Series
1
Neural networks: the basic building block
2 Forward pass and backpropagation
3 Training neural networks: a practical guide
4 Convolutional neural networks
5 Recurrent neural networks and LSTMs
6 Attention mechanism and transformers
7 Word embeddings: from one-hot to dense representations
8 Transfer learning and fine-tuning
9 Optimization techniques for deep networks
10 Regularization for deep networks
11 Encoder-decoder architectures
12 Generative models: an overview
13 Restricted Boltzmann Machines
14 Deep Belief Networks
15 Variational Autoencoders
16 Generative Adversarial Networks: training and theory
17 DCGAN, conditional GANs, and GAN variants
18 Representation learning and self-supervised learning
19 Domain adaptation and fine-tuning strategies
20 Distributed representations and latent spaces
21 AutoML and hyperparameter optimization
22 Neural architecture search
23 Network compression and efficient inference
24 Graph neural networks
25 Practical deep learning: debugging and tuning
1
What is Linux and how it differs from other OSes
2 Installing Linux and setting up your environment
3 The Linux filesystem explained
4 Users, groups, and permissions
5 Essential command line tools
6 Shell scripting fundamentals
7 Processes and job control
8 Standard I/O, pipes, and redirection
9 The Linux networking stack
10 Package management and software installation
11 Disk management and filesystems
12 Logs and system monitoring
13 SSH and remote access
14 Cron jobs and task scheduling
15 Linux security basics for sysadmins
1
Why Maths Matters for ML: A Practical Overview
2 Scalars, Vectors, and Vector Spaces
3 Matrices and Matrix Operations
4 Matrix Inverses and Systems of Linear Equations
5 Eigenvalues and Eigenvectors
6 Matrix Decompositions: LU, QR, SVD
7 Norms, Distances, and Similarity
8 Calculus Review: Derivatives and the Chain Rule
9 Partial Derivatives and Gradients
10 The Jacobian and Hessian Matrices
11 Taylor series and local approximations
12 Probability fundamentals
13 Random variables and distributions
14 Bayes theorem and its role in ML
15 Information theory: entropy, KL divergence, cross-entropy
1
What is machine learning: a map of the field
2 Data, features, and the ML pipeline
3 Linear regression
4 Bias, variance, and the tradeoff
5 Regularization: Ridge, Lasso, and ElasticNet
6 Logistic regression and classification
7 Evaluation metrics for classification
8 Naive Bayes classifier
9 K-Nearest Neighbors
10 Decision trees
11 Ensemble methods: Bagging and Random Forests
12 Boosting: AdaBoost and Gradient Boosting
13 Support Vector Machines
14 K-Means clustering
15 Dimensionality Reduction: PCA
16 Gaussian mixture models and EM algorithm
17 Model selection and cross-validation
18 Feature engineering and selection
1
What is optimization and why ML needs it
2 Convex sets and convex functions
3 Optimality conditions: first order
4 Optimality conditions: second order
5 Line search methods
6 Least squares: the closed-form solution
7 Steepest descent (gradient descent)
8 Newton's method for optimization
9 Quasi-Newton methods: BFGS and L-BFGS
10 Conjugate gradient methods
11 Constrained optimization and Lagrangian duality
12 KKT conditions
13 Penalty and barrier methods
14 Interior point methods
15 The simplex method
16 Frank-Wolfe method
17 Optimization in dynamic programming and optimal control
18 Stochastic gradient descent and variants
1
How attackers think: the attacker mindset
2 Networking fundamentals for security
3 Cryptography fundamentals
4 Public key infrastructure and certificates
5 Authentication and authorization
6 Web application security: OWASP Top 10
7 Network attacks and defenses
8 Linux privilege escalation
9 Windows security fundamentals
10 Malware types and analysis basics
11 Reconnaissance and OSINT
12 Exploitation basics and CVEs
13 Post-exploitation and persistence
14 Defensive security: hardening and monitoring
15 Incident response
16 CTF skills and practice labs
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