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deep learning

86 tutorials found

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ai machine learning supervised learning scikit-learn neural networks pytorch tensorflow expert topics ml foundations core ml deep dive advanced topics foundations model transformers

Machine Learning(50)

What is Machine Learning? — Complete Introduction

Math Foundations for Machine Learning — Linear Algebra, Calculus, Probability

Linear Regression — Complete Guide with Math and Code

Logistic Regression — Complete Guide for Classification

K-Nearest Neighbors — Complete Guide

Decision Trees — Complete Guide with Visualizations

Naive Bayes Classifier — Complete Guide with Visualizations

Support Vector Machines — Complete Guide with Visualizations

Clustering — Complete Guide with Visualizations

Model Evaluation — Complete Guide with Visualizations

Random Forest — Complete Guide for Ensemble Learning

XGBoost and Gradient Boosting — Complete Guide

Feature Engineering — Complete Guide

Dimensionality Reduction — PCA, t-SNE, UMAP Complete Guide

Model Selection and Hyperparameter Tuning Complete Guide

Regularization — Ridge, Lasso and Elastic Net Complete Guide

Ensemble Methods — Bagging, Boosting, Stacking Complete Guide

Time Series Analysis and Forecasting Complete Guide

NLP Fundamentals — Text Processing, Embeddings and Classification

Recommendation Systems — Collaborative and Content-Based Filtering

Neural Networks Fundamentals — Perceptrons to Deep Learning

Convolutional Neural Networks — Complete Guide for Vision

RNN, LSTM and GRU — Sequential Data Complete Guide

GANs — Generative Adversarial Networks Complete Guide

Transformers — Attention Is All You Need Complete Guide

BERT and Encoder Models — Complete Guide

Transfer Learning — Pre-trained Models Complete Guide

Autoencoders — Encoding, Decoding and Representation Learning

Attention Mechanisms — Deep Dive

Training Deep Networks — Optimization, Regularization and Best Practices

GPT Architecture — Decoder-Only Transformers Complete Guide

Advanced Topics

Reinforcement Learning — Complete Guide

Advanced Topics

MLOps — Machine Learning Operations Complete Guide

Advanced Topics

Model Deployment — APIs, Containers and Production ML

Advanced Topics

A/B Testing for ML — Experiment Design and Statistical Rigor

Advanced Topics

ML Ethics — Fairness, Bias, Interpretability and Responsible AI

Advanced Topics

Model Interpretability — SHAP, LIME and Explainable AI

Advanced Topics

Causal Inference — Moving Beyond Correlation

Advanced Topics

Graph Neural Networks — Learning on Graph Structures

Diffusion Models — State-of-the-Art Generative AI

Federated Learning — Privacy-Preserving ML

Meta-Learning — Learning to Learn

Self-Supervised Learning — Pre-training Revolution

AutoML — Automated Machine Learning

ML System Design — Architecture and Production Patterns

Feature Stores — Managing ML Features at Scale

ML Research Methods — Reading Papers and Reproducibility

Capstone Projects — End-to-End ML Applications

ML Interview Prep — Questions, Answers and System Design

ML Cheatsheet — Quick Reference Guide

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

data-science-premium(2)

Recommender Systems with Deep Learning

Tabular Deep Learning

fintech-ai(2)

LSTM & Transformer Stock Price Prediction

Neural Black-Scholes Options Pricing

Generative AI(2)

Transformer Architecture

Attention Mechanism

Computer Vision(1)

Visual Question Answering

Computer Vision

Data Science(1)

Project 3: Deploy a Deep Learning Model

Module 16: Deployment Project

Drone AI(1)

Deep Learning Basics for Drones

Math(1)

Applications in Deep Learning

NLP(1)

Deep Learning for Sentiment Analysis