What is Deep Learning?
Deep Learning is a subset of Machine Learning that uses artificial neural networks with multiple layers (hence "deep") to progressively extract higher-level features from raw data. Unlike classical ML, deep learning can automatically discover the representations needed for detection or classification from raw input — without hand-crafted features.
Powered by CNNs for vision, RNNs / LSTMs for sequences, Transformers for language and multimodal tasks, and Diffusion Models for generation — deep learning is the backbone of GPT, DALL·E, Stable Diffusion, AlphaFold, and the largest AI systems in production today.
Explore Deep Learning
Nine curated sections covering the full spectrum of the Deep Learning landscape
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01
Deep Learning Frameworks
The core software stacks powering deep learning: TensorFlow, PyTorch, Keras, JAX, MXNet, Caffe2, PaddlePaddle, and more — covering ecosystem, model-building APIs, automatic differentiation, hardware acceleration, and deployment pipelines.
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02
Deep Learning Resources
Curated textbooks, MOOCs, research papers, YouTube lectures, and hands-on tutorials — from Andrew Ng's Deep Learning Specialization to the Deep Learning book by Goodfellow, Bengio & Courville and beyond.
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03
Deep Learning Algorithms
The algorithmic foundations of deep learning: CNNs, RNNs, LSTMs, GRUs, Autoencoders, VAEs, GANs, Transformers, Attention Mechanisms, Diffusion Models, and Neural Architecture Search — explained with use cases and implementations.
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04
Deep Learning Applications
Real-world deployments of deep learning across industries: autonomous driving, medical imaging, speech recognition, recommendation systems, drug discovery, fraud detection, robotics, and generative AI for text, image, and code.
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05
Data Sets for Machine & Deep Learning
Benchmark and production-ready datasets for training and evaluating models: ImageNet, COCO, MS CELEB, LibriSpeech, SQuAD, CIFAR, Open Images, Hugging Face Datasets, and domain-specific collections for NLP, vision, and tabular tasks.
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06
Libraries for Deep Learning Graphs
Graph-aware deep learning libraries: PyTorch Geometric, DGL (Deep Graph Library), Spektral, StellarGraph, and others enabling GNNs, knowledge graph embeddings, and graph-based reasoning over relational and structured data.
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07
SGD Optimisation Algorithms
The gradient-based optimisers that train deep learning models: SGD with Momentum, Adam, AdaGrad, RMSProp, AdamW, Lion, Adadelta, and learning-rate schedulers — covering theory, convergence properties, and practical tuning guidance.
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08
Tools Based on OpenAI GPT-3
Products and developer tools built on the GPT-3 language model: writing assistants, code generators, summarizers, search tools, content creators, chatbots, and no-code interfaces — the ecosystem that preceded GPT-4 and ChatGPT.
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09
Graph Neural Network (GNN) Frameworks
Frameworks for learning on graph-structured data: PyTorch Geometric, DGL, GraphSAGE, Graph Attention Networks (GAT), Graph Convolutional Networks (GCN), and knowledge graph completion tools for social networks, drug interaction prediction, and recommendation engines.
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