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Here is my implementation of a Multihead Attention Layer with a Kolmogorov–Arnold Networks (KAN) linear layer! Pretty simple, right?! I am using the efficient implementation of the KAN network available in this repo: github.com/Blealtan/efficien… -- 👉 Don't forget to subscribe to my ML newsletter: newsletter.TheAiEdge.io --

Kolmogorov-Arnold network obliterates Deepmind's results with much smaller networks and much more automation. KANs also discovered new formulas for signature and discovered new relations of knot invariants in unsupervised ways. Incredible 🔥🔥🔥🔥🔥 The bell 🔔has tolled for deep learning finally? #kan #deeplearning

Turns out, a simple sum operator might just render #deeplearning obsolete. Solid math-based ideas are proving once again to outperform gimmicks. #kolmogorovarnoldnetwork

Data pipelines will put you in the top 1% of the market. Learning how to use Python (#1 programming language in the world) to manipulate data in production applications is a super-power. Here is how you can build your first pipeline (using Python, 'cause you don't First, there are three things you need when building a production application: • You need it to be reliable • You need it to be scalable • You need it to be efficient You can't accomplish any of that without using a pipeline to orchestrate the process. To build your first pipeline, you need to understand a couple of concepts: 1. Data Nodes: They represent any data you want to load, manipulate, or save. 2. Tasks: These are functions that will interact with the data. A pipeline combines data nodes with tasks. It's that simple! In this short video, I build a simple pipeline to process a few dataset columns. I'm using Taipy, an open-source library that lets you build and orchestrate pipelines. Star their GitHub repository: github.com/Avaiga/taipy Look at the video. It doesn't get easier than that! Thanks to the team behind Taipy for collaborating with me on this post.

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Normalization is an important supporting actor in visual perception (besides convolutional feature extraction). Normalization is what you need interneurons for.

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9 most common statistical tests used in A/B testing. Here's a breakdown: A/B Testing is the biggest driver of conversion rate improvement (more than Customer Journey analysis, Segmentation, Surveys, and Cart Abandonment Analysis). That's why if A/B testing is NOT in your toolbox, you'd better get up to speed now. Let me help. Here are the 9 most common statistical tests that beginners get tripped up with. And an explanation of when to use them: 1. Z-Test: Used with sample sizes, known population variance. Used to determine if there is a difference between two means. Often used for proportions like click-through rates. 2. T-Test (Student's): Smaller sample sizes, unknown population variance. Suitable for comparing means from two groups. Includes independent and paired samples t-tests. 3. Welch's T-Test: Unequal variances and/or sample sizes. An adaptation of the t-test that does not assume equal variances, offering more flexibility. 4. Chi-Squared Test: Categorical data, testing for independence or goodness of fit. Useful for assessing if there is a significant association between two categorical variables. 5. ANOVA: Comparing means of three or more groups. Ideal for understanding if there are any significant differences between the means of multiple groups. 6. Mann-Whitney U Test: Non-parametric alternative to Z-test / T-test for non-normal distributions. Comparing two independent groups, especially when the data is not normally distributed. 7. Fisher’s Exact Test: Small sample sizes, especially in 2x2 contingency tables. Used for examining the significance of the association between two kinds of classifications. 8. Regression Analysis: Relationship between a dependent variable and one/more independent variables. For more complex A/B tests to understand the impact of multiple variables on an outcome. 9. Pearson's Chi-Squared Test: Categorical data in contingency tables. Determines if there is a significant difference in the distribution of categories between groups. === Need to learn A/B Testing to prepare for 2024? Learning A/B testing can help you get a new job, get a promotion, and make a career transition. Companies need this skill to improve online conversions, and profitability now more than ever. I have a good news. I have a free workshop on A/B test in #R and #Python: 👉 Register here for my Free A/B Testing Workshop in #R (December 6th): us02web.zoom.us/webinar/regi… 👉 Register here for my Free A/B Testing Workshop in #Python (December 20th): us02web.zoom.us/webinar/regi… #DataScience #DataAnalytics

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