Error displayed: This error usually happens when PyMuPDF is not installed correctly or there’s a conflict with an outdated version. The fitz module should import directly from PyMuPDF, but if you see from frontend import… Fixed: No module named ‘frontend’ when importing fitz (PyMuPDF)
Both are excellent choices for “human-level” TTS, but they serve different needs. OpenAI is the budget-friendly, high-speed workhorse, while ElevenLabs is the premium “creative studio” for maximum realism and voice cloning. The Breakdown Feature OpenAI… openAi vs. ElevenLabs for natural text to speech: comparison + code
Streamlit, an open-source Python library that allows you to create interactive web apps for data science and machine learning in minutes. It is popular because it lets you build full web applications using only Python,… Streamlit for creating interactive web apps for data science and machine learning in minutes.
Error: (base) C:\Windows\System32>pip install –upgrade numpyWARNING: Ignoring invalid distribution -andas (c:\users\foxku\appdata\roaming\python\python310\site-packages)WARNING: Ignoring invalid distribution -umpy (c:\users\foxku\appdata\roaming\python\python310\site-packages)WARNING: Ignoring invalid distribution -andas (c:\users\foxku\appdata\roaming\python\python310\site-packages)WARNING: Ignoring invalid distribution -umpy (c:\users\foxku\appdata\roaming\python\python310\site-packages)Requirement already satisfied: numpy in c:\programdata\anaconda3\lib\site-packages (2.2.6)WARNING: Ignoring invalid distribution… Pandas import error fixed
jupyter notebook shortcuts Jupyter Notebook has many helpful keyboard shortcuts that can speed up your workflow. Here are the most commonly used ones: General Shortcuts Command Mode (press Esc to enter) Edit Mode (press Enter… Useful shortcuts in jupyter notebook & colab
Error In the newly updated version of the Moviepy package, it should be the following import instead It doesn’t mean that MoviePy isn’t installed correctly or Python can’t find it.
You can reduce a video’s size in Python without noticeably losing quality by using efficient codecs, lower bitrates, or slight resolution/frame rate adjustments — typically through FFmpeg, which can be called from Python. Below are… How to reduce a video’s size in Python without noticeably losing quality
This typically means: Let’s go over a realistic and mockable example where: After that, we’ll go through an example where Then we’ll fix it by isolating the unsafe loss. After that, we’ll list which operations… Fixed: CUDA error: an illegal memory access was encountered
Let’s break down the two concept and how to implement it. The Difference: Data Parallelism vs. Model Parallelism Think of it like a factory: PyTorch Underutilizing GPUs? Unraveling the Mystery of nn.Parallel and Single-GPU Usage… Data Parallelism & Model Parallelism
In Google Colab, cd changes the directory only within the current cell. However, Colab resets the working directory when a new cell runs. To make sure the directory change is persistent, try the following: Method… How to change working directory in Colab
Error detected: The error suggests that the package name isn’t correctly formatted. Try using double quotes or removing the quotes altogether: or If the issue persists, you might want to check if your pip version… Pip package installation specifications and Error fixed: pip install -U ‘accelerate>=0.26.0’ ERROR: Invalid requirement: “‘accelerate”: Expected package name at the start of dependency specifier ‘accelerate
OSError: You are trying to access a gated repo. Make sure to have access to it at https://huggingface.co/google/gemma-3-27b-it. 401 Client Error. (Request ID: Root=1-67da7d97-6326b5f53a96415516d2c709;7a741876-1aae-4afd-8d26-afd122bc2c2d) Cannot access gated repo for url https://huggingface.co/google/gemma-3-27b-it/resolve/main/config.json. Access to model google/gemma-3-27b-it… Fixed: OSError: You are trying to access a gated repo. Make sure to have access to it at https://huggingface.co….
Google offers two libraries for generative AI: google-generativeai for complex, direct work with Gemini models, and google-genai for simplified access and easier integration in applications.
Debugging code generated by ChatGPT by pasting the error into ChatGPT for it to resolve its own problem, most of the time, is a very bad choice for me. So, most of the time, I… How to debug codes generated by ChatGPT
Sometimes, even though you already installed torchtext, but can’t import it, or when you import it, you receive this error: and then if you try to verify if the installation was successful by using this… Fixed: TorchText install issue: The procedure entry point?? could not be located in the dynamic link library
In NumPy, you can perform element-wise operations on matrices using vectorized operations, numpy.vectorize for custom functions, and numpy.apply_along_axis for applying functions along specific axes.
The content outlines various PySpark functions used for data manipulation in DataFrames. Key functions include filtering with where(), limiting rows with limit(), returning distinct rows, dropping columns, and grouping by criteria. Each function includes a brief example, illustrating how to access, modify, and aggregate data effectively within PySpark.
This song and example code help remember PySpark data frame creation functions easier. Key functions include creating Data Frames, displaying data, printing schemas, and filtering. The document facilitates understanding how to manipulate data effectively in PySpark, making it a useful reference for users working with large datasets.
This song and code examples help us understand and remember various Pandas functions for data manipulation, including grouping, aggregating, and transforming data. Key functions include groupby(), pivot_table(), resample(), rolling(), expanding(), cumsum(), cumprod(), cut(), qcut(), aggregate(), and transform().
A cute, catchy song on various Pandas functions applied to DataFrames. Key functions include sorting values, resetting the index, dropping columns and duplicates, sampling data, and handling missing values. Example codes illustrate each function’s output, demonstrating how to manipulate and visualize data effectively with Pandas.
The provided content showcases a series of PyTorch functions with descriptions and examples. Functions like torch.abs, torch.ceil, torch.floor, torch.clamp, torch.std, torch.prod, and torch.unique are explained with their respective use cases. These functions are fundamental for manipulating tensors in PyTorch.
The provided content showcases common linear algebra operations in PyTorch, including determinant calculation, matrix inverse, LU decomposition, QR decomposition, Cholesky decomposition, SVD, eigenvalue and eigenvector computation, matrix and vector norms, trace calculation, solving linear systems, and other operations with code and output examples.
Tensor Creation: Example: Here are examples for each of the basic tensor creation functions in PyTorch: Output: Output: Output: Output: Output: Output: Output: Output: Output:
Autograd: Random Number Generation: Loss Functions: Optimization: Examples for Autograd, Random Number Generation, Loss Functions, and Optimization in PyTorch: Autograd Output: Output: Output: Output: Random Number Generation Output: Loss Functions Output: Output: Optimization This function… PyTorch function song & examples: Autograd, Random Number Generation, Loss Functions, optimization
The provided content discusses tensor reshaping and tensor type and device management in PyTorch. It covers functions such as tensor.view(), tensor.reshape(), tensor.transpose(), tensor.squeeze(), tensor.unsqueeze(), tensor.to(), tensor.type(), tensor.is_cuda, tensor.cpu(), and tensor.cuda(). Demonstrated examples showcase effective memory management and computation, especially when utilizing GPUs.
PyTorch Tensor Operations song & examples on element-wise addition, subtraction, multiplication, and division, matrix multiplication, as well as operations like sum, mean, max, min, concatenation, and stacking of tensors.
The codes to for this graph is as below, with the following keypoints: Legend Handling: The legend is constructed from both plots (line plot & bar plot), ensuring that all data series are labeled correctly.… Line and Bar Plot in the same graph with Error Bars
Method 1: Go to Google Colab Visit Google Colab. A dialog like this will appear Do similarly if you want to open a notebook from Github. Alternatively, if you’re already in Google Colab and want… How to open a jupyter notebook in Google Drive/Github using Colab
Pipelines in Python and R are powerful for structuring and processing data. In Python, Pandas and scikit-learn offer pipeline capabilities for data manipulation and machine learning workflows, while in R, the %>% operator from the magrittr package enables efficient data processing in a concise and composable manner.