#30daysofmachinelearning 検索結果

Day 6 of #30DaysOfMachineLearning 🔥 • CNNs + Conv layers • Built image classifier w/ augmentations • Training loop & evaluation Getting hands-on with vision models that actually see 🔗 github.com/Bishal-Stha/Py… #PyTorch #DeepLearning #LearnInPublic @OpenAI @GoogleDeepMind

Day 5 of #30DaysOfMachineLearning 🔥 • Revisited vanishing/exploding gradients • Compared ReLU vs. ELU activation • Explored batch normalization Building deeper, more stable networks step by step 🔗 github.com/Bishal-Stha/Py… #PyTorch #DeepLearning #LearnInPublic @OpenAI @Google

Day 4 of #30DaysOfMachineLearning 🚀 • OOP PyTorch: Dataset, DataLoader, Model • Optimizers + Training Loop • Model evaluation metrics Gaining confidence in building robust deep learning 🔗 github.com/Bishal-Stha/Py… #PyTorch #DeepLearning @OpenAI @huggingface #LearnInPublic

Day 3 of #30DaysOfMachineLearning • Evaluation loop • Accuracy with torchmetrics • Dropout & overfitting insights • Fine-tuning + layer freezing • Random search for performance boost Exploring the art of smarter training! 🔗 github.com/Bishal-Stha/Py… #PyTorch #DeepLearning

Starting a #30DaysOfMachineLearning challenge today 💪 Here's a Kaggle notebook I threw together with the Fast AI library -> kaggle.com/code/joemuller…

Kernel bandwidth in Kernel Density Estimation - balancing detail and smoothness. Smaller bandwidth - more variability but may overfit Larger one - smoother estimates but can oversmooth. 🔧Cross-validation & Silverman's rule aid bias-variance trade-off. #30daysofMachineLearning

Nonparametric Density Estimation - a powerful statistical tool for estimating probability density functions without assuming specific distributions. Kernel Density Estimation, Histogram methods help unveil complex data patterns (informed decision-making). #30daysofMachineLearning

📈Evaluating grouping of data points within clusters & separated across clusters is key in assessing clustering quality. Fowlkes-Mallows index, quantify effectiveness of clustering by examining relationships in data points based on clustering assignments. #30daysofMachineLearning

📊Pairwise measures in clustering evaluation analyze similarity of cluster assignments by comparing pairs of data points within and across clusters. Metrics, Jaccard coeff. & Rand index, offer insights between clustering outcomes & ground truth labels. #30daysofMachineLearning

Mutual Information in clustering evaluation assesses the dependency between observed and expected joint probabilities of clusters and ground truth. #ClusteringEvaluation #30daysofMachineLearning

📊 Conditional Entropy in clustering evaluation measures the cluster-specific entropy, revealing how ground truth is distributed within each cluster. #ClusteringEvaluation #DataAnalysis #30daysofMachineLearning

📊 Maximum Matching in clustering evaluation ensures one cluster is matched to one partition, maximizing purity under the one-to-one matching constraint. It is essential for assessing clustering performance effectively. #ClusteringEvaluation #30daysofMachineLearning

📊 Purity in clustering quantifies the extent to which a cluster contains points exclusively from one ground truth partition. It is a crucial measure for evaluating the quality of clustering results accurately. #ClusteringEvaluation #DataAnalysis #30daysofMachineLearning

🤖 Exploring clustering evaluation in #30daysofMachineLearning! Did you know there are two main categories of measures? External measures rely on external ground-truth, while internal measures derive goodness from the data itself. 💡

The Davies-Bouldin Index is critical in cluster evaluation, it captures trade-off between cluster compactness & separation. A lower index value : more cohesive and well-separated clusters, showcasing its significance in optimizing clustering algorithms. #30daysofMachineLearning

Epsilon (eps) plays a pivotal role in density-based clustering, the Elbow effect shedding light on its impact. 🔧 Manipulating epsilon values, we can observe clusters merging or outliers emerging, offering valuable insights into data patterns. #30daysofMachineLearning

A cluster in density-based clustering revolves around density-connected points forming dense regions. Identifying maximal sets of points with density-reachable relationships, we pinpoint clusters amidst varying densities -> precise clustering analysis. #30daysofMachineLearning

Direct density reachability in DBSCAN highlights the direct connection between core objects, essential for clustering and outlier detection. The path of direct density reachability, unveils intricate relationships within dense regions in the data space. #30daysofMachineLearning

Epsilon and minPts are the dynamic duo in density-based clustering algorithms like DBSCAN. 🪩Epsilon controls the reachability distance, 🪩minPts determines the density threshold for defining clusters. These parameters enable accurate clustering. #30daysofMachineLearning

Understanding core points, border points, and outliers is crucial in density-based clustering like DBSCAN. Core points form the heart of clusters, while border points lie on the edges, and outliers stand alone. #30daysofMachineLearning

#Day4 of #30daysofmachinelearning #learninginpublic 𝐍𝐮𝐦𝐩𝐲 𝐚𝐫𝐚𝐧𝐠𝐞 𝐚𝐧𝐝 𝐥𝐢𝐧𝐬𝐩𝐚𝐜𝐞 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬

Got 96 score on Pre-Test Machine Learning Fundamentals from Skill Academy Pro #30daysofAI #30daysofMachineLearning

Understanding core points, border points, and outliers is crucial in density-based clustering like DBSCAN. Core points form the heart of clusters, while border points lie on the edges, and outliers stand alone. #30daysofMachineLearning

2nd week of our #30daysofmachinelearning. @dsn_ai_network @OneInnovationH1

One thing that I am investing quite a lot of time in is diving deep into Machine Learning Sharing my learning in talks, workshops, and now will be doing a #30daysofMachineLearning here where I will post something new for you to learn! 🔔Learning starts tomorrow: @drishtijjain !

#30daysofmachinelearning #DSNOffa Introducing participants to Data Visualization using matplotlib and seaborn. @dsn_ai_network @OneInnovationH1 @Asanloko1

@dsn_offa @dsn_ai_network @OneInnovationH1 #30daysOfMachineLearning #Day3

whip my model like a racehorse !! 🔥🔥🔥 67th of 5824 teams 🔥🔥🔥 #kaggle #30daysofmachinelearning

Today is a beautiful day. We introduced all participants to Zindi and everyone participated in the financial inclusion challenge. @OneInnovationH1 @dsn_ai_network #30daysofmachinelearning #DSNOffa

Day 4: Continuation of data structures, creating and defining functions. #30daysofmachinelearning #DSNOffa

Day 8: Additional explanation on versions control with git and basic projects with python. Examples are temperature converter, a BMI calculator, an acronym generator etc. #30daysofmachinelearning #DSNOffa @OneInnovationH1 @dsn_ai_network

Day 1 We started our free 30 days Machine Learning today at Offa.We introduced participants to DSN as well as AI and Machine Learning.Platforms such as jupyter notebook was also introduced to participants #30daysofmachinelearning #DSNOffa @dsn_ai_network @o_funminiyi @Asanloko1

Day 12 of #30daysofmachinelearning Today, I explore Correlation and Causation, their differences, and examples. I also got to know about Confounding Variables and Spurious Correlations as well as their examples.

Today, I started #30daysofmachinelearning with the introduction to machine learning book by Andreas C. Muller and Sarah Guido. This challenge is basically for me to understand the conceptual meanings and definitions of all the machine learning concepts and their applications.

@dsn_offa @dsn_ai_network @OneInnovationH1 @analyticbridge @dataai #30DaysOfMachineLearning Successful Days of Building and Deploying Model on Heroku... baisieinsurance.herokuapp.com