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Course Outline
- Overview of neural networks and deep learning
- The concept of Machine Learning (ML)
- Why we need neural networks and deep learning?
- Selecting networks to different problems and data types
- Learning and validating neural networks
- Comparing logistic regression to neural network
- Neural network
- Biological inspirations to Neural network
- Neural Networks– Neuron, Perceptron and MLP(Multilayer Perceptron model)
- Learning MLP – backpropagation algorithm
- Activation functions – linear, sigmoid, Tanh, Softmax
- Loss functions appropriate to forecasting and classification
- Parameters – learning rate, regularization, momentum
- Building Neural Networks in Python
- Evaluating performance of neural networks in Python
- Basics of Deep Networks
- What is deep learning?
- Architecture of Deep Networks– Parameters, Layers, Activation Functions, Loss functions, Solvers
- Restricted Boltzman Machines (RBMs)
- Autoencoders
- Deep Networks Architectures
- Deep Belief Networks(DBN) – architecture, application
- Autoencoders
- Restricted Boltzmann Machines
- Convolutional Neural Network
- Recursive Neural Network
- Recurrent Neural Network
- Overview of libraries and interfaces available in Python
- Caffee
- Theano
- Tensorflow
- Keras
- Mxnet
- Choosing appropriate library to problem
- Building deep networks in Python
- Choosing appropriate architecture to given problem
- Hybrid deep networks
- Learning network – appropriate library, architecture definition
- Tuning network – initialization, activation functions, loss functions, optimization method
- Avoiding overfitting – detecting overfitting problems in deep networks, regularization
- Evaluating deep networks
- Case studies in Python
- Image recognition – CNN
- Detecting anomalies with Autoencoders
- Forecasting time series with RNN
- Dimensionality reduction with Autoencoder
- Classification with RBM
Requirements
Knowledge/appreciation of machine learning, systems architecutre and programming languages are desirable
Testimonials
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
Jonathan Blease
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
Gudrun Bickelq
The interactive part, tailored to our specific needs.
Thomas Stocker
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
I generally enjoyed the knowledgeable trainer.
Sridhar Voorakkara
I was amazed at the standard of this class - I would say that it was university standard.
David Relihan
Very good all round overview. Good background into why Tensorflow operates as it does.
Kieran Conboy
I liked the opportunities to ask questions and get more in depth explanations of the theory.
Sharon Ruane
The trainer very easily explained difficult and advanced topics.
Leszek K
I liked the new insights in deep machine learning.
Josip Arneric
We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.
Tea Poklepovic
I mostly enjoyed the graphs in R :))).
Faculty of Economics and Business Zagreb
Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for.
Commerzbank AG
I was benefit from topic selection. Style of training. Practice orientation.
Commerzbank AG
Communication with lecturers
文欣 张
like it all
lisa xie
a lot of exercises that I can directly use in my work.
Alior Bank S.A.
Examples on real data.
Alior Bank S.A.
neuralnet, pROC in a loop.
Alior Bank S.A.
A wide range of topics covered and substantial knowledge of the leaders.
- ING Bank Śląski S.A.; Kamil Kurek Programowanie
Lack
- ING Bank Śląski S.A.; Kamil Kurek Programowanie
Big theoretical and practical knowledge of the lecturers. Communicativeness of trainers. During the course, you could ask questions and get satisfactory answers.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Practical part, where we implemented algorithms. This allowed for a better understanding of the topic.
- ING Bank Śląski S.A.; Kamil Kurek Programowanie
exercises and examples implemented on them
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Examples and issues discussed.
- ING Bank Śląski S.A.; Kamil Kurek Programowanie
Substantive knowledge, commitment, a passionate way of transferring knowledge. Practical examples after a theoretical lecture.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Practical exercises prepared by Mr. Maciej
- ING Bank Śląski S.A.; Kamil Kurek Programowanie
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
- Explore
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
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