A guide to getting started with deep learning on Linux, including a step-by-step instruction on how to install the necessary tools and libraries.
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Deep learning is a subset of machine learning that is concerned with algorithms inspired by the structure and function of the brain, known as artificial neural networks. Neural networks are capable of machine learning and pattern recognition. Deep learning is usually used to refer to the use of multiple layers in a neural network.
There are many software frameworks for deep learning, most of which are available on Linux. In this article, we will introduce you to some of the most popular deep learning frameworks and show you how to get started with them on your Linux machine.
What is Deep Learning?
Deep learning is a subset of machine learning that focuses on using neural networks to learn from data. Neural networks are similar to the brain in that they are made up of a series of interconnected nodes, or neurons. Deep learning algorithms use these neural networks to learn from data in a way that is similar to the way humans learn.
Deep learning has been shown to be effective for a variety of tasks, including image recognition, natural language processing, and time series prediction. Deep learning is often used in conjunction with other machine learning algorithms, such as support vector machines and decision trees.
If you are interested in getting started with deep learning on Linux, there are a few things you will need to do. First, you will need to install some software. Second, you will need to get some data to train your neural network on. And third, you will need to choose an appropriate deep learning algorithm for your task.
Installing Deep Learning Software on Linux
There are a number of different software packages that you can use for deep learning on Linux. The two most popular packages are TensorFlow and Keras.
TensorFlow is an open source deep learning package that was developed by Google. Keras is a high-level deep learning package that runs on top of TensorFlow (or Theano).
You can install TensorFlow and Keras using the pip command:
pip install tensorflow keras
Why Use Linux for Deep Learning?
Linux is a great choice for deep learning for several reasons. First, it’s free and open source, so you can get started without spending any money. Second, it’s highly customizable, so you can tailor your environment to your specific needs. And third, it’s widely used in the deep learning community, so you can find lots of resources and support.
What You’ll Need
To get started with deep learning on Linux, you’ll need a few things:
-A computer with a Linux operating system. I’ll be using Ubuntu 16.04 in this guide, but any recent version of Linux should work fine.
-A GPU. This is necessary for training deep learning models quickly. I’ll be using an NVIDIA GTX 1080 Ti, but any recent NVIDIA GPU should work fine.
-Deep learning frameworks such as TensorFlow, Keras, and PyTorch. I’ll be using TensorFlow in this guide.
-The CUDA toolkit and cuDNN library for TensorFlow (optional, but recommended). These libraries will greatly improve the performance of TensorFlow on a GPU.
If you’re new to deep learning and Linux, it’s best to start with a distribution that has all of the necessary software installed and configured. I recommend using Ubuntu 16.04 or 18.04, but other distributions should work as well. I’ll be using Ubuntu 18.04 for this guide.
To get started, you’ll need to install a few dependencies:
-TensorFlow: This is Google’s open-source framework for deep learning. You can install it using the instructions here.
-Keras: This is a high-level deep learning library that runs on top of TensorFlow (or Theano). You can install it using the instructions here.
-NVIDIA cuDNN: This is a GPU-accelerated library for deep learning. You’ll need to register for an NVIDIA Developer account and download the cuDNN v5.1 Library for Linux from here (select the “cuDNN v5.1 Library for Linux” option).
In order to get started with deep learning on Linux, you will need to configure your environment. This includes installing any dependencies that are required for your deep learning framework of choice, as well as setting up any necessary environment variables.
Once your environment is configured, you can then begin working with deep learning models. The process for doing this will vary depending on the framework that you are using. However, in general, you will first need to load a pre-trained model and then use it to make predictions on new data.
Training is the process of using an algorithm to learn from data. It starts with a large dataset and a set of parameters that define how the training process works. The algorithm then adjusts the parameters until it finds a set that results in accurate predictions on the training data.
The process of training a deep learning model can be computationally intensive, so it’s important to choose the right hardware. GPUs are well-suited for deep learning because they can perform the matrix operations required for training much faster than CPUs. For this reason, most deep learning research is conducted on GPUs.
If you’re just getting started with deep learning, you can use one of the many available cloud services. These services provide powerful GPUs that can be used for training. They also handle all of the set-up and maintenance required to run deep learning algorithms, so you can focus on building your models.
Once you’ve chosen your hardware, you’ll need to install some software. The two most popular deep learning frameworks are TensorFlow and Keras. TensorFlow is developed by Google and is used by many large companies such as Facebook, Uber, and Airbnb. Keras is a high-level framework that makes building models easy. It’s frequently used with TensorFlow, but it can also be used with other frameworks such as MXNet or PyTorch.
Once you’ve installed your software, you’re ready to start building models!
In machine learning, inference is the process of making predictions based on a trained model. In deep learning, this is often done by feeding an input through a neural network to produce an output. Inference can be done on a variety of devices, including CPUs, GPUs, and specialized acceleration hardware.
There are many different ways to do inference, but one common approach is to use a toolkit like TensorFlow or Caffe2. These toolkits allow you to define a neural network in code and then run it on any supported platform.
To get started with inference on Linux, you’ll need to install a deep learning toolkit and choose a device to run it on. If you’re not sure which toolkit to use, TensorFlow is a good option. You can install it using your package manager or from source.
Once TensorFlow is installed, you can choose which device you want to run it on. If you have a GPU available, you can use that for accelerated inference. Otherwise, your CPU will work just fine.
Once you have everything installed, you’re ready to start using deep learning for inference!
If you’re new to deep learning, Linux is a great platform to get started with. It’s open source, so you can get started without spending any money. There are many excellent deep learning frameworks available for Linux, such as TensorFlow, Caffe, and PyTorch. And, if you’re looking for a powerful GPU to train your models on, Linux is the way to go.
In this article, we’ve walked through how to install TensorFlow on Ubuntu 18.04. We’ve also showed you how to set up a GPU-accelerated environment with nvidia-docker. With all of that in place, you’re ready to start exploring deep learning!
If you want to learn more about deep learning on Linux, consider checking out some of the following resources:
-The Deep Learning Book by Geoffrey Hinton, Yoshua Bengio, and Aaron Courville: This book is considered to be the Bible of deep learning, and is a great place to start if you want to really understand the theory behind this field.
-Deep Learning 101 by Andrew Ng: This is a great tutorial that covers the basics of deep learning, and includes a number of practical exercises.
– Neural Networks and Deep Learning by Michael Nielsen: This online book offers a more in-depth look at neural networks and deep learning.
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