PyTorch (Machine Learning Library)

PyTorch is an open-source machine learning library, designed for maximum flexibility and speed in the implementation of deep neural networks. Coursera's PyTorch catalogue teaches you about this powerful tool created by Facebook's artificial intelligence research group. You'll learn everything from the basics of PyTorch, tensor operations, auto gradients, to the designing and training of neural networks for machine learning and artificial intelligence. You'll also explore practical applications of PyTorch in image and language processing, reinforcement learning, and more. This skill is essential for AI researchers, data scientists and anyone interested in the field of machine learning.

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Results for "PyTorch (Machine Learning Library)"

  • Skills you'll gain: Plotly, PyTorch (Machine Learning Library), NumPy, Matplotlib, Pandas (Python Package), Plot (Graphics), Data Visualization Software, Interactive Data Visualization, Machine Learning Methods, Python Programming, Applied Machine Learning, Scatter Plots, Numerical Analysis, Data Manipulation, Deep Learning, Image Analysis, Linear Algebra, Data Wrangling

  • Skills you'll gain: PyTorch (Machine Learning Library), Model Deployment, Hugging Face, Model Optimization, Fine-tuning, Convolutional Neural Networks, Transfer Learning, Data Quality, Generative AI, Data Manipulation, Deep Learning, Generative Model Architectures, Model Training, Image Analysis, MLOps (Machine Learning Operations), Large Language Modeling, Data Pipelines, Artificial Neural Networks, Computer Vision, Natural Language Processing

  • Skills you'll gain: Generative Model Architectures, Recurrent Neural Networks (RNNs), Fine-tuning, Generative AI, MLOps (Machine Learning Operations), Generative Adversarial Networks (GANs), Vision Transformer (ViT), PyTorch (Machine Learning Library), Artificial Neural Networks, Convolutional Neural Networks, AI Workflows, Hugging Face, Image Analysis, Deep Learning, Model Deployment, Autoencoders, Machine Learning, Computer Science, Algorithms, Data Science

  • Skills you'll gain: PyTorch (Machine Learning Library), Fine-tuning, Convolutional Neural Networks, Deep Learning, Natural Language Processing, Embeddings, Hugging Face, Computer Vision, Supervised Learning, Classification Algorithms, Data Preprocessing, Predictive Modeling, Machine Learning, Data Processing, Artificial Intelligence and Machine Learning (AI/ML), Statistical Methods, Probability & Statistics, Machine Learning Algorithms

  • Skills you'll gain: Prompt Engineering, Large Language Modeling, Retrieval-Augmented Generation, Generative AI, PyTorch (Machine Learning Library), Prompt Patterns, Generative AI Agents, Fine-tuning, Vector Databases, LLM Application, Generative Model Architectures, Generative Adversarial Networks (GANs), Embeddings, Natural Language Processing, Tool Calling, Hugging Face, Model Optimization, Reinforcement Learning, Transfer Learning, Data Pipelines

  • Skills you'll gain: Model Deployment, Fine-tuning, PyTorch (Machine Learning Library), Model Evaluation, Model Training, Vision Transformer (ViT), Model Optimization, Transfer Learning, MLOps (Machine Learning Operations), Natural Language Processing, Debugging, Containerization, Kubernetes, Docker (Software), Distributed Computing, Performance Tuning, Tensorflow, Deep Learning, Cloud Computing, Data Pipelines

  • Skills you'll gain: PyTorch (Machine Learning Library), Model Optimization, Transfer Learning, Convolutional Neural Networks, Artificial Neural Networks, Deep Learning, Model Training, Logistic Regression, Image Analysis, Applied Machine Learning, Model Evaluation, Computer Vision, Classification Algorithms, Supervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Statistical Methods

  • Skills you'll gain: Model Deployment, Unit Testing, MLOps (Machine Learning Operations), Kubernetes, Docker (Software), Containerization, Test Driven Development (TDD), Application Deployment, Continuous Integration, Software Testing, Model Training, CI/CD, Scalability, Scikit Learn (Machine Learning Library), Tensorflow, PyTorch (Machine Learning Library), Performance Tuning, Python Programming, Software Engineering, Git (Version Control System)

  • Skills you'll gain: Supervised Learning, Model Optimization, PyTorch (Machine Learning Library), Fine-tuning, Generative Model Architectures, Convolutional Neural Networks, Recurrent Neural Networks (RNNs), Generative AI, Deep Learning, Model Training, Applied Machine Learning, Statistical Machine Learning, Classification And Regression Tree (CART), Autoencoders, Machine Learning Methods, Machine Learning Software, Machine Learning, MLOps (Machine Learning Operations), Machine Learning Algorithms, Artificial Intelligence and Machine Learning (AI/ML)

  • Skills you'll gain: Large Language Modeling, Deep Learning, Prompt Engineering, Image Analysis, Model Deployment, Recurrent Neural Networks (RNNs), PyTorch (Machine Learning Library), Convolutional Neural Networks, Model Optimization, Tensorflow, LLM Application, Transfer Learning, Computer Vision, Responsible AI, Generative Model Architectures, Model Training, Natural Language Processing, Embeddings, Generative AI, Artificial Neural Networks

  • Skills you'll gain: Generative AI, Large Language Modeling, PyTorch (Machine Learning Library), Generative Model Architectures, Multimodal Prompts, Fine-tuning, Image Analysis, Model Evaluation, Autoencoders, Hugging Face, Embeddings, Computer Vision, Convolutional Neural Networks, Artificial Neural Networks, LLM Application, Natural Language Processing, Deep Learning, Prompt Engineering, Model Training, Image Quality

  • Skills you'll gain: Prompt Engineering, Apache Spark, Large Language Modeling, Retrieval-Augmented Generation, PyTorch (Machine Learning Library), Computer Vision, Unsupervised Learning, Generative Model Architectures, Prompt Patterns, Generative AI, PySpark, Model Optimization, Keras (Neural Network Library), Supervised Learning, LLM Application, Vector Databases, Fine-tuning, Machine Learning, Python Programming, Data Science