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Deep Learning with PyTorch

Build neural networks from scratch. Master CNNs for images, RNNs for sequences, and Transformers using the research-standard PyTorch library.

Deep Learning powers the current AI boom. This course takes you from the basics of a single neuron to building complex architectures used in research and industry. You will master PyTorch tensors and the autograd engine to build neural networks from the ground up. Dive deep into Convolutional Neural Networks (CNNs) for computer vision and Recurrent Neural Networks (RNNs) for sequential data. We concludes with an introduction to Transformers, the architecture behind GPT and BERT.

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⏱️ 5-Minute Lessons (Bite-sized learning)
🚀 21-Lesson Path (Independent modules)
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Complete Course Syllabus

  • 1
    Tensors & Autograd
    PyTorch fundamentals and automatic differentiation engine.
  • 2
    Neural Net Basics
    Building layers, activation functions, and training loops.
  • 3
    CNNs for Vision
    Convolutional layers, pooling, and image classification.
  • 4
    RNNs for Sequences
    Handling temporal data with recurrent architectures.
  • 5
    Transformers Intro
    Attention mechanisms and the modern NLP architecture.

Estimated completion time: 21 lessons • Self-paced learning • Lifetime access

Career Outlook

Estimated Salary
$130k - $180k

Career Paths

Deep Learning Engineer $140k-$190k
AI Researcher $130k-$180k
Computer Vision Eng $135k-$175k

What You Will Learn

Design and train deep neural networks using PyTorch modules
Build CNNs to classify and analyze image data
Implement RNNs and LSTMs for time-series and text data
Debug training loops and optimize hyperparameters for convergence
Understand the architecture of Transformer models like BERT

Skills You Will Gain

PyTorch Neural Networks Computer Vision Backpropagation Deep Learning

Who Is This For

ML Engineers
Researchers
Data Scientists

Prerequisites

Machine Learning Basics
Calculus concepts

Deep Learning with PyTorch FAQs

Vs TensorFlow?

PyTorch is preferred for research and flexibility.

Hardware needed?

We use Google Colab (free cloud GPUs).

Math required?

Linear algebra and calculus are very helpful.

Is this generative AI?

It teaches the architecture (Transformers) behind GenAI.

Need more info?
All Reork courses are 100% free, self-paced, and fully accessible on mobile devices. No prior experience is required. Read our full Platform FAQ.

Tech Stack & Tools

PyTorch
Google Colab
CUDA
TensorBoard
Python

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Your Learning Path

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Deep Learning with PyTorch Basics

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Deep Learning with PyTorch for Beginners
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Advanced Deep Learning with PyTorch

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Advanced Deep Learning with PyTorch Course
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