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Deep Learning Short Course

Neural networks and AI architectures. Master deep learning through bite-sized, swipeable learning tiles — designed for busy people who want to learn on the go.

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About This Deep Learning Course

This Deep Learning short course on SkillTiles breaks down everything you need to know into 10 structured milestones, each packed with bite-sized learning tiles you can swipe through in under 15 seconds each.

Whether you're a complete beginner or looking to refresh your knowledge, this online deep learning course uses AI-powered content to adapt to your learning pace. Each tile delivers a focused concept, example, or quiz — making it perfect for commutes, coffee breaks, or quick study sessions.

Covering 70 key topics across 10 milestones, this course takes you from foundations to practical application. Earn badges as you complete each milestone and track your progress on your learning dashboard.

Course Curriculum

1

Neural Network Basics

Layers, activation functions, and backpropagation

perceptrons and neuronsactivation functions (ReLU, sigmoid)forward propagationloss functionsbackpropagation simplifiedgradient descentCNNs vs RNNs overview
2

Deep Learning Frameworks

PyTorch and TensorFlow essentials

PyTorch vs TensorFlowtensors and operationsautograd basicsbuilding models with nn.Moduletraining loopsGPU accelerationmodel saving and loading
3

Convolutional Networks

Deep learning for images

convolution operationpooling layerscommon architectures (ResNet, VGG)transfer learningdata augmentationimage classification pipelinefeature visualization
4

Recurrent Networks

Sequential data processing

RNN architecturevanishing gradient problemLSTM cellsGRU cellssequence-to-sequencebidirectional RNNsattention mechanism basics
5

Transformers

The architecture behind modern AI

self-attention mechanismmulti-head attentionpositional encodingencoder-decoder architectureBERT overviewGPT architecturevision transformers (ViT)
6

Generative Models

Create new data with AI

autoencodersvariational autoencoders (VAE)GAN architecturediffusion models overviewimage generation pipelinetext generation basicsethical considerations
7

Training Techniques

Train models effectively

optimizers (SGD, Adam)learning rate schedulingbatch normalizationdropout regularizationearly stoppinggradient clippingmixed precision training
8

NLP with Deep Learning

Language understanding at scale

word embeddings (Word2Vec, GloVe)fine-tuning BERTtext classification with transformersnamed entity recognitionquestion answering systemsHugging Face ecosystemprompt engineering basics
9

Practical Deep Learning

End-to-end DL projects

dataset collection and cleaningexperiment tracking (Weights & Biases)hyperparameter optimizationmodel debugging strategiesreproducibility best practicesKaggle competition tipsbuilding a DL portfolio
10

Advanced Deep Learning

Cutting-edge DL topics

few-shot learningself-supervised learningknowledge distillationneural architecture searchmulti-modal modelsreinforcement learning from human feedbackscaling laws and compute

What You'll Learn in This Deep Learning Course

perceptrons and neurons
activation functions (ReLU, sigmoid)
forward propagation
loss functions
backpropagation simplified
gradient descent
CNNs vs RNNs overview
PyTorch vs TensorFlow
tensors and operations
autograd basics
building models with nn.Module
training loops
GPU acceleration
model saving and loading

Ready to master Deep Learning?

Start this deep learning short course today. Swipe through AI-powered tiles, earn badges, and build a daily learning habit — no experience needed.

Start Learning Deep Learning