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tensorflow

25 tutorials found

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deep learning neural networks pytorch foundations deep dive architecture sequence models computer vision generative models production optimization training transformers introduction mathematics

Deep Learning(25)

What Is Deep Learning — Foundations and The Deep Learning Revolution

Math Foundations for Deep Learning — Linear Algebra, Calculus and Probability

Backpropagation Algorithm — Forward Pass, Backward Pass and Computational Graphs

Activation Functions — Sigmoid, ReLU, GELU and The Dead Neuron Problem

Loss Functions for Deep Learning — MSE, Cross-Entropy, Focal Loss and Beyond

Optimizers for Deep Learning — SGD, Adam, AdamW and Learning Rate Schedules

Weight Initialization — Xavier, He, LSUV and Variance Preservation

Regularization for Deep Learning — Dropout, BatchNorm, Data Augmentation and Weight Decay

CNN Architecture Deep Dive — LeNet to ResNet to EfficientNet

Computer Vision

Object Detection — YOLO, Faster R-CNN, Anchor Boxes and mAP

Computer Vision

Semantic Segmentation — FCN, U-Net, DeepLab and Medical Imaging

Computer Vision

RNN Deep Dive — Vanilla RNN, BPTT and Vanishing/Exploding Gradients

Sequence Models

LSTM Networks — Gates, Cell State and Bidirectional Architectures

Sequence Models

GRU Networks — Gated Recurrent Units Deep Dive

Sequence Models

Sequence-to-Sequence Models — Encoder-Decoder Architecture

Sequence Models

Attention Mechanisms — Deep Dive

Vision Transformers — ViT and Beyond

GANs Deep Dive — Generative Adversarial Networks

Generative Models

Variational Autoencoders — Deep Dive

Generative Models

Diffusion Models Deep Dive — DDPM and Beyond

Generative Models

Graph Neural Networks — Deep Dive

Specialized Architectures

Model Compression — Pruning, Quantization, Distillation

Neural Architecture Search — Automated ML

Self-Supervised Learning — Contrastive and Masked Methods

Advanced Topics

Deep Learning Systems Design — Distributed Training and Production