Building Neural Networks with PyTorch in Google Colab

Deep Learning with PyTorch in Google Colab

PyTorch and Google Colab have become synonymous with Deep Learning as they provide people with an easy and affordable way to quickly get started building their own neural networks and training models. GPUs aren’t cheap, which makes building your own custom workstation challenging for many. Although the cost of a deep learning workstation can be a barrier to many, these systems have become more affordable recently thanks to the lower cost of NVIDIA’s new RTX 30 series.

Even with more affordable options of having your own deep learning system on hand, many people still flock to using PyTorch and Google Colab as they get used to working with deep learning projects.

Source

**PyTorch and Google Colab are Powerful for Developing Neural Networks **

PyTorch was developed by Facebook and has become famous among the Deep Learning Research Community. It allows for parallel processing and has an easily readable syntax that caused an uptick in adoption. PyTorch is generally easier to learn and lighter to work with than TensorFlow, and is great for quick projects and building rapid prototypes. Many use PyTorch for computer vision and natural language processing (NLP) applications.

Google Colab was developed by Google to help the masses access powerful GPU resources to run deep learning experiments. It offers GPU and TPU support and integration with Google Drive for storage. These reasons make it a great choice for building simple neural networks, especially compared to something like Random Forest.

#overviews #deep learning #google colab #neural networks #python #machine-learning

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Building Neural Networks with PyTorch in Google Colab

Deep Learning with PyTorch in Google Colab

PyTorch and Google Colab have become synonymous with Deep Learning as they provide people with an easy and affordable way to quickly get started building their own neural networks and training models. GPUs aren’t cheap, which makes building your own custom workstation challenging for many. Although the cost of a deep learning workstation can be a barrier to many, these systems have become more affordable recently thanks to the lower cost of NVIDIA’s new RTX 30 series.

Even with more affordable options of having your own deep learning system on hand, many people still flock to using PyTorch and Google Colab as they get used to working with deep learning projects.

Source

**PyTorch and Google Colab are Powerful for Developing Neural Networks **

PyTorch was developed by Facebook and has become famous among the Deep Learning Research Community. It allows for parallel processing and has an easily readable syntax that caused an uptick in adoption. PyTorch is generally easier to learn and lighter to work with than TensorFlow, and is great for quick projects and building rapid prototypes. Many use PyTorch for computer vision and natural language processing (NLP) applications.

Google Colab was developed by Google to help the masses access powerful GPU resources to run deep learning experiments. It offers GPU and TPU support and integration with Google Drive for storage. These reasons make it a great choice for building simple neural networks, especially compared to something like Random Forest.

#overviews #deep learning #google colab #neural networks #python #machine-learning

Build your own Neural Network for CIFAR-10 using PyTorch

In 6 simple steps

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Neural network seems like a black box to many of us. What happens inside it, how does it happen, how to build your own neural network to classify the images in datasets like MNIST, CIFAR-10 etc. are the questions that keep popping up. Let’s try to understand a Neural Network in brief and jump towards building it for CIFAR-10 dataset. By the end of this article you will have answers to :

1. What are neural networks?
2. How to build a neural network model for cifar-10 dataset by using PyTorch?

What are neural networks?

Neural networks(NN) are inspired by the human brain. A neuron in a human brain, individually is at rest until it collects signals from others through a structure called dendrites, when the excitation that it receives is sufficiently high, the neuron is fired up(gets activated) and it passes on the information. Artificial neural networks(ANN) are made up of interconnected model/artificial neurons(known as perceptron) that take many weighted inputs , add them up and pass it through a non-linearity to produce an output. Sounds simple!

#neural-networks #machine-learning #pytorch #cifar-10 #neural networks

Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

Embedding your <image> in google colab <markdown>

This article is a quick guide to help you embed images in google colab markdown without mounting your google drive!

Just a quick intro to google colab

Google colab is a cloud service that offers FREE python notebook environments to developers and learners, along with FREE GPU and TPU. Users can write and execute Python code in the browser itself without any pre-configuration. It offers two types of cells: text and code. The ‘code’ cells act like code editor, coding and execution in done this block. The ‘text’ cells are used to embed textual description/explanation along with code, it is formatted using a simple markup language called ‘markdown’.

Embedding Images in markdown

If you are a regular colab user, like me, using markdown to add additional details to your code will be your habit too! While working on colab, I tried to embed images along with text in markdown, but it took me almost an hour to figure out the way to do it. So here is an easy guide that will help you.

STEP 1:

The first step is to get the image into your google drive. So upload all the images you want to embed in markdown in your google drive.

Step 2:

Google Drive gives you the option to share the image via a sharable link. Right-click your image and you will find an option to get a sharable link.

On selecting ‘Get shareable link’, Google will create and display sharable link for the particular image.

#google-cloud-platform #google-collaboratory #google-colaboratory #google-cloud #google-colab #cloud

No Code introduction to Neural Networks

The simple architecture explained

Neural networks have been around for a long time, being developed in the 1960s as a way to simulate neural activity for the development of artificial intelligence systems. However, since then they have developed into a useful analytical tool often used in replace of, or in conjunction with, standard statistical models such as regression or classification as they can be used to predict or more a specific output. The main difference, and advantage, in this regard is that neural networks make no initial assumptions as to the form of the relationship or distribution that underlies the data, meaning they can be more flexible and capture non-standard and non-linear relationships between input and output variables, making them incredibly valuable in todays data rich environment.

In this sense, their use has took over the past decade or so, with the fall in costs and increase in ability of general computing power, the rise of large datasets allowing these models to be trained, and the development of frameworks such as TensforFlow and Keras that have allowed people with sufficient hardware (in some cases this is no longer even an requirement through cloud computing), the correct data and an understanding of a given coding language to implement them. This article therefore seeks to be provide a no code introduction to their architecture and how they work so that their implementation and benefits can be better understood.

Firstly, the way these models work is that there is an input layer, one or more hidden layers and an output layer, each of which are connected by layers of synaptic weights¹. The input layer (X) is used to take in scaled values of the input, usually within a standardised range of 0–1. The hidden layers (Z) are then used to define the relationship between the input and output using weights and activation functions. The output layer (Y) then transforms the results from the hidden layers into the predicted values, often also scaled to be within 0–1. The synaptic weights (W) connecting these layers are used in model training to determine the weights assigned to each input and prediction in order to get the best model fit. Visually, this is represented as:

#machine-learning #python #neural-networks #tensorflow #neural-network-algorithm #no code introduction to neural networks