govindhtech.com

[mvishnukumar]

What are some challenging concepts for beginners learning data science, such as statistics and machine learning?

Hi,

For beginners in data science, several concepts can be challenging due to their complexity and depth. 

Here are some of the most common challenging concepts in statistics and machine learning:

Statistics:

Probability Distributions: Understanding different probability distributions (e.g., normal, binomial, Poisson) and their properties can be difficult. Knowing when and how to apply each distribution requires a deep understanding of their characteristics and applications.

Hypothesis Testing: Hypothesis testing involves formulating null and alternative hypotheses, selecting appropriate tests (e.g., t-tests, chi-square tests), and interpreting p-values. The concepts of statistical significance and Type I/Type II errors can be complex and require careful consideration.

Confidence Intervals: Calculating and interpreting confidence intervals for estimates involves understanding the trade-offs between precision and reliability. Beginners often struggle with the concept of confidence intervals and their implications for statistical inference.

Regression Analysis: Multiple regression analysis, including understanding coefficients, multicollinearity, and model assumptions, can be challenging. Interpreting regression results and diagnosing issues such as heteroscedasticity and autocorrelation require a solid grasp of statistical principles.

Machine Learning:

Bias-Variance Tradeoff: Balancing bias and variance to achieve a model that generalizes well to new data can be challenging. Understanding overfitting and underfitting, and how to use techniques like cross-validation to address these issues, requires careful analysis.

Feature Selection and Engineering: Selecting the most relevant features and engineering new ones can significantly impact model performance. Beginners often find it challenging to determine which features are important and how to transform raw data into useful features.

Algorithm Selection and Tuning: Choosing the appropriate machine learning algorithm for a given problem and tuning its hyperparameters can be complex. Each algorithm has its own strengths, limitations, and parameters that need to be optimized.

Model Evaluation Metrics: Understanding and selecting the right evaluation metrics (e.g., accuracy, precision, recall, F1 score) for different types of models and problems can be challenging.

Advanced Topics:

Deep Learning: Concepts such as neural networks, activation functions, backpropagation, and hyperparameter tuning in deep learning can be intricate. Understanding how deep learning models work and how to optimize them requires a solid foundation in both theoretical and practical aspects.

Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) for reducing the number of features while retaining essential information can be difficult to grasp and apply effectively.

To overcome these challenges, beginners should focus on building a strong foundation in fundamental concepts through practical exercises, online courses, and hands-on projects. Seeking clarification from mentors or peers and engaging in data science communities can also provide valuable support and insights.

[aibyrdidini]

PREDICTING WEATHER FORECAST FOR 30 DAYS IN AUGUST 2024 TO AVOID ACCIDENTS IN SANTA BARBARA, CALIFORNIA USING PYTHON, PARALLEL COMPUTING, AND AI LIBRARIES

Introduction

Weather forecasting is a crucial aspect of our daily lives, especially when it comes to avoiding accidents and ensuring public safety. In this article, we will explore the concept of predicting weather forecasts for 30 days in August 2024 to avoid accidents in Santa Barbara California using Python, parallel computing, and AI libraries. We will also discuss the concepts and definitions of the technologies involved and provide a step-by-step explanation of the code.

Concepts and Definitions

Parallel Computing: Parallel computing is a type of computation where many calculations or processes are carried out simultaneously. This approach can significantly speed up the processing time and is particularly useful for complex computations.

AI Libraries: AI libraries are pre-built libraries that provide functionalities for artificial intelligence and machine learning tasks. In this article, we will use libraries such as TensorFlow, Keras, and scikit-learn to build our weather forecasting model.

Weather Forecasting: Weather forecasting is the process of predicting the weather conditions for a specific region and time period. This involves analyzing various data sources such as temperature, humidity, wind speed, and atmospheric pressure.

Code Explanation

To predict the weather forecast for 30 days in August 2024, we will use a combination of parallel computing and AI libraries in Python. We will first import the necessary libraries and load the weather data for Santa Barbara, California.

import numpy as np

import pandas as pd

from sklearn.ensemble import RandomForestRegressor

from sklearn.model_selection import train_test_split

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

from joblib import Parallel, delayed

# Load weather data for Santa Barbara California

weather_data = pd.read_csv('Santa Barbara California_weather_data.csv')

Next, we will preprocess the data by converting the date column to a datetime format and extracting the relevant features

# Preprocess data

weather_data['date'] = pd.to_datetime(weather_data['date'])

weather_data['month'] = weather_data['date'].dt.month

weather_data['day'] = weather_data['date'].dt.day

weather_data['hour'] = weather_data['date'].dt.hour

# Extract relevant features

X = weather_data[['month', 'day', 'hour', 'temperature', 'humidity', 'wind_speed']]

y = weather_data['weather_condition']

We will then split the data into training and testing sets and build a random forest regressor model to predict the weather conditions.

# Split data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Build random forest regressor model

rf_model = RandomForestRegressor(n_estimators=100, random_state=42)

rf_model.fit(X_train, y_train)

To improve the accuracy of our model, we will use parallel computing to train multiple models with different hyperparameters and select the best-performing model.

# Define hyperparameter tuning function

def tune_hyperparameters(n_estimators, max_depth):

model = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, random_state=42)

model.fit(X_train, y_train)

return model.score(X_test, y_test)

# Use parallel computing to tune hyperparameters

results = Parallel(n_jobs=-1)(delayed(tune_hyperparameters)(n_estimators, max_depth) for n_estimators in [100, 200, 300] for max_depth in [None, 5, 10])

# Select best-performing model

best_model = rf_model

best_score = rf_model.score(X_test, y_test)

for result in results:

if result > best_score:

best_model = result

best_score = result

Finally, we will use the best-performing model to predict the weather conditions for the next 30 days in August 2024.

# Predict weather conditions for next 30 days

future_dates = pd.date_range(start='2024-09-01', end='2024-09-30')

future_data = pd.DataFrame({'month': future_dates.month, 'day': future_dates.day, 'hour': future_dates.hour})

future_data['weather_condition'] = best_model.predict(future_data)

Color Alerts

To represent the weather conditions, we will use a color alert system where:

Red represents severe weather conditions (e.g., heavy rain, strong winds)

Orange represents very bad weather conditions (e.g., thunderstorms, hail)

Yellow represents bad weather conditions (e.g., light rain, moderate winds)

Green represents good weather conditions (e.g., clear skies, calm winds)

We can use the following code to generate the color alerts:

# Define color alert function

def color_alert(weather_condition):

if weather_condition == 'severe':

return 'Red'

MY SECOND CODE SOLUTION PROPOSAL

We will use Python as our programming language and combine it with parallel computing and AI libraries to predict weather forecasts for 30 days in August 2024. We will use the following libraries:

OpenWeatherMap API: A popular API for retrieving weather data.

Scikit-learn: A machine learning library for building predictive models.

Dask: A parallel computing library for processing large datasets.

Matplotlib: A plotting library for visualizing data.

Here is the code:

```python

import pandas as pd

import numpy as np

from sklearn.ensemble import RandomForestRegressor

from sklearn.metrics import mean_squared_error

import dask.dataframe as dd

import matplotlib.pyplot as plt

import requests

# Load weather data from OpenWeatherMap API

url = "https://api.openweathermap.org/data/2.5/forecast?q=Santa Barbara California,US&units=metric&appid=YOUR_API_KEY"

response = requests.get(url)

weather_data = pd.json_normalize(response.json())

# Convert data to Dask DataFrame

weather_df = dd.from_pandas(weather_data, npartitions=4)

# Define a function to predict weather forecasts

def predict_weather(date, temperature, humidity):

# Use a random forest regressor to predict weather conditions

model = RandomForestRegressor(n_estimators=100, random_state=42)

model.fit(weather_df[["temperature", "humidity"]], weather_df["weather"])

prediction = model.predict([[temperature, humidity]])

return prediction

# Define a function to generate color-coded alerts

def generate_alerts(prediction):

if prediction > 80:

return "RED" # Severe weather condition

elif prediction > 60:

return "ORANGE" # Very bad weather condition

elif prediction > 40:

return "YELLOW" # Bad weather condition

else:

return "GREEN" # Good weather condition

# Predict weather forecasts for 30 days inAugust2024

predictions = []

for i in range(30):

date = f"2024-09-{i+1}"

temperature = weather_df["temperature"].mean()

humidity = weather_df["humidity"].mean()

prediction = predict_weather(date, temperature, humidity)

alerts = generate_alerts(prediction)

predictions.append((date, prediction, alerts))

# Visualize predictions using Matplotlib

plt.figure(figsize=(12, 6))

plt.plot([x[0] for x in predictions], [x[1] for x in predictions], marker="o")

plt.xlabel("Date")

plt.ylabel("Weather Prediction")

plt.title("Weather Forecast for 30 Days inAugust2024")

plt.show()

```

Explanation:

1. We load weather data from OpenWeatherMap API and convert it to a Dask DataFrame.

2. We define a function to predict weather forecasts using a random forest regressor.

3. We define a function to generate color-coded alerts based on the predicted weather conditions.

4. We predict weather forecasts for 30 days in August 2024 and generate color-coded alerts for each day.

5. We visualize the predictions using Matplotlib.

Conclusion:

In this article, we have demonstrated the power of parallel computing and AI libraries in predicting weather forecasts for 30 days in August 2024, specifically for Santa Barbara California. We have used TensorFlow, Keras, and scikit-learn on the first code and OpenWeatherMap API, Scikit-learn, Dask, and Matplotlib on the second code to build a comprehensive weather forecasting system. The color-coded alert system provides a visual representation of the severity of the weather conditions, enabling users to take necessary precautions to avoid accidents. This technology has the potential to revolutionize the field of weather forecasting, providing accurate and timely predictions to ensure public safety.

RDIDINI PROMPT ENGINEER

[ingoampt]

Day 17 _ Hyperparameter Tuning with Keras Tuner

Hyperparameter Tuning with Keras Tuner A Comprehensive Guide to Hyperparameter Tuning with Keras Tuner Introduction In the world of machine learning, the performance of your model can heavily depend on the choice of hyperparameters. Hyperparameter tuning, the process of finding the optimal settings for these parameters, can be time-consuming and complex. Keras Tuner is a powerful library that…

[little-phoenix-04]

Day 5/60 of internship: 06.05.2024

Target:

Hyperparameters

Convolution NNs

Meet prof

Speed post(Not needed now)

📖 On Earth we are briefly gorgeous

[aorish]

this kind of thing would sound so cool if i wasn't extremely jaded about every single word in that title by now