tensorflow VS Pandas

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TensorFlow

GitHub Repository - The main TensorFlow repository.

The official 2.8.4 container was published in Nov 2022. That's 1.5 years of OS updates at least. I looked up the 2.8.4 source and found that it's using Ubuntu 20.04 as the base OS. Of note, we're using the x86_64 architecture according to the container image layer: ENV NVARCH=x86_64.

# L2-normalize the encoding tensors image_encoding = tf.math.l2_normalize(image_encoding, axis=1) audio_encoding = tf.math.l2_normalize(audio_encoding, axis=1) # Find euclidean distance between image_encoding and audio_encoding # Essentially trying to detect if the face is saying the audio # Will return nan without the 1e-12 offset due to https://github.com/tensorflow/tensorflow/issues/12071 d = tf.norm((image_encoding - audio_encoding) + 1e-12, ord='euclidean', axis=1, keepdims=True) discriminator = keras.Model(inputs=[image_input, audio_input], outputs=[d], name="discriminator")

[3]: https://github.com/tensorflow/tensorflow/graphs/contributors

To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐

pandas: while this library includes some convenient methods for visualizing data that hook into matplotlib, we'll mainly be using it for its main purpose as a general tool for working with data (https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf).

Many day-to-day tasks may require one-time data analysis, so writing services every time doesn't pay off. You can treat JIRA as a data source and use a typical data analytics tool belt. For example, you may take Jupyter, fetch the list of recent bugs in the project, prepare a list of "features" (attributes valuable for analysis), utilize pandas to calculate the statistics, and try to forecast trends using scikit-learn. In this article, I would like to explain how to do it.

It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”

pandas - The standard data analysis and manipulation tool numpy - scientific computing library seaborn - statistical data visualization sklearn - basic machine learning and predictive analysis CausalML - a suite of uplift modeling and causal inference methods PyTorch - professional deep learning framework PivotTablejs - Drag’n’drop Pivot Tables and Charts for Jupyter/IPython Notebook LazyPredict - build and work with and compare multiple models phidata - Build AI Assistants with memory, knowledge and tools. Lux - automates visualization and data analysis pycaret - low-code machine learning library. really nice Cleanlab - for when you are working with messy data drawdata - draw a dataset from inside Jupyter pyforest - lazy import popular data science libs streamlit - simple ui builder, useful for demonstrating ML results

Pandas is a powerful data manipulation and analysis library that provides easy-to-use data structures and data analysis tools. It includes the read_excel and to_excel functions to read from and write to Excel files. It leverages third-party libraries like OpenPyXL and xlrd to read from and write to Excel files.

How to Accomplish: Use statistical analysis tools and libraries (e.g., Pandas for tabular data) to calculate and visualize these characteristics. For image datasets, custom scripts to analyze object sizes or mask distributions can be useful. Tools like OpenCV can assist in analyzing image properties, while libraries like Pandas and NumPy are excellent for tabular and numerical analysis. To address class imbalances, consider techniques like oversampling, undersampling, or synthetic data generation with SMOTE.

Pandas - A powerful data analysis and manipulation library for Python. Pandas Documentation - Official documentation.

It's also possible for you to give a package an alias by using the as keyword. For instance, you could use the pandas package as pd like this: