{"id":196368,"date":"2023-09-18T11:06:17","date_gmt":"2023-09-18T15:06:17","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=196368"},"modified":"2023-09-18T11:07:17","modified_gmt":"2023-09-18T15:07:17","slug":"how-to-setup-in-python-tensorflow-2-1-for-deep-learning","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/how-to-setup-in-python-tensorflow-2-1-for-deep-learning\/","title":{"rendered":"How to Setup in Python TensorFlow 2.1+ for Deep Learning"},"content":{"rendered":"\n

The article “How to Setup in Python TensorFlow 2.1+ for Deep Learning” first appeared on Quant at Risk<\/a> blog<\/em>.<\/p>\n\n\n\n

Excerpt<\/em><\/p>\n\n\n\n

If you wonder how to start with Deep Learning<\/a> \u2013 this post is for you. Using MacOS<\/strong> as an exemplary working operating system, I will help you to set up and adjust your current Python<\/strong>-based world to be ready for commencing your first DL project. Are you ready? Good! Here we go!<\/p>\n\n\n\n

1. Download and Install Python 3.7+ within Anaconda Distribution<\/strong><\/p>\n\n\n\n

No matter are you new to Python programming or quite advanced in coding, Anaconda Inc.<\/a> (previously known as Continuum Analytics) grew to a current position as a market leader and standard-provider when it comes to downloading and setting up a rich Python-based environment on your PC, Mac, or Linux. Simply speaking, if you want to work efficiently in Python then the use of the Anaconda Distribution<\/strong> for the most recent version of Python is the starting pack<\/strong> you are looking for! Simply visit this webpage<\/a>, choose the operating system for which you intend to download your Anaconda Distribution, for instance:<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

and (preferably) select the Graphical Installer<\/strong> of Python 3.7. First, it will start downloading the installation file, and next, when run, you will be guided through a few simple steps making the entire installation of the Anaconda Distribution on your local machine complete.<\/p>\n\n\n\n

Once the installation process is done (here: ver 2019.10), you might inspect the local .bash_profile<\/strong> file located in your home directory (MacOS\/Linux). The process adds a few lines, easy to spot:<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

This initialises the environment every time you open a new Terminal window. From the moment, accessing Python directly is as simple as typing:<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Usually when you install the Anaconda Distribution (on some random day since its official release date), keep in mind that some packages and libraries which come along as default ones might require a refreshment. Start with<\/p>\n\n\n\n

conda update conda<\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Next, update all default libraries with<\/p>\n\n\n\n
conda update --all<\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
In that process you can notice which versions were originally built-in and to which version they will be upgraded:<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Anaconda Distribution provides you with a number of excellent libraries you can consider as fundamental ones. Make sure your \u201cbatteries included\u201d contain:<\/p>\n\n\n\n
numpy              # numerical computations<\/code><\/p>\n\n\n\n
scipy              # additional numerical tools<\/code><\/p>\n\n\n\n
pandas             # data processing<\/code><\/p>\n\n\n\n
matplotlib         # plotting<\/code><\/p>\n\n\n\n
seaborn            # fancy plotting<\/code><\/p>\n\n\n\n
statsmodels        # statistical modeling<\/code><\/p>\n\n\n\n
sympy              # symbolical computations<\/code><\/p>\n\n\n\n
scikit-learn       # machine-learning<\/code><\/p>\n\n\n\n
and keep them updated on a regular basis using conda update \u2013all<\/em> command.<\/p>\n\n\n\n
Regarding the above list of libraries, the last one there, the scikit-learn<\/a>, is the mecca for all Machine Learning<\/strong> practitioners. If you wish to start your experience with ML algorithms, you will step into a wonderful world of AI simply by studying provided there elegant documentation and tonnes of valuable examples:<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Visit Quant at Risk<\/a> blog for the next step in this tutorial – how to install Keras and TensorFlow 2.1+<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"
If you wonder how\u00a0to start with Deep Learning\u00a0\u2013 this post is for you. <\/p>\n","protected":false},"author":710,"featured_media":185996,"comment_status":"open","ping_status":"closed","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[339,343,349,338,341],"tags":[6922,2105,6614,595,924],"contributors-categories":[13732],"class_list":["post-196368","post","type-post","status-publish","format-standard","has-post-thumbnail","category-data-science","category-programing-languages","category-python-development","category-ibkr-quant-news","category-quant-development","tag-anaconda","tag-deep-learning","tag-jupyter-notebook","tag-python","tag-tensorflow","contributors-categories-quant-at-risk"],"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nHow to Setup in Python TensorFlow 2.1+ for Deep 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