I used PyTorch for creating my machine learning projects. For example, my last project was called 'Code Parrot'. It was from an NLP Transformers book. I tried creating a chatbot which can autocomplete Python source code using Chat GPT. I collected the data for training from Google's BigQuery database, sorted it for only Python files, and then trained the tokenizer and subsequently trained models, Chat GPT 2.4, I guess.
Software Engineer Intern at a tech services company with 11-50 employees
Real User
Top 5
Aug 27, 2024
We are using it for deep learning to implement the deep learning algorithms. It is also quite good compared to TensorFlow. Many of them use PyTorch more than TensorFlow.
Associate Machine Learning Engineer at a tech services company with 501-1,000 employees
Real User
Top 5
Jul 15, 2024
I use the solution in my company primarily for building models. I took it up because I saw others using PyTorch in general. It has a whole graph structure that is automatically maintained behind it, which makes it suitable for building machine-learning models.
PyTorch is used to develop machine learning models. I use the products depending on the kind of project I work on, and though I use PyTorch, I am more of a Python person. The use of the products depends on the kind of project I deal with, and though I use PyTorch, I am more of a Python person who uses it for data engineering. I use PyTorch when I am developing or writing my research, but not at the start of my work. The use of the product depends on the project I work on in my company. Python is mostly enough to deal with the boom due to the introduction of generative AI, but when there is a need for fine-tuning, we go for PyTorch in our company.
We work a lot with text processing, vectorization, and other NLP tasks. Sometimes, we need to process websites, presentations, or optics quickly because they're used in user engines and other applications. We use PyTorch to test our implementations as well.
Our primary use case for this solution is training your mathematical models. We are a data science team that trains mathematical models with this solution. It can spin up VMs, and you can use it up, or in your local machines.
Associate Data Analyst at a financial services firm with 10,001+ employees
Real User
Apr 12, 2021
I work within the field of NLP and data logistics, but most of the information is stored in the description. I mostly use NLP algorithms to extract the sentiment or the similarity if they have a similarity score calculation. For example, something like the windshield cracking, or something extremely severe like a garage burning, or multiple car damage. The NLP tasks don't deal with the financial data directly but use extra information to get some insights. It's not really a good practice in India to use deep learning models directly on financial data because of audit reasons.
We've built this course as an introduction to deep learning. Deep learning is a field of machine learning utilizing massive neural networks, massive datasets, and accelerated computing on GPUs. Many of the advancements we've seen in AI recently are due to the power of deep learning. This revolution is impacting a wide range of industries already with applications such as personal voice assistants, medical imaging, automated vehicles, video game AI, and more.
In this course, we'll be...
I used PyTorch for creating my machine learning projects. For example, my last project was called 'Code Parrot'. It was from an NLP Transformers book. I tried creating a chatbot which can autocomplete Python source code using Chat GPT. I collected the data for training from Google's BigQuery database, sorted it for only Python files, and then trained the tokenizer and subsequently trained models, Chat GPT 2.4, I guess.
I use PyTorch for developing my own projects, such as artificial intelligence and machine learning projects.
We are using it for deep learning to implement the deep learning algorithms. It is also quite good compared to TensorFlow. Many of them use PyTorch more than TensorFlow.
I use the solution in my company primarily for building models. I took it up because I saw others using PyTorch in general. It has a whole graph structure that is automatically maintained behind it, which makes it suitable for building machine-learning models.
PyTorch is used to develop machine learning models. I use the products depending on the kind of project I work on, and though I use PyTorch, I am more of a Python person. The use of the products depends on the kind of project I deal with, and though I use PyTorch, I am more of a Python person who uses it for data engineering. I use PyTorch when I am developing or writing my research, but not at the start of my work. The use of the product depends on the project I work on in my company. Python is mostly enough to deal with the boom due to the introduction of generative AI, but when there is a need for fine-tuning, we go for PyTorch in our company.
We use the solution for reliability engineering. We apply ML techniques to predict product failures and identify the reasons behind those failures.
We work a lot with text processing, vectorization, and other NLP tasks. Sometimes, we need to process websites, presentations, or optics quickly because they're used in user engines and other applications. We use PyTorch to test our implementations as well.
Our primary use case for this solution is training your mathematical models. We are a data science team that trains mathematical models with this solution. It can spin up VMs, and you can use it up, or in your local machines.
I work within the field of NLP and data logistics, but most of the information is stored in the description. I mostly use NLP algorithms to extract the sentiment or the similarity if they have a similarity score calculation. For example, something like the windshield cracking, or something extremely severe like a garage burning, or multiple car damage. The NLP tasks don't deal with the financial data directly but use extra information to get some insights. It's not really a good practice in India to use deep learning models directly on financial data because of audit reasons.
It is mostly used for everything that is connected to machine learning, data science, training, and other similar things.