#jupyter_notebook #deep_learning #deep_reinforcement_learning #jax #machine_learning #numpy #reinforcement_learning #transformer

https://github.com/google/trax

#other #computer_vision #deep_learning #e_books #ebook #machine_learning #math #mathematics #nlp #numpy #opencv #opencv_computer_vision #pandas #pdf #python #reinforcement_learning #scikit_learn #scipy #tensorflow

https://github.com/changwookjun/StudyBook

#jupyter_notebook #jupyter #jupyter_notebook #jupyter_notebooks #jupyter_tutorial #numpy #numpy_arrays #numpy_tutorial #pandas #pandas_dataframe #pandas_tutorial #python #python_pandas #python_tutorial #python_tutorials

https://github.com/codebasics/py

#python #bounding_boxes #computer_vision #data_augmentation #data_visualization #deep_learning #deep_neural_networks #image_processing #k_means #mean_average_precision #numpy #object_detection #pandas #performance_visualization #pretrained_weights #python3 #random_weights #tensorflow2 #train #yolo #yolov3

https://github.com/emadboctorx/yolov3-keras-tf2

#python #datasets #evaluation #metrics #natural_language_processing #nlp #numpy #pandas #pytorch #tensorflow

https://github.com/huggingface/nlp

#python #numpy

https://github.com/numpy/numpy

#python #book #dataviz #matplotlib #numpy #open_access #plotting #scientific_publications

https://github.com/rougier/scientific-visualization-book

#jupyter_notebook #andrew_ng #andrew_ng_course #andrew_ng_machine_learning #andrewng #coursera #coursera_machine_learning #data_science #deep_learning #deep_neural_networks #dl #machine_learning #ml #neural_network #neural_networks #numpy #pandas #python #pytorch #reinforcement_learning

https://github.com/ashishpatel26/Andrew-NG-Notes

#python #binder #exercises #notebook #numpy

https://github.com/rougier/numpy-100

#python #cublas #cuda #cudnn #cupy #curand #cusolver #cusparse #cusparselt #cutensor #gpu #nccl #numpy #nvrtc #nvtx #rocm #scipy #tensor

https://github.com/cupy/cupy

#jupyter_notebook #data_analysis #data_science #data_science_tips #data_visualization #jupyter #jupyter_notebook #jupyter_tips #matplotlib #matplotlib_tips #numpy #pandas #pandas_tips #python #python_tips #sklearn

https://github.com/ChawlaAvi/Daily-Dose-of-Data-Science

#python #automl #distributed #genetic_algorithm #julia #machine_learning #numpy #symbolic_regression

https://github.com/MilesCranmer/PySR

#other #matplotlib #numpy #pandas

The book "利用Python进行数据分析" (Using Python for Data Analysis) has a new third edition with several improvements. It includes updated versions of Python (3.10) and Pandas (1.4.0), adding new methods and features. The book is more user-friendly for beginners, simplifying code readability by avoiding confusing shortcuts. There are also additional resources like video guides, study notes, and online versions available. This makes it easier for users to learn and apply data analysis techniques effectively.

For advanced users, the book "极速Python" (Fast Python) focuses on high-performance techniques for large datasets, covering topics like data structure optimization, high concurrency, and distributed data processing. It integrates technologies like Arrow and Ray, which are crucial for efficient data handling and analysis in modern applications. This helps users handle big data more efficiently and stay updated with the latest technological advancements.

https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version

#python #audio #docker_image #dsp #equalizer #filter #limiter #mastering #matchering #matching #music #numpy #python #python_library #python3 #scipy #sound #spectrum #vst

Matchering 2.0 is a tool that helps make your music sound like your favorite songs. Here’s how it works: you give it two audio files - one you want to master (your song) and another you want it to sound like (a reference song). Matchering then adjusts your song to match the volume, frequency response, peak amplitude, and stereo width of the reference song. This means you can quickly make your music sound professional and consistent, just like popular tracks. You can use it without installation through online services or install it on your computer for more control. It's especially useful for music producers and audio engineers who want to enhance their tracks easily.

https://github.com/sergree/matchering

#python #autograd #deep_learning #gpu #machine_learning #neural_network #numpy #python #tensor

PyTorch is a powerful Python package that helps you with tensor computations and deep neural networks. It uses strong GPU acceleration, making your computations much faster. Here are the key benefits PyTorch allows you to use GPUs for tensor computations, similar to NumPy, but much faster.
- **Flexible Neural Networks** You can seamlessly use other Python packages like NumPy, SciPy, and Cython with PyTorch.
- **Fast and Efficient**: PyTorch has minimal framework overhead and is highly optimized for speed and memory efficiency.

Overall, PyTorch makes it easier and faster to work with deep learning projects by providing a flexible and efficient environment.

https://github.com/pytorch/pytorch