本课程零基础入门PyTorch，覆盖张量、自动求导、训练循环，深入CNN、LSTM、ViT及GAN，并教授混合精度、分布式训练、模型部署等企业级技能，助力开发者掌握深度学习全流程。
原始标题：Master PyTorch Library Completely 2026 + UPDATES
本课程是一门面向 Python 开发者、数据科学家及科研人员的零基础、项目驱动型 PyTorch 与深度学习全栈实战指南。课程内容由浅入深，前期聚焦于 PyTorch 核心底层的硬核技术，涵盖张量运算（Tensors）、自动求导机制（Autograd）、自定义数据集加载（DataLoaders）以及手写完整训练循环（前向传播、损失计算、反向传播与优化器更新）；同时，课程重点培养学员的工程实战与排错能力，详细剖析了维度不匹配（Shape mismatches）、CUDA 显存溢出、梯度消失/爆炸（NaN）等现实开发中最常见的 Bug 调试技巧。
在进阶与应用层面，课程不仅覆盖了传统深度学习架构，还直击工业级部署与前沿技术的核心痛点。学员将通过丰富的 Jupyter Notebook 实战项目，亲手构建从传统的 CNN 图像分类、LSTM 文本情感分析、时间序列预测，到前沿的 Vision Transformers (ViT) 和生成对抗网络（GAN）；更难能可贵的是，课程还倾囊相授了企业级落地必备的高级技能，包括混合精度训练（AMP）、分布式数据并行（DDP）、模型量化，以及如何利用 TorchScript、ONNX 和 TensorRT 将模型成功部署到生产环境，真正实现从算法理论到商业化落地的全流程闭环。
Published 8/2026
Created by Shayan Janati
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 60 Lectures ( 6h 55m ) | Size: 5.6 GB
From Zero to Expert: Build, Train, and Deploy Deep Learning Models with PyTorch – Tensors, Autograd, CNNs, RNNs …
What you’ll learn
⚡ Master PyTorch tensors, operations, and automatic differentiation (autograd)
⚡ Build neural networks with nn.Module, nn.Linear, nn.Conv2d, nn.LSTM, and more
⚡ Write complete training loops: forward pass, loss, backward, optimizer step
⚡ Use optimisers, learning rate schedulers, and regularisation techniques
⚡ Debug common errors: shape mismatches, CUDA errors, NaN gradients, overfitting
⚡ Create custom datasets and dataloaders for any type of data
⚡ Build real‑world projects: image classification, sentiment analysis, time‑series forecasting, transfer learning, GANs, and autoencoders
⚡ Deploy models with TorchScript, ONNX, and TensorRT
⚡ Use mixed precision, distributed training, and model quantisation
⚡ Understand and implement advanced architectures like Vision Transformers, Siamese Networks, and more
Requirements
❗ Basic Python programming knowledge (functions, classes, loops)
❗ A computer with internet access (GPU optional; all code runs on CPU as well)
❗ Basic familiarity with NumPy and Matplotlib is helpful but not required
❗ No prior deep learning experience needed – everything is explained from scratch
Description
Master PyTorch Library Completely is the ultimate hands-on course for Python developers, data scientists, and machine learning engineers who want to master PyTorch and deep learning from the ground up. This comprehensive course covers every essential PyTorch concept with practical coding exercises in every single lecture. You will start with the fundamentals – tensors, tensor operations, and PyTorch’s powerful autograd engine for automatic differentiation – before moving into neural network building blocks such as nn.Linear, nn.Conv2d, nn.LSTM, and custom nn.Module architectures. The course thoroughly covers the complete training loop, loss functions, optimisers, learning rate schedulers, regularisation, and reproducibility. You will also master debugging techniques for shape mismatches, CUDA errors, and gradient problems, ensuring you can handle real-world challenges confidently.
But this course goes far beyond theory. You will complete multiple mini-projects and real-world projects, including linear and logistic regression, a neural network on MNIST, a CNN on CIFAR-10, transfer learning with ResNet, sentiment analysis with LSTM, an autoencoder for dimensionality reduction, time-series forecasting, and even a Generative Adversarial Network (GAN) for synthetic image generation. Additionally, the course includes advanced topics such as Vision Transformers, graph neural networks, deployment with TorchScript and ONNX, mixed precision training, distributed data parallel, and model quantisation.
By the end of this course, you will have a deep, practical understanding of PyTorch and the confidence to build, train, debug, and deploy state-of-the-art deep learning models. All code is provided in interactive Jupyter notebooks with clear explanations, exercises, and solutions, making this the most complete PyTorch course available.
Who this course is for
⭐ Python developers who want to break into deep learning and AI
⭐ Data scientists and ML engineers transitioning from TensorFlow or Keras
⭐ Students and researchers who need PyTorch for academic or real‑world projects
⭐ Anyone who prefers a hands‑on, project‑based approach to learning PyTorch
⭐ Learners who want to go beyond basics and master deployment, optimisation, and advanced architectures
此处内容需要权限查看
会员免费查看
