![]() Profiling and inspecting memory in PyTorch ![]() dprint ( d ) # Elemwise '' 3Įnter fullscreen mode Exit fullscreen mode # replacing computations with more efficient ones. # expression graphs by removing unnecessary operations and function (, dc ) assert f_dc ( 1.5, 2.5 ) = 1.0 # Compiling functions with `aesara.function` also optimizes function (, c ) assert f_c ( 1.5, 2.5 ) = 4.0 # Compute the gradient of the example expression with respect to `a`ĭc = aesara. # values as input and computes the value of `c`.į_c = aesara. dscalar ( " b " ) # Create a simple example expressionĬ = a + b # Convert the expression into a callable object that takes `(a, b)` Import aesara from aesara import tensor as at # Declare two symbolic floating-point scalarsĪ = at. Here's an example of what to get started with: This means no more size assertions in your deep-learning code! With jaxtyping, you have type annotations and runtime shape checking for Tensors in JAX, PyTorch and TensorFlow. This saves you time fixing errors and helps you learn faster and build reliable models. Why should you care? Type annotation and runtime checking are like a "smart assistants" who double-checks you're using the right data in the right way. To participate, simply sign up to Quine and head to Quests. ![]() The latest Creator Quest challenges you to build an education app using Generative AI. However, if you’re more on the applied AI side we recommend you check out Creator Quests, an open-source challenge that rewards developers for creating cool GenerativeAI apps with ChatGPT, Claude, Gemini and more. These repos will be particularly useful when you’re training Machine Learning models. Lastly, after a little hand-picking, we find the below 8 repos. Then, we calculated the likelihood that the top 1% of developers will star a repo compared to the likelihood that the bottom 50% won’t. ![]() To come up with this list, we looked at the repos that the top 1% have starred. In simple terms, DevRank uses Google’s PageRank algorithm to measure how important a developer is in open source based on their contributions to open source repos. In this post, we rank developers based on their DevRank. How do we find the repos that the top 1% of devs use? □ Today, let's dive into 8 Language Models and Deep Learning repos that the top 1% of developers use (and those you have likely never heard of). ![]()
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