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Since September 2023
Instructor since September 2023
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Learn to count with the Abacus, Soroban, Japanese abacus method - Mental Calculation
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From 94.98 C$ /h
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The Abacus method is divided into two steps:
+ We first learn to count using the Abacus (abacus). We become familiar with the balls and the different operations they allow us to carry out. Through sessions with the abacus, mental arithmetic becomes faster and faster. It turns out to be a game and no longer a chore.
+ When calculation with the abacus is mastered, you can carry out operations without the abacus, only with your fingers and your head, in just a few seconds! This stage involves mental visualization and motor representation of numbers. The gestures necessary for handling the abacus actually help to better remember the numbers.

In addition to quick and easy learning of mental arithmetic, the Abacus method has other advantages:
• Photographic and auditory memory
• Concentration and attention
• Imagination and creativity
• Logical and analytical thinking
• Multitasking capabilities
• Self-confidence and self-discipline
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At teacher's location :
  • Mission Admission : préparation aux entretiens des concours d’entrée en écoles, Rue Jean-Baptiste Pigalle, Paris, France
Age
Children (7-12 years old)
Teenagers (13-17 years old)
Adults (18-64 years old)
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Duration
60 minutes
The class is taught in
French
English
Russian
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(GMT -05:00)
New York
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Dave
Hello,
I am an experienced machine learning teacher with 5 years of expertise in teaching this discipline at all levels. My expertise using Python and R allows me to teach different machine learning algorithms such as neural networks, decision trees and clustering algorithms. I am also experienced in using popular Python and R libraries such as TensorFlow, Keras, Scikit-learn and ggplot2.
In addition to my machine learning skills, I am able to help students read and understand research papers for their presentations, as well as work on projects in Python and R. My commitment to machine learning is passionate and I enjoy sharing my knowledge with my students.
If you are interested in my services as a machine learning teacher for all levels, do not hesitate to contact me.
In addition to my machine learning skills, I am also able to help you with mathematics, statistics and dissertation writing.
I am available to teach the following subjects:
1.Python or R
2. Data exploration
3.Machine learning
3.1. Intro ML
3.2. Linear Model
-> Linear Models for Regression and Classification
3.3. kernel
-> Kernelization
3.4. Model selection
3.5. model set,
-> Bagging / RandomForest, Boosting (XGBoost, LightGBM,...) , Stacking
3.6. Data preprocessing
-> Data pre-processing
-> Pipelines: choose the right preprocessing steps and models in your pipeline
-> Cross validation
3.7. Neural Networks
-> Neural architectures
-> Training neural nets: Forward pass: Tensor operations and Backward pass: Backpropagation
-> Neural network design: Activation functions, weight initialization and Optimizers
-> Neural networks in practice: Model selection, Early stopping, Memorization capacity and information bottleneck, L1/L2 regularization, Dropout, Batch normalization
3.8. Convolutional Neural Networks
-> Convolved Image
-> Convolutional neural networks
->Data increase
-> Model interpretation
-> Using pre-trained networks (transfer learning)
3.9. Neural Networks for text
-> Bag of word representations, Word embeddings, Word2Vec, FastText, GloVe
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arrow icon previousarrow icon next
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Dave
Hello,
I am an experienced machine learning teacher with 5 years of expertise in teaching this discipline at all levels. My expertise using Python and R allows me to teach different machine learning algorithms such as neural networks, decision trees and clustering algorithms. I am also experienced in using popular Python and R libraries such as TensorFlow, Keras, Scikit-learn and ggplot2.
In addition to my machine learning skills, I am able to help students read and understand research papers for their presentations, as well as work on projects in Python and R. My commitment to machine learning is passionate and I enjoy sharing my knowledge with my students.
If you are interested in my services as a machine learning teacher for all levels, do not hesitate to contact me.
In addition to my machine learning skills, I am also able to help you with mathematics, statistics and dissertation writing.
I am available to teach the following subjects:
1.Python or R
2. Data exploration
3.Machine learning
3.1. Intro ML
3.2. Linear Model
-> Linear Models for Regression and Classification
3.3. kernel
-> Kernelization
3.4. Model selection
3.5. model set,
-> Bagging / RandomForest, Boosting (XGBoost, LightGBM,...) , Stacking
3.6. Data preprocessing
-> Data pre-processing
-> Pipelines: choose the right preprocessing steps and models in your pipeline
-> Cross validation
3.7. Neural Networks
-> Neural architectures
-> Training neural nets: Forward pass: Tensor operations and Backward pass: Backpropagation
-> Neural network design: Activation functions, weight initialization and Optimizers
-> Neural networks in practice: Model selection, Early stopping, Memorization capacity and information bottleneck, L1/L2 regularization, Dropout, Batch normalization
3.8. Convolutional Neural Networks
-> Convolved Image
-> Convolutional neural networks
->Data increase
-> Model interpretation
-> Using pre-trained networks (transfer learning)
3.9. Neural Networks for text
-> Bag of word representations, Word embeddings, Word2Vec, FastText, GloVe
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