{"id":24536,"date":"2018-08-13T04:36:41","date_gmt":"2018-08-13T04:36:41","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=24536"},"modified":"2026-04-28T16:00:58","modified_gmt":"2026-04-28T10:30:58","slug":"python-deep-learning-environment-setup","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/python-deep-learning-environment-setup\/","title":{"rendered":"Python Deep Learning Environment Setup"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:148,&quot;href&quot;:&quot;https:\\\/\\\/www.python.org\\\/downloads&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251205194542\\\/https:\\\/\\\/www.python.org\\\/downloads\\\/&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-06 12:20:56&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2025-12-10 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04:11:10&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-07 07:28:04&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-10 21:18:11&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-14 09:09:54&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-17 15:23:05&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-20 21:50:04&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-25 01:42:36&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-31 07:07:32&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-07 07:44:36&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-11 11:42:46&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-17 02:44:00&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-20 13:00:12&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-25 04:49:05&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-28 05:40:20&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-01 14:59:57&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-05 06:08:26&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-09 07:21:26&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-16 15:38:32&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-20 06:15:16&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-26 15:06:41&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-30 06:19:42&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-02 14:23:57&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-02 14:23:57&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<p><span style=\"font-weight: 400\">Moving on from introducing <a href=\"https:\/\/data-flair.training\/blogs\/deep-learning\/\" target=\"_blank\" rel=\"noopener\"><strong>Deep Learning<\/strong><\/a>, now, we guide you to set up your machine for your journey with Deep Learning using Python. So, today in this<a href=\"https:\/\/data-flair.training\/blogs\/deep-learning-with-python-tutorial\/\" target=\"_blank\" rel=\"noopener\"><strong> Deep Learning with Python Tutorial<\/strong><\/a>, we will see Python Deep Learning Environment Setup. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Moreover, we will see how to install Python. Also, we will learn about Python Libraries such as NumPy, Keras, TensorFlow, Matplotlib, and Theano. After this, we will look at Python Hardware i.e., GPU and CPU. Next, in the Python Deep Learning Environment Setup Tutorial, we also discuss the <a href=\"https:\/\/data-flair.training\/blogs\/python-tutorial-for-beginners\/\" target=\"_blank\" rel=\"noopener\"><strong>Python<\/strong> <\/a>Text Editor and Python Pip. <\/span><\/p>\n<p><span style=\"font-weight: 400\">You will have every necessary library installed, an environment created, and you will be able to implement deep learning. <\/span><\/p>\n<p><span style=\"font-weight: 400\">So, let\u2019s begin Python Deep Learning Environment Setup.<\/span><\/p>\n<h3><strong>How to Install Python?<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Before moving to Python Deep Learning Environment Setup, you will need to install Python. You can download it from Python\u2019s official website-<\/span><\/p>\n<p><strong><a href=\"https:\/\/www.python.org\/downloads\/\">https:\/\/www.python.org\/downloads\/<\/a><\/strong><\/p>\n<p><span style=\"font-weight: 400\">The latest version at the time of writing is 3.7.<\/span><\/p>\n<div id=\"attachment_24568\" style=\"width: 690px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/python.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-24568\" class=\"wp-image-24568 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/python.png\" alt=\"Python Deep Learning Environment Setup\" width=\"680\" height=\"422\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/python.png 680w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/python-150x93.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/python-300x186.png 300w\" sizes=\"auto, (max-width: 680px) 100vw, 680px\" \/><\/a><p id=\"caption-attachment-24568\" class=\"wp-caption-text\">How to Install Python?<\/p><\/div>\n<h4><strong>a. Installing with Anaconda Python<\/strong><\/h4>\n<p><strong>Alternatively, you can get Python with Anaconda:<\/strong><br \/>\n<strong><a href=\"https:\/\/www.anaconda.com\/download\/\" target=\"_blank\" rel=\"noopener\">https:\/\/www.anaconda.com\/download\/<\/a><\/strong><\/p>\n<div id=\"attachment_24569\" style=\"width: 457px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/anaconda-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-24569\" class=\"wp-image-24569 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/anaconda-1.png\" alt=\"Python Deep Learning Enironment Setup\" width=\"447\" height=\"223\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/anaconda-1.png 447w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/anaconda-1-150x75.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/anaconda-1-300x150.png 300w\" sizes=\"auto, (max-width: 447px) 100vw, 447px\" \/><\/a><p id=\"caption-attachment-24569\" class=\"wp-caption-text\">Install Anaconda Python<\/p><\/div>\n<p><span style=\"font-weight: 400\">In both cases of installing Python and Anaconda Python, choose the configurations that support your machine.<\/span><br \/>\n<span style=\"font-weight: 400\">For a detailed guide on how to install Python on Windows, read <\/span><strong><a href=\"https:\/\/data-flair.training\/blogs\/install-python-windows\/\" target=\"_blank\" rel=\"noopener\">A Step-by-Step Guide to Install Python Windows<\/a><\/strong><span style=\"font-weight: 400\">.<\/span><\/p>\n<h3><strong>Python Libraries<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Now, in Python Deep Learning Environment Setup, first, take a look at what libraries we will need for deep learning with Python. We will show you how to install these <strong><a href=\"https:\/\/data-flair.training\/blogs\/python-library\/\" target=\"_blank\" rel=\"noopener\">Python Libraries<\/a><\/strong>.<\/span><\/p>\n<h4><strong>a. Python NumPy<\/strong><\/h4>\n<div id=\"attachment_24571\" style=\"width: 2592px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/NumPy-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-24571\" class=\"wp-image-24571 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/NumPy-1.png\" alt=\"Pyhton Deep Learning Environment Setup\" width=\"2582\" height=\"1024\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/NumPy-1.png 2582w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/NumPy-1-150x59.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/NumPy-1-300x119.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/NumPy-1-768x305.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/NumPy-1-1024x406.png 1024w\" sizes=\"auto, (max-width: 2582px) 100vw, 2582px\" \/><\/a><p id=\"caption-attachment-24571\" class=\"wp-caption-text\">Python NumPy<\/p><\/div>\n<p><span style=\"font-weight: 400\"><strong><a href=\"https:\/\/data-flair.training\/blogs\/python-numpy-tutorial\/\">NumPy<\/a><\/strong> is a Python library for working on large, multi-dimensional arrays and matrices. Python NumPy also supports many high-level mathematical functions to operate on these.<\/span><\/p>\n<p><span style=\"font-weight: 400\">To install this, use Python pip-<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">pip install numpy<\/pre>\n<h4><strong>b. Keras Python<\/strong><\/h4>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/keras.png\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-24572 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/keras.png\" alt=\"Python Deep Learning Environment Setup\" width=\"1200\" height=\"348\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/keras.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/keras-150x44.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/keras-300x87.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/keras-768x223.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/keras-1024x297.png 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400\">Keras is a <a href=\"https:\/\/data-flair.training\/blogs\/neural-network-algorithms\/\" target=\"_blank\" rel=\"noopener\"><strong>neural network <\/strong><\/a>library for Python that is open-source and written in Python. Python Keras can run on top of TensorFlow, Theano Python, or Microsoft Cognitive Toolkit.<\/span><\/p>\n<p><span style=\"font-weight: 400\">You can install it with Python pip-<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">pip install keras<\/pre>\n<h4><strong>c. Theano Python<\/strong><\/h4>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/theano.png\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-24573 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/theano.png\" alt=\"Python Deep Learning Environment Setup\" width=\"179\" height=\"115\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/theano.png 179w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/theano-150x96.png 150w\" sizes=\"auto, (max-width: 179px) 100vw, 179px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400\">Theano is a Python library for numerical computation. Python Theano uses a NumPy-esque syntax to express computation; it compiles these to run efficiently on CPU or GPU architectures.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Install it using Python pip-<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">pip install theano<\/pre>\n<h4><strong>d. TensorFlow Python<\/strong><\/h4>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/tensorflow.png\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-24574 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/tensorflow.png\" alt=\"Python Deep Learning Environment Setup\" width=\"2000\" height=\"1667\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/tensorflow.png 2000w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/tensorflow-150x125.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/tensorflow-300x250.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/tensorflow-768x640.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/tensorflow-1024x854.png 1024w\" sizes=\"auto, (max-width: 2000px) 100vw, 2000px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400\"><a href=\"https:\/\/data-flair.training\/blogs\/tensorflow-tutorial\/\"><strong>TensorFlow<\/strong><\/a> is a Python library by Google that aims at fast numerical computing. We can use TensorFlow Python to create <a href=\"https:\/\/data-flair.training\/blogs\/artificial-neural-network-model\/\"><strong>Deep Learning models<\/strong><\/a> either directly or by using wrapper libraries.<\/span><\/p>\n<p><strong>Features of TensorFlow Python:<\/strong><\/p>\n<ul>\n<li>It can work effectively on a single device as it does on other complicated devices.<\/li>\n<li>It has tensorboards, which make debugging of nodes easier.<\/li>\n<li>It has a better visualization power than any other library.<\/li>\n<\/ul>\n<h4><strong>e. Python Matplotlib<\/strong><\/h4>\n<div id=\"attachment_24575\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/matplotlib-2.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-24575\" class=\"wp-image-24575 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/matplotlib-2.png\" alt=\"Python Deep Learning Environment Setup\" width=\"1200\" height=\"220\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/matplotlib-2.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/matplotlib-2-150x28.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/matplotlib-2-300x55.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/matplotlib-2-768x141.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/matplotlib-2-1024x188.png 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-24575\" class=\"wp-caption-text\">Python Matplotlib<\/p><\/div>\n<p><span style=\"font-weight: 400\"><strong><a href=\"https:\/\/data-flair.training\/blogs\/python-matplotlib-tutorial\/\">Matplotlib<\/a><\/strong> is a Python library for 2D plotting and can work together with NumPy. <\/span><\/p>\n<p><strong>Features of Python Matplotlib:<\/strong><\/p>\n<ul>\n<li>It creates a huge variety of visualizations by using bar graphs, line plots, histograms, etc.<\/li>\n<li>It generates high-quality graphs by maintaining its aesthetics.<\/li>\n<li>Your graphs can be customized from colors, markers, to annotations.<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Install it with Python pip-<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">pip install matplotlib<\/pre>\n<h3><strong>Python Text Editor<\/strong><\/h3>\n<div id=\"attachment_24577\" style=\"width: 334px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/jupyter-1-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-24577\" class=\"wp-image-24577 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/jupyter-1-1.png\" alt=\"Python Deep Learning Environment Setup\" width=\"324\" height=\"298\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/jupyter-1-1.png 324w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/jupyter-1-1-150x138.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/jupyter-1-1-300x276.png 300w\" sizes=\"auto, (max-width: 324px) 100vw, 324px\" \/><\/a><p id=\"caption-attachment-24577\" class=\"wp-caption-text\">Python Text Editor &#8211; Jupyter Notebook<\/p><\/div>\n<p><span style=\"font-weight: 400\">For Python Deep Learning Environment Setup, we recommend that you use the Jupyter Notebook. It observes the following benefits:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Thoroughly record steps and results.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Isolate code by splitting the file into cells.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Automatic terminal statements below code cells.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Ability to write Markdown, LaTeX, and <strong>Python code<\/strong> in one file.<\/span><\/li>\n<\/ul>\n<p><strong>Have a look at Python Deep Learning Computational Graphs<\/strong><br \/>\n<span style=\"font-weight: 400\">You can later save your code as a <strong>.pTensorFlowy file<\/strong>.<\/span><\/p>\n<div id=\"attachment_24578\" style=\"width: 781px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/jupyter2-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-24578\" class=\"wp-image-24578 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/jupyter2-1.png\" alt=\"Python Deep Learning Environment Setup\" width=\"771\" height=\"415\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/jupyter2-1.png 771w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/jupyter2-1-150x81.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/jupyter2-1-300x161.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/jupyter2-1-768x413.png 768w\" sizes=\"auto, (max-width: 771px) 100vw, 771px\" \/><\/a><p id=\"caption-attachment-24578\" class=\"wp-caption-text\">Jupyter Notebook<\/p><\/div>\n<h3><strong>Python Hardware<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">To run your deep learning code in Python, you can use Python Hardware, either a CPU or a GPU.<\/span><\/p>\n<h4><strong>a. Central Processing Unit (CPU)<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">CPUs perform narrow tasks serially. You would need at least a recent quad-core CPU to not bottleneck the GPU.<\/span><\/p>\n<h4><strong>b. Graphics Processing Unit (GPU)<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Although a GPU is meant for video simulation and gaming, they have a parallel architecture. This makes them suitable for vector and matrix operations.<\/span><\/p>\n<h3>Optimizing Hardware Utilization<\/h3>\n<p>There is always a trade-off between the CPU and the GPU while establishing a deep learning environment. Thus, the performance of data can be accelerated by GPUs during the model training and evaluation processes because of their parallel computing nature. However, in some cases or for particular smaller models, the powerful CPU is enough. Make sure that the chosen HW configuration corresponds to the defined project conditions and the size of the data.<\/p>\n<p>Besides, having the drivers and libraries updated is important to enhance the functionality of the equipment. Specifically, the GPU\u2019s driver is crucial for determining computation rate and integrability with depth learning frameworks. It is wise to update templates and configurations from time to time and ensure that your deep learning is as efficient as possible.<\/p>\n<p>So, this was all in the Python Deep Learning Environment Setup. Hope you like our explanations.<\/p>\n<h3><strong>Conclusion<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Hence, in this Python Deep Learning Environment Setup Tutorial, we discussed Python Installation, Python Libraries i.e., NumPy, Keras, TensorFlow, Matplotlib, and Theano. Moreover, we discussed Python text editor and Python hardware, such as GPU and CPU. <\/span><\/p>\n<p>Once setup is done, test it by writing a small model and training it on simple data. This confirms everything works fine. If your system is strong, install CUDA for GPU training. A good setup makes training faster and allows bigger models. With Python\u2019s ecosystem, you\u2019re ready to explore real deep learning projects.<\/p>\n<p><span style=\"font-weight: 400\">Still, if any doubt regarding Deep Learning with Python, ask in the comments tab.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Moving on from introducing Deep Learning, now, we guide you to set up your machine for your journey with Deep Learning using Python. So, today in this Deep Learning with Python Tutorial, we will&#46;&#46;&#46;<\/p>\n","protected":false},"author":5,"featured_media":24566,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36,46],"tags":[3674,5122,5149,6223,6789,7994,7995,8601,9170,10480,10481,10576,10638,10676,10727,10759,10819,10887,12635,14519,14598,14697,14698],"class_list":["post-24536","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","category-python","tag-deep-learning-with-python","tag-gpu","tag-graphics-processing-unit","tag-how-to-install-python","tag-install-python","tag-keras","tag-keras-python","tag-matplotlib","tag-numpy","tag-python-deep-learning-environment-setup","tag-python-deep-learning-installation","tag-python-hardware","tag-python-libraries","tag-python-matplotlib","tag-python-numpy","tag-python-pip","tag-python-scipy","tag-python-text-editor","tag-scipy","tag-tensorflow","tag-tensorflow-python","tag-theano","tag-theano-python"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - 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