ml gate crash

Learning Neural Networks for Computer Vision

1. Foundational Concepts (1-2 weeks)

  • Linear Algebra and Calculus Basics
    • Focus on vectors, matrices, and operations.
    • Key concepts: dot product, matrix multiplication, gradients.
    • Resources:
      • Khan Academy videos
      • 3Blue1Brown’s Essence of Linear Algebra series
  • Basic Probability and Statistics
    • Understand basic probability distributions and concepts like mean, variance, and standard deviation.
    • Resources:
      • StatQuest with Josh Starmer on YouTube

2. Introduction to Neural Networks (2-3 weeks)

  • Basic Neural Network Architecture
    • Understand neurons, activation functions, layers, and how they form a network.
    • Key Concepts: Feedforward networks, loss functions, backpropagation.
    • Resources:
      • Andrew Ng's Machine Learning course (Coursera)
      • Deep Learning Specialization
  • Common Frameworks
    • Familiarize with PyTorch and/or TensorFlow basics.
    • Learn about model training, evaluation, and making predictions.
    • Resources:
      • Official documentation
      • Beginner tutorials

3. Deep Learning for Computer Vision (3-4 weeks)

  • Convolutional Neural Networks (CNNs)
    • Understand how CNNs work and their structure (convolutional layers, pooling layers).
    • Key Applications: Image classification, object detection.
    • Resources:
      • Fast.ai’s Practical Deep Learning for Coders course
  • Transfer Learning
    • Learn how to use pre-trained models for your own tasks.
    • Understand fine-tuning and feature extraction.
    • Resources:
      • PyTorch tutorials on transfer learning

4. Common Techniques and Architectures (3 weeks)

  • Object Detection and Segmentation
    • Familiarize with architectures like YOLO, SSD, and Mask R-CNN.
    • Key Concepts: Intersection over Union (IoU), non-maximum suppression.
    • Resources:
      • Papers with Code for state-of-the-art methods
  • Data Augmentation and Preprocessing
    • Learn techniques to improve model robustness and performance.
    • Key Concepts: Resizing, normalization, random transformations.
    • Resources:
      • Keras or PyTorch documentation on data augmentation

5. Practical Applications and Projects (4 weeks)

  • Build Simple Projects
    • Start with image classification (e.g., CIFAR-10 dataset).
    • Progress to more complex tasks like object detection with Detectron2.
    • Resources:
      • Kaggle competitions and datasets
  • Experiment and Iterate
    • Tweak model parameters and observe effects.
    • Use techniques like hyperparameter tuning and cross-validation.

6. Advanced Topics and Further Learning (Ongoing)

  • Generative Adversarial Networks (GANs)
    • High-level understanding of GANs and their applications.
    • Resources:
      • Ian Goodfellow's original paper and related tutorials
  • Stay Updated
    • Follow research papers, blogs, and forums (e.g., arXiv, Towards Data Science).
    • Join communities (e.g., Reddit, Discord) to engage with others.

7. Review and Reflect (Ongoing)

  • Weekly Reviews
    • Dedicate time each week to review what you’ve learned and reflect on projects.
    • Create summaries or mind maps to reinforce concepts.
  • Documentation and Sharing
    • Document your projects and findings on GitHub or a personal blog to reinforce learning and contribute to the community.

ML and DL in a fast lane

Day 1-2: Supervised Learning

  • Linear Regression
    • Overview of regression models: relationship between independent and dependent variables.
    • Focus on loss function (MSE) and assumptions.
    • Important: Understand bias-variance tradeoff and overfitting.
  • Logistic Regression
    • Classification problems, sigmoid function, cost function (cross-entropy).
    • Important: Decision boundary and interpretation of coefficients.
  • Support Vector Machines (SVM)
    • Hyperplanes and margin.
    • Linear and non-linear SVM using kernels.
    • Important: How SVM maximizes margin for better generalization.
  • Decision Trees
    • Tree-like structure, decision nodes, and leaf nodes.
    • Gini impurity vs. entropy (information gain).
    • Important: Overfitting and pruning.
  • Random Forests
    • Ensemble method: decision trees combined for robustness.
    • Bagging and random feature selection.
    • Important: Random Forest reduces overfitting of individual decision trees.
  • Ensemble Methods
    • Bagging (Bootstrap Aggregating): Combining models to reduce variance.
    • Boosting: Sequentially focusing on misclassified data points.
    • Important: Key algorithms: AdaBoost, Gradient Boosting, XGBoost.

Day 3-4: Unsupervised Learning

  • K-means Clustering
    • Centroids, assignment, and update steps.
    • Distance metric (Euclidean).
    • Important: How to select the number of clusters (K).
  • Hierarchical Clustering
    • Agglomerative vs. Divisive methods.
    • Dendrogram and cutting the tree at different levels.
  • Gaussian Mixture Models (GMM)
    • Mixture of Gaussian distributions, Expectation-Maximization (EM) algorithm.
    • Important: Flexibility over K-means, probabilistic model.
  • Principal Component Analysis (PCA)
    • Dimensionality reduction via eigenvalues and eigenvectors.
    • Focus on variance maximization and visualization.
    • Important: Use for reducing features in high-dimensional data.

Day 5-6: Deep Learning

  • Neural Networks
    • Neurons, activation functions, forward and backward propagation.
    • Important: Gradient descent and backpropagation for optimization.
  • Convolutional Neural Networks (CNNs)
    • Image processing: convolution, pooling, fully connected layers.
    • Important: Role of CNN layers in feature extraction and classification.
  • Recurrent Neural Networks (RNNs)
    • Sequential data, hidden states.
    • Important: Backpropagation through time (BPTT) and vanishing gradient problem.
  • TensorFlow/PyTorch Frameworks
    • Install and learn basics of one framework (e.g., TensorFlow).
    • Build simple models (e.g., neural networks).
  • Regularization Techniques
    • Dropout: Randomly drop neurons during training to prevent overfitting.
    • L2 Regularization: Penalize large weights to avoid overfitting.

Day 7: Evaluation Metrics & Advanced Topics

  • Confusion Matrix, Precision, Recall, F1-score, AUC-ROC
    • Key metrics for classification problems.
    • Focus on understanding tradeoffs between precision and recall.
    • Important: AUC-ROC as a measure of classifier performance.
  • Cross-validation and Hyperparameter Tuning
    • Important: Cross-validation to assess model generalization.
    • Grid search and random search for hyperparameter optimization.
  • Bayesian Methods
    • Basic understanding of Bayes' Theorem, priors, posteriors.
    • Important: How Bayesian methods relate to probabilistic inference.
  • Markov Chains
    • States, transitions, and stationary distributions.
  • Reinforcement Learning
    • Agents, environments, actions, rewards.
    • Important: Key concepts: Q-learning, exploration vs. exploitation.
  • Probabilistic Graphical Models (PGMs)
    • Graphical representation of probability distributions.
    • Focus on Bayesian Networks and Markov Networks.

Study Tips:

  • Key Focus Areas:
    • Supervised Learning (SVMs, Random Forests).
    • Deep Learning (NN, CNN, RNN).
    • Evaluation Metrics (Precision/Recall, AUC-ROC).
  • Practice:
    • Try coding exercises after each section using tools like Scikit-learn, TensorFlow, or PyTorch.
  • Time Management:
    • Aim for 1-2 hours per topic, with hands-on examples for Deep Learning and Evaluation Metrics.
  • Revisit Key Concepts:
    • Focus on overfitting, regularization, ensemble methods, and hyperparameter tuning.