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Discover the Best Private Computer programming Classes in Temara

For over a decade, our private Computer programming tutors have been helping learners improve and fulfil their ambitions. With one-on-one lessons at home or in Temara, you’ll benefit from high-quality, personalised teaching that’s tailored to your goals, availability, and learning style.

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10 computer programming teachers in Temara

Math · Computer programming
Science · Computer programming · Math
Math · Computer programming
Trusted teacher: Any programming language - compiled or interpreted, statically typed or dynamic, boils down to the same common principles. These principles can then be applied to make any programming language - with any rules you desire. You can use '&' to represent addition and '>>!' to represent conditional loops. You can use 'beep' to represent branches and end each line with @. This class aims to teach you how to create these principles to design a language, and then the different approaches on how to do it. An end project will be to actually make your own programming language! The topics covered are listed below: Pretence: Programming Language Types (grammar), Programming Language Types (formation), Programming Language Types (typing) Stages (Lexing), Stages (Parsing), Stages (Interpreting) Intermediate Forms (AST), Intermediate Forms (Bytecode), Transpiling Optimisation (types), Optimisation (application to AST), Optimisation (application to Bytecode) Building Ray (Classes): Grammar Type, Formation Type, Typing Type Data Types, Expressions (types), Expressions (precedence) Statements (declarations), Statements (statements), Standard Library Tokens, Tokenisation (characters), Tokenisation (literals) Parsing (AST), Parsing (Visitors), Interpreting (Visitor) Static Scope, Semantic Analysis, Resolvers (Visitor) Building Ray (Nodes) Parsing (Literals), Interpreting (Literals), Resolving (Literals) Parsing (Groups), Interpreting (Groups), Resolving (Groups) Parsing (Operators), Interpreting (Operators), Resolving (Operators) Parsing (Variable declarations), Interpreting (Variable declarations), Resolving (Variable declarations) Parsing (Variable getters), Interpreting (Variable getters), Resolving (Variable getters) Parsing (Variable setters), Interpreting (Variable setters), Resolving (Variable setters) Parsing (Branches), Interpreting (Branches), Resolving (Branches) Parsing (Logic Gates), Interpreting (Logic Gates), Resolving (Logic Gates) Parsing (Loops conditional), Interpreting (Loops conditional), Resolving (Loops conditional) Parsing (Loops count), Interpreting (Loops count), Resolving (Loops count) Parsing (Function declarations), Interpreting (Function declarations), Resolving (Function declarations) Parsing (Function calls), Interpreting (Function calls), Resolving (Function calls) Parsing (Scope Termination), Interpreting (Scope Termination), Resolving (Scope Termination) Parsing (Class declarations), Interpreting (Class declarations), Resolving (Class declarations) Parsing (Instances), Interpreting (Instances), Resolving (Instances) Parsing (Methods), Interpreting (Methods), Resolving (Methods) Parsing (Instance Lookup), Interpreting (Instance Lookup), Resolving (Instance Lookup) Parsing (Constructors), Interpreting (Constructors), Resolving (Constructors) Parsing (Inheritance), Interpreting (Inheritance), Resolving (Inheritance) Optimisations (Visitor), Optimisations (Literal-Only Expressions), Optimisations (Variable Expressions) Project: Build your own language! (Supervised)
Computer science · Computer programming
Trusted teacher: Master in Computer Science from the State University of Campinas (Brazil) and university professor in Peru. He has participated in the most important Artificial Intelligence conferences including ACL, NeurIPS, ICML, ICLR, KDD, ICCV and CVPR, summer schools such as Machine Learning (MLSS), Deep Learning (DLRL) and Probabilistic ML (ProbAI). He has also participated in various programming contests and has experience preparing interviews for applications to companies such as Google, Meta, Microsoft, among others. He has extensive experience in the areas of Machine Learning and Deep Learning applied mainly to computer vision and natural language processing. He has experience in teaching, providing illustrative explanations for a better understanding of both the theoretical and practical parts. Some examples of presentations given: He has also advised students from different countries in their graduation and master's theses, providing them with a theoretical and practical base with examples that they can then use to continue their development. Some of the things I can help you with: - Machine Learning: Linear regression, logistic regression, regularization, LDA, QDA, SVMs, decision trees, random forest, boosting, PCA, clustering (K-means, DBSCAN, hierarchical, GMM), neural networks, model selection, metrics evaluation, MLE, Bayesian learning, data preprocessing, etc. - Deep Learning: Multilayer Perceptron (MLP), backpropagation, activation functions, multiclass classification, optimizers (SGD, Adam, RMSProp, etc.), CNNs, architectures (ResNet, DenseNet, EfficientNet, Siamese, etc.), RNNs, LSTMs, Seq2seq, Attention, Transformers (BERT, GPT, ViT, etc.), autoencoders, generative models (VAE, GAN, Diffusion, etc.), etc. - Languages: Python, C++ - Libraries and frameworks: PyTorch, Tensorflow, Keras, Huggingface, numpy, pandas, scikit-learn, sympy, etc.
Computer programming · Python
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