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Introduction to Deep Learning with PyTorch
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อัปเดตแล้ว 01/2569PyTorchArtificial Intelligence4 ชม.16 videos49 Exercises3,900 เอ็กซ์พี82,467คำแถลงแสดงความสำเร็จ
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Understanding the power of Deep Learning
Deep learning is everywhere: in smartphone cameras, voice assistants, and self-driving cars. It has even helped discover protein structures and beat humans at the game of Go. Discover this powerful technology and learn how to leverage it using PyTorch, one of the most popular deep learning libraries.Train your first neural network
First, tackle the difference between deep learning and "classic" machine learning. You will learn about the training process of a neural network and how to write a training loop. To do so, you will create loss functions for regression and classification problems and leverage PyTorch to calculate their derivatives.Evaluate and improve your model
In the second half, learn the different hyperparameters you can adjust to improve your model. After learning about the different components of a neural network, you will be able to create larger and more complex architectures. To measure your model performances, you will leverage TorchMetrics, a PyTorch library for model evaluation.Upon completion, you will be able to leverage PyTorch to solve classification and regression problems on both tabular and image data using deep learning. A vital capability for experienced data professionals looking to advance their careers.
ข้อกำหนดเบื้องต้น
Supervised Learning with scikit-learnIntroduction to NumPyPython Toolbox1
Introduction to PyTorch, a Deep Learning Library
Self-driving cars, smartphones, search engines... Deep learning is now everywhere. Before you begin building complex models, you will become familiar with PyTorch, a deep learning framework. You will learn how to manipulate tensors, create PyTorch data structures, and build your first neural network in PyTorch with linear layers.
2
Neural Network Architecture and Hyperparameters
To train a neural network in PyTorch, you will first need to understand additional components, such as activation and loss functions. You will then realize that training a network requires minimizing that loss function, which is done by calculating gradients. You will learn how to use these gradients to update your model's parameters.
3
Training a Neural Network with PyTorch
Now that you've learned the key components of a neural network, you'll train one using a training loop. You'll explore potential issues like vanishing gradients and learn strategies to address them, such as alternative activation functions and tuning learning rate and momentum.
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Evaluating and Improving Models
Training a deep learning model is an art, and to make sure our model is trained correctly, we need to keep track of certain metrics during training, such as the loss or the accuracy. We will learn how to calculate such metrics and how to reduce overfitting.
Introduction to Deep Learning with PyTorch
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