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data Advanced 21 lessons

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.

PyTorch

Google Colab

CUDA

TensorBoard

Python

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Advanced Deep Learning with PyTorch

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Advanced Deep Learning with PyTorch Course

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