Deep Learning Short Course

Dive into deep learning, the breakthrough technology powering ChatGPT, image recognition, voice assistants, and autonomous vehicles. Deep learning uses multi-layered neural networks to learn complex patterns from massive datasets. This course covers perceptrons, activation functions, forward and backward propagation, loss functions, gradient descent, and the differences between CNNs and RNNs.. 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

Neural Network Basics

Layers, activation functions, and backpropagation

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

Deep Learning Frameworks

PyTorch and TensorFlow essentials

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

Convolutional Networks

Deep learning for images

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

Recurrent Networks

Sequential data processing

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

Transformers

The architecture behind modern AI

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

Generative Models

Create new data with AI

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

Training Techniques

Train models effectively

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

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

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

Advanced Deep Learning

Cutting-edge DL topics

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