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Course Outline
Getting Started
- Quickstart: Running Examples and DL4J in Your Projects
- Comprehensive Setup Guide
Introduction to Neural Networks
- Restricted Boltzmann Machines
- Convolutional Nets (ConvNets)
- Long Short-Term Memory Units (LSTMs)
- Denoising Autoencoders
- Recurrent Nets and LSTMs
Multilayer Neural Nets
- Deep-Belief Network
- Deep AutoEncoder
- Stacked Denoising Autoencoders
Tutorials
- Using Recurrent Nets in DL4J
- MNIST DBN Tutorial
- Iris Flower Tutorial
- Canova: Vectorization Lib for ML Tools
- Neural Net Updaters: SGD, Adam, Adagrad, Adadelta, RMSProp
Datasets
- Datasets and Machine Learning
- Custom Datasets
- CSV Data Uploads
Scaleout
- Iterative Reduce Defined
- Multiprocessor / Clustering
- Running Worker Nodes
Text
- DL4J's NLP Framework
- Word2vec for Java and Scala
- Textual Analysis and DL
- Bag of Words
- Sentence and Document Segmentation
- Tokenization
- Vocab Cache
Advanced DL2J
- Build Locally From Master
- Contribute to DL4J (Developer Guide)
- Choose a Neural Net
- Use the Maven Build Tool
- Vectorize Data With Canova
- Build a Data Pipeline
- Run Benchmarks
- Configure DL4J in Ivy, Gradle, SBT etc
- Find a DL4J Class or Method
- Save and Load Models
- Interpret Neural Net Output
- Visualize Data with t-SNE
- Swap CPUs for GPUs
- Customize an Image Pipeline
- Perform Regression With Neural Nets
- Troubleshoot Training & Select Network Hyperparameters
- Visualize, Monitor and Debug Network Learning
- Speed Up Spark With Native Binaries
- Build a Recommendation Engine With DL4J
- Use Recurrent Networks in DL4J
- Build Complex Network Architectures with Computation Graph
- Train Networks using Early Stopping
- Download Snapshots With Maven
- Customize a Loss Function
Requirements
Knowledge in the following:
- Java
Testimonials
lots of information, all questions ansered, interesting examples
A1 Telekom Austria AG
Deep Learning for Telecom (with Python) Course
The exercises were very good and interactive. Instructors were always answering all questions and providing their insight on all topics
Felix Navarro, Motorola Solutions
Deep Learning for Telecom (with Python) Course
The Colab Notebooks with the training and examples notes.
Felix Navarro, Motorola Solutions
Deep Learning for Telecom (with Python) Course
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
- Explore
Deep Reinforcement Learning with Python Course
Abhi always made sure we were following along. Good mix of practice and theory.
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Deep Reinforcement Learning with Python Course
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Python for Advanced Machine Learning Course
Having access to the notebooks to work through
Premier Partnership
Python for Advanced Machine Learning Course
The trainers knowledge of the topics he was teaching.
Premier Partnership
Python for Advanced Machine Learning Course
We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company
Sebastiaan Holman
Machine Learning and Deep Learning Course
The training provided the right foundation that allows us to further to expand on, by showing how theory and practice go hand in hand. It actually got me more interested in the subject than I was before.
Jean-Paul van Tillo
Machine Learning and Deep Learning Course
Coverage and depth of topics
Anirban Basu
Machine Learning and Deep Learning Course
The global overview of deep learning
Bruno Charbonnier
Advanced Deep Learning Course
The exercises are sufficiently practical and do not need a high knowledge in Python to be done.
Alexandre GIRARD
Advanced Deep Learning Course
Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.
Paul Kassis
Advanced Deep Learning Course
Very flexible
Frank Ueltzhöffer
Artificial Neural Networks, Machine Learning and Deep Thinking Course
The topic is very interesting
Wojciech Baranowski
Introduction to Deep Learning Course
Trainers theoretical knowledge and willingness to solve the problems with the participants after the training
Grzegorz Mianowski
Introduction to Deep Learning Course
Topic. Very interesting!
Piotr
Introduction to Deep Learning Course
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
Artificial Neural Networks, Machine Learning, Deep Thinking Course
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Artificial Neural Networks, Machine Learning, Deep Thinking Course
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