It's easy to train a Model on your Data. Just a couple of clicks 1. Under What do you want to do? select Train a model.
2. Name your model.
3. Under Label select species. Select the target column and click Train. That's how simple it is to train a Model. 4/6
@sumanth_077
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Simple Model Training in Just a Few Clicks
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Machine Learning Model: Key Capabilities and Workflow Steps
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What are all things you can do? – Predict Missing Values
– Spot Abnormal Values
– Train and Evaluate the Model
– Analyze and Interpret the Model Results
– Exporting the trained Model 3/6 -
Installing and Using Simple ML for Sheets Extension
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It's simple to use. Go to the Extensions Tab in Sheets Get and install the add-on. "Simple ML for Sheets" Once you start the extension with the data in your Sheet. You will see the side panel as below! 2/6
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Simple ML for Sheets: Building ML Models in Google Sheets
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No Google Colab, No Jupyter Notebook! Introducing Simple ML for Sheets. Let's build Machine Learning Models right inside Google Sheets. Thread
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Understanding Tensors: Fundamental Data Structure in Machine Learning
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Tensor is a fundamental data structure in Machine Learning. I will clearly explain it today! Imagine you have pictures of cats. Each picture is made up of tiny pixels, and each pixel has some color values. Now, a tensor is a container that stores this data. TensorFlow,
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Daily Python, Data Science and Machine Learning Tutorials
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That's a wrap! Every day, I share tutorials around Python, Data Science & Machine Learning. You can follow me → @Sumanth_077 Like/RT the first tweet to help this reach more people!
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XLA and Automatic Parallelization in JAX for Computation Speed
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XLA & Automatic Parallelization It also supports XLA or Accelerated Linear Algebra, which is an optimizing compiler, designed specifically to increase the computation speed. And also supports Parallelization and can be done using "pmap()" https://
jax.readthedocs.io/en/latest/note
books/quickstart.html
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Automatic Vectorization with JAX vmap Function for Faster Code Execution
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Automatic Vectorization Vectorization helps faster code execution and Jax has an inbuilt function to do the same And can be done with using "vmap()" function Here are the detailed use cases and implementation of Vectorization. http://
jax.readthedocs.io/en/latest/_aut
osummary/jax.vmap.html#jax.vmap
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Auto Differentiation: Powerful Tool for ML Model Training
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Auto Differentiation This is really powerful to automate the calculation of derivatives especially when differentiating complex algorithms and mathematical functions. It is highly useful in training machine learning models and can be done with 'jax.grad()'
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Jax: High Performance Machine Learning Library Faster Than Numpy
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Jax is a Python library for High Performance Machine Learning and is really faster than Numpy Here is everything you need to know about Jax and its powerful features for Machine Learning: Thread