Deep Learning Roadmap

Handling Imbalanced Data

• Resampling Techniques: Oversampling, Undersampling

• Synthetic Data Generation: SMOTE

• Cost-Sensitive Learning

Core Deep Learning Concepts

• Neural Networks:

  • Perceptron

  • Feedforward Neural Networks

  • Backpropagation and Gradient Descent

• Deep Learning Frameworks:

  • TensorFlow

  • PyTorch

• Training Deep Neural Networks:

  • Loss Functions

  • Optimizers (SGD, Adam, etc.)

  • Regularization Techniques (Dropout, Batch Normalization)

• Regularization and Optimization

  • L1 and L2 Regularization

  • Dropout

  • Hyperparameter Tuning

• Handling Outliers and Noise

  • Robust Regression Methods

  • Preprocessing and Data Cleaning Techniques

Classification

• Binary Classification:

  • Logistic Regression as a Baseline

  • Binary Cross-Entropy Loss

  • Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC

• Multi-Class Classification:

  • Softmax Activation

  • Categorical Cross-Entropy Loss

  • One-vs-All and One-vs-One Strategies

  • Evaluation Metrics: Confusion Matrix, Accuracy, Top-K Accuracy

• Advanced Classification Techniques:

  • Deep Neural Networks for Classification

  • Convolutional Neural Networks (CNNs) (for non-CV applications like signal processing)

  • Recurrent Neural Networks (RNNs) and LSTMs (for sequence classification)

Regression

• Linear Regression:

  • Ordinary Least Squares (OLS)

  • Gradient Descent for Linear Regression

  • Evaluation Metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), R-squared

• Non-Linear Regression:

  • Polynomial Regression

  • Basis Functions and Feature Engineering

• Deep Learning for Regression:

  • Feedforward Neural Networks for Regression

  • Loss Functions: Mean Squared Error (MSE), Huber Loss

• Regularization Techniques:

  • Ridge Regression (L2 Regularization)

  • Lasso Regression (L1 Regularization)

  • Elastic Net

• Advanced Regression Models:

  • Decision Trees and Random Forests

  • Gradient Boosting Machines (GBM), XGBoost, and LightGBM

  • Support Vector Regression (SVR)
    

Time Series Analysis

• Traditional Methods:

  • ARIMA Models

  • Exponential Smoothing

• Deep Learning for Time Series:

  • Recurrent Neural Networks (RNNs)

  • Long Short-Term Memory (LSTM) Networks

Generative Models

• Autoencoders:

  • Autoencoders

  • Variational Autoencoders (VAEs)

• Generative Adversarial Networks (GANs):

  • GANs

  • Conditional GANs

  • DCGANs

• Reinforcement Learning
  

  • Markov Decision Processes (MDPs)

  • Policy and Value Functions