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!
@sumanth_077
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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
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Wrapping Up: Python, Data Science, ML & LLM Content Daily
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That's a wrap! Every day, I share and simply content around Python, Data Science, Machine Learning & Large Language Models. Find me → @Sumanth_077 Like/RT the first tweet and help this reach more people.
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Predict Method Implementation with Binary Classification Threshold
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Finally the predict method: It uses the trained model to make predictions based on input features. This method applies a threshold of 0.5 to make binary predictions, returning 1 for positive class predictions and 0 for negative class predictions.
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Defining the Fit Method for Model Training with Gradient Descent
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Define the "fit" method that does the training of model The method takes input data "X" and the corresponding target values "y" It also updates the weights and bias using gradient descent.
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Computing Cross-Entropy Loss Cost Function for Classification
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Compute Cost Function Next define the cost function, which is the cross-entropy loss, used to measure the error between predicted probabilities and actual labels.
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Sigmoid Activation Function: Computing Class Probability in Machine Learning
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Define Sigmoid Activation Function: This is used to compute the probability of the target class. Sigmoid function takes a linear combination of input features and returns values between 0 and 1, which represent the probability that a given sample belongs to the positive class.