Description Take any directory full of stuff that you're working on; web apps, scripts, Jupyter notebooks, data files, whatever it may be. By adding an anaconda-project.yml to this project directory, a single anaconda-project runcommand will be able to set up all dependencies and then launch the project. Anaconda projects should run in the same way on your machine, on a colleague's machine, or when deployed to a server. Running an Anaconda project executes a command specified in the anaconda-project.yml (any arbitrary commands can be configured). anaconda-project.yml automates project setup; Anaconda can establish all prerequisite conditions for the project's commands to execute successfully. These conditions could include: • creating a conda environment with certain packages in it • prompting the user for passwords or other configuration • downloading data files • starting extra processes such as a database server The goal is that if your project runs on your machine, it will also run on others' machines (or on your future machine after you reboot a few times and forget how your project works). The command anaconda-project init DIRECTORY_NAME creates an anaconda-project.yml, converting your project directory into an Anaconda project. Put another way... Traditional build scripts such as setup.py automate "building" the project (going from source code to something runnable), while anaconda-project automates "running" the project (taking build artifacts and doing any necessary setup prior to executing them).
Libraries used :-
- tensorflow
- keras
- numpy
- matplotlib
- sklearn
References
- https://www.kaggle.com/c/fake-news/data
- https://www.kaggle.com/rchitic17/real-or-fake
- https://zenodo.org/record/1048820#.XvSmSi0w1bU
- https://www.kaggle.com/antmarakis/fake-news-data
- Lecun, Y., Haffner, P., Bottou, L., & Bengio, Y. (1999). Object Recognition with GradienBased Learning. Shape, Contour and Grouping in Computer Vision Lecture Notes in Computer Science, 319-345. doi:10.1007/3-540-46805-6_19
- https://missinglink.ai/guides/convolutional-neural-networks/convolutional-neural-network-architecture-forging-pathways-future/
- https://arxiv.org/pdf/1503.04069.pdf 8. https://missinglink.ai/guides/neural-network-concepts/deep-learning-long-short-term-memorylstm-networks-remember/ 9. https://towardsdatascience.com/regularization-in-machine-learning-76441ddcf99a