February's platform release is here with new updates including: Codespaces added to DataRobot Workbench to enhance the code-first experience New filter options for data-slice functionality added two new filter options – letting you set a range, inclusive, of the actual
CODE
-
Azure OpenAI Announces Assistants API and New Models
By
–
Azure OpenAI Service announces Assistants API, New Models for Finetuning, Text-to-Speech and more bit.ly/3Ijhnax We are launching Assistants API in public preview, new text-to-speech capabilities, upcoming updated models for GPT-4 Turbo preview and GPT-3.5 Turbo [Translated from EN to English]
→ View original post on X — @marktabnet, 2024-03-01 15:07 UTC
-
Wrapping up Python, MLOps, ML and LLMs content sharing
By
–
That's a wrap! Every day, I share and simply content around Python, MLOps, Machine Learning & LLMs. Find me →
@Sumanth_077 Like/RT the first tweet and help this reach more people. -
Implementing _predict_single Method for k-NN Classification
By
–
Finally a '_predict_single' method as mentioned it predicts the class label for a single data point This is done by: 1. Calculating the distance between one point to all training examples 2. Selecting the k-nearest Neighbours, & returning the most common class label among them
-
Predict Method for Class Labels Using Single Point Prediction
By
–
A 'predict' method that predicts the class labels for a set of data points. It calls '_predict_single' method that actually predicts the class label for a single data point.
-
Implementing Fit Method and Euclidean Distance in KNN Algorithm
By
–
Next define the "fit" method that takes the training data(X) and it's corresponding labels(y) and stores them. This is the reason why the computation cost is high as it stores all the training data! And also a method to calculates the euclidean distance between two data points.
-
KNNClassifier Class Initialization and K Value Selection
By
–
Define the KNNClassifier class and initialize the number of Neighbours (k) to be considered in __init__ method Defining the K value is critical in KNN! A small value of k means noise will have a higher influence on the result and a large value makes it computationally expensive
-
K Nearest Neighbors KNN Python Implementation From Scratch
By
–
K Nearest Neighbors(KNN) implemented from scratch in Python: Here is the step by step explanation with code.
-
Mistral-7B KVCache Optimization for XLA Compilation
By
–
hey @fchollet . I think this is tricky to do in general (like a no free lunch way).
I think the Mistral-7B implementation KerasNLP writes out a KVCache (for compiler optimization) and specifically writes code in a way that is needed for XLA Compilation to work well.
Do let me -
LangChain Founders Share Building Story at ClickHouse Meetup
By
–
Want to hear more about the story of how we built LangChain? Come join us at the ClickHouse meetup in SF on March 4! Note: Signup required before this Friday, March 1st. https://
meetup.com/clickhouse-sil
icon-valley-meetup-group/events/299058486/
…