⚡️ Let's learn how to code very simple neural network with #PyTorch 
It is not something state-of-the-art but very basic model to give you idea how to make actual #MachineLearning model.

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Let's learn about using API for most common tasks with #PyTorch (Note: Code of this thread is via official docs)
So, Let's build simple classifier with #PyTorch (Language: #Python)

First, we need to import required libraries. 
Note: For different kinds of #data, #torch provide TorchVision, TorchText and TorchAudio.

Here, we will be using image data. 

Let's load data now.
As torch provides various #datasets, you can use datasets like #MNIST, #coco etc. directly without downloading them externally.

All included datasets have two args: "transform" and "target_transform". They modify samples and labels respectively. 

We need to wrap dataset in DataLoader object to make it iterable. We select batch size to 32 here but you can go with any reasonable number. 

You can check shape of dataloaders with this: 

Now crucial part, we are creating #model and in #PyTorch, we create class which inherits from "nn.Module".
Layers of network goes into "__init__" function. The order of layers in forward pass is written in "forward" function.
We are checking GPU availibility at the start. 

Now, let's code training part for which, we need #optimizer and #loss function. #PyTorch provides us with code directly from #library. 

We are writing #training function now where model make prediction, compare it with ground truth and make adjustments as per difference between predicted class and ground truth. 

We need to test model for test dataset too. We will write separate function for the same. Note that we are not supposed to get #optimizer here as this part does not contribute to training. 

Now, we run actual training for 5 epochs. Results are not great but we haven't optimized our network plus number of epochs is also small but high accuracy was not target of this experiment (maybe one more thread for same). 

Now, we cannot train model every time so we save model for future usage of same. It just take couple of lines to save trained model. 

Now, we can load saved model and use the same for prediction. Let's first see how to load model created in #PyTorch 

Now, for final part, we will use saved model to make actual prediction on data, this is how it can be done with just few lines of code with #PyTorch 

Congratulations 🥳! You created, trained and used entire neural network with #PyTorch. It was really easy all thanks to creators of #PyTorch. You can literally code neural network without knowing all complex math behind it.

That's all for today!
If you liked what you just read, RT first tweet.
Do consider following @BurstNeuron for similar content about #MachineLearning , #DeepLearning , #DataScience , #Artificial_Intelligence and #Python 

Thanks for reading. 🙏🙏🙏

Note: One tweet went missing (no idea why). Here it is.

We need to define #Optimizer and Loss functions in model before training. 

Code Simple Neural Network with PyTorch

🤖💻 Are you passionate about ML and looking to build powerful #DeepLearning models? Then, #PyTorch is the perfect tool for you! With an intuitive API, dynamic computational graph, and large community, PyTorch makes building complex neural networks easy and fun. 🚀

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🔍 First of all, PyTorch is an #opensource machine learning library developed by Facebook's AI research team. It's built on top of #Python and provides a flexible way to build and train deep learning models.
#deeplearning #DataScience

💪 PyTorch is known for its ease of use and flexibility. It provides a simple and intuitive API that makes it easy to build and train complex neural networks.

📈 PyTorch is also great for experimentation and research. It provides a dynamic computational graph that allows you to easily modify your model during runtime. This means you can experiment with different architectures and hyperparameters without having to recompile your code.

👩‍💻 PyTorch has a large and active community. This means you can easily find tutorials, examples, and support online. There are also many pre-trained models available, which can save you a lot of time and effort.

👨‍🏫 PyTorch also has excellent documentation and resources for beginners. If you're new to deep learning, PyTorch is a great place to start. There are many tutorials and courses available that can teach you the basics of deep learning and PyTorch.

🎓 Finally, PyTorch is widely used in industry and academia. By learning PyTorch, you'll be gaining a valuable skill that is in high demand in the job market.

So, if you're interested in deep learning and machine learning, PyTorch is definitely worth learning! 🔥 #PyTorch #DeepLearning #MachineLearning #ArtificialIntelligence #AI #OpenSource #Python  #NeuralNetworks

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Thanks!

Why you should use PyTorch?

Lot of amazing work is happening in the field of ML.

🤖 Thinking about learning #MachineLearning? You're in luck! In this thread, I'll be sharing some of the benefits of learning this exciting field.
#DataScience #100DaysOfCode 

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💰 One of the biggest benefits of learning #MachineLearning is the potential for #career advancement. Machine learning skills are in high demand across a wide range of industries, including #finance, #healthcare, and #technology.

🧠 Another benefit of learning #MachineLearning is the opportunity to develop new skills and knowledge. Machine learning requires a deep understanding of #mathematics, #statistics, and computer science, and learning these skills can be highly rewarding.

📈 #MachineLearning can also help you make better decisions. By analyzing large amounts of data, machine learning algorithms can identify patterns and insights that would be difficult or impossible to see with the naked eye.

🌐 Additionally, #MachineLearning can be used to solve complex problems and create new opportunities. From personalized marketing to self-driving cars, machine learning is powering some of the most exciting innovations of our time.

🤝 Finally, #MachineLearning can be used to make a positive impact on the world. By using machine learning to solve problems in #healthcare, #education, and the #environment, we can create a better world for everyone.

I hope you found this thread informative and inspiring! Let me know in the comments below why you're interested in learning #MachineLearning. 👇

If you are interested in more such content, do consider following @BurstNeuron 

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Why I should learn Machine Learning?

🚀 Hey everyone! Are you interested in #MachineLearning and #DeepLearning, but not sure where to start? 
Don't worry, I've got you covered! In this thread, I'll be sharing some of the most important libraries you need to know for these fields.
#100DaysOfCode 
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🧮 Let's start with the basics: #NumPy and #Pandas. 
NumPy is a powerful library for numerical computing in #Python, while Pandas provides easy-to-use data structures for data analysis. 
These libraries are essential for data pre-processing and manipulation in #MachineLearning.

📊 Want to create stunning visualizations for your #data? 
Look no further than these amazing visualization libraries: #Matplotlib, #Seaborn and #Plotly. From basic charts to interactive dashboards, these libraries have got you covered! #DataVisualization #Python 🚀

🤖 Next up is #ScikitLearn. This library provides a wide range of machine learning #algorithms for classification, regression, clustering, and more. It also includes tools for model selection, evaluation, and data preprocessing. #MLAlgorithms

🧬 Another important library is #TensorFlow, which is a popular open-source platform for building and deploying #DeepLearning models. It provides a variety of tools for building and training neural networks, including Keras, a high-level API for building and testing models.

🐍 If you're a fan of #Python, you'll love #PyTorch, another popular deep learning library. It's known for its simplicity, flexibility, and ease of use, and is widely used in industry and academia for a variety of applications.
(It is more popular compared to #TensorFlow )

🤯 Last but not least, we have #OpenCV, a computer vision library that is often used in #MachineLearning and #DeepLearning projects. It includes a wide range of image and video processing tools, as well as machine learning algorithms for object detection and recognition.

I hope you found this list helpful! What other libraries would you add to this list? Let me know in the comments below! 🙌
For more content like this, consider following @BurstNeuron 
Do RT for better reach.
#DataScience #MachineLearning