"DEEP MATHEMATICS" COURSE
starts June 15 / 10 weeks / price - 34.900 rubles
starts June 15 / 10 weeks / price - 34.900 rubles
By clicking the button, I consent to the processing of personal data and agree to the privacy policy and the offer agreement.
Course objectives
The main objective of the course is to understand how modern neural networks work at the algorithmic level.
- 1Get acquainted with the mathematical basis for training modern neural networks
- 2Get to know the formal models of neurons-layers-networks
- 3Understand the principles of training modern neural networks
What are you going to learn
- Understand the operation of the network at the lowest level of information processing
- Select the parameters of the neural network model taking into account the peculiarities of the task
Training format
- Recorded sessionsYou will have webinar records available for viewing at any time
- Closed training platformWithin 24 hours of payment, you will have access to the GetCourse closed training platform with class notes and course materials.
- Mentor's assistanceYou will have a chat room where you can ask your mentor any questions you may have
Training Program
You can see a brief training program below
Click on the class title to see its detailed description.
Click on the class title to see its detailed description.
- Physiological model of neuron
- McCullock-Pitts neuron implementation: convolution+activation function
- Calculating the output of a linear neuron
- Delta-law training of a linear neuron on the example of linearly separated space examples
- Implementation of a neural network layer
- Implementation of a multilayer network
- Combination of solutions for linear neurons
- Regression goal
- Definition of the regression model
- Error function
- The method of least squares
- Search for regression parameters based on the gradient descent
- Stochastic gradient descent
- Learning the linear neural network and its limitations
- Nonlinear neuron
- The problems of training a nonlinear neuron
- Why do we need the backpropagation method
- Implementation of the backpropagation method
- Nonlinear MLP Training
- Problems of training nonlinear MLP : overfitting, underfitting, high computational complexity, gradient attenuation
- Method of stochastic gradient descent
- Loss function (types of loss function and their implementation)
- Regularization L1, L2
- Implementation of Adam method
- Implementation of NAdam method
- Methods of the 2nd order
- Model of linear separation of objects in the feature space
- Classifier error function
- Linear classifier
- Support vector machines
- Logistic regression
- KNN
- Classifier based on non-linear MLP
- Dispersion-displacement dilemma
- Solver ensembles
- Evaluation of classifiers - metrics (accuracy, Precition, Recall, F1, ROC)
- Why do you need reduction
- Evaluation of correlation of features with each other and with the target property
- Selection of features based on correlation, based on model analysis
- Feature space transformation : PCA
- Features : T-SNE
- What happens in a neural network: hidden layers as a feature detector - network visualization.
- Tensor - definition and meaning
- Why a convolutional network works: architecture as processing of tensors and composition of convolutional networks.
- Convolutional layers
- Implementation of the convolutional layer and visualization of its work
- Convolutional layer training
- Pooling layers - visualization
- Backpropagation through the pooling layer
- Normalization layers - implementation and visualization
- Reverse layers - how it works
- Why is it possible to transfer training
- Degradation of deep networks
- Residual models: implementation and training
- Basic principles of building a convolutional network: examples and implementation
- The model of recurrent connections in the neural network and the impact on the result of the work
- Hopfield network - implementation and operation
- Recurrent networktTraining: Backpropagation Through Time (BPTT).
- Long short-term memory (LSTM) networks.
- Gated Recurrent Unit (GRU) recurrent network
- Training without a teacher
- Implementation of training without a teacher (kmeans)
- Network - self organizing map
- Training as sistribution restoration (EM algorithm can also be a little bit)
- RBM - architecture and training
- Visualization of probabilistic models
- Problems of probabilistic models
Apply for course
Course teacher
Maria Korlyakova
Education:
Bauman Moscow State Technical University + MPEI postgraduate course. Candidate of Technical Sciences, Associate Professor
Over 30 publications in the last 5 years (Scopus, RINC, EAC, conferences)
Area of scientific interest:
Neural networks for image processing (traction/segmentation/detectors), task complexity analysis and evaluation of neural network parameters (Machine Learning, Deep Learning, Computer vision, Object).
Main technological modules:
Python (scikit-learn, Keras), OpenCV, Matlab (Neuro NetWork Toolbox, Computer Vision Toolbox, Statistic and ML Toolbox).
I work in projects of vision systems (tracking/segmentation/detectors) on neural networks, time series neural network analysis.
I work (BMSTU) as the head of graduation works (masters, bachelors) and research students in the direction of Deep learning, Computer Vision. My students participate in programs of Kaggle platform, Sberbank, Polytechnics, etc.
Bauman Moscow State Technical University + MPEI postgraduate course. Candidate of Technical Sciences, Associate Professor
Over 30 publications in the last 5 years (Scopus, RINC, EAC, conferences)
Area of scientific interest:
Neural networks for image processing (traction/segmentation/detectors), task complexity analysis and evaluation of neural network parameters (Machine Learning, Deep Learning, Computer vision, Object).
Main technological modules:
Python (scikit-learn, Keras), OpenCV, Matlab (Neuro NetWork Toolbox, Computer Vision Toolbox, Statistic and ML Toolbox).
I work in projects of vision systems (tracking/segmentation/detectors) on neural networks, time series neural network analysis.
I work (BMSTU) as the head of graduation works (masters, bachelors) and research students in the direction of Deep learning, Computer Vision. My students participate in programs of Kaggle platform, Sberbank, Polytechnics, etc.
Certificate
We issue a certificate of course completion
Photos from our
face-to-face classes
face-to-face classes
Our manager will answer any questions about the course
- Artem Voronov-GashevCustomer service managerEmail: info@neural-university.ru
Phone: +7 (499) 648-67-44
Whatsapp/viber/telegram:
+7(918) 916-41-84
"DEEP MATHEMATICS" COURSE
Starts June 15 / 10 weeks / price - 34.900 rubles
Book minimum price
Book minimum price
By clicking the button, I consent to the processing of personal data and agree to the privacy policy and the offer agreement.
"DEEP MATHEMATICS" COURSE
Starts June 15 / 10 weeks / price - 34.900 rubles
By clicking the button, I consent to the processing of personal data and agree to the privacy policy and the offer agreement.