In this two-day Deep Learning training course, you will gain the tools and knowledge to begin developing or enhance your existing Deep Learning projects.
- Boost your data analysis with the latest principles and tools of Deep Learning -
Join Leonardo De Marchi, Lead Data Scientist for Badoo, as he takes a look at Deep Learning concepts with Keras by analysing an image recognition project. He will teach you theory and practical knowledge to develop the model from start to finish. Learn how to best examine the business needs of a project to design a solution, as well as how to create a multi-layer network. Finally, this Deep Learning course will introduce you to some more sophisticated practices, including: implementing different types of networks for image recognition; using dropouts and random noise to improve results; selecting the proper architecture; and using pre-trained models.
Who you will be sitting next to
Find yourself next to data scientists, analysts and developers who are interested in Deep Learning as a way to save time and resources.
How to apply these skills
Come away with a thorough understanding of how Deep Learning and its associated tools and concepts can inform and dramatically shape your data analysis, while helping you to save resources.
Book early to receive a discount on the course price and in doing so you will not only commit to growing your own skillset, but help us grow our community of over 140,000 passionate techies.
Learn how to:
- Understand the theory behind neural networks
- Work with Keras and Pytorch
- Create a basic Deep Learning setup
- Complete an image recognition task end to end
- Debug and tune the network
- Use some more advanced concepts and tricks of the trade
- Introduction to Deep Learning
- Key ML concepts and terminology
- Examples in industry and research
- Formalizing your own DL problem
Feedforward Neural Networks
- Training neural networks, optimization methods, error back-propagation
- Introduction to Keras and Pytorch
Lab 0: Getting Started with GPUs and cloud computing
- Quickly set up a machine with Deep Learning and NVIDIA Docker
- Set up additional libraries like keras-viz
- Quick demo on how to use tensorboard
TensorFlow, Keras and Pytorch
- Pros and Cons
- How to use the right tool
Lab 1: Getting Started with Keras
- Basic concepts
Lab 2: Implement and train a feed-forward neural network in Keras
- Tackling the problem of facial expression recognition
Convolutional Neural Networks (CNNs)
- Understanding the convolutional architecture
- Convolutional and pooling layers
- Applications to image classification
Lab 3: Implementing CNN using Keras A
- Extending a feed-forward network with convolutional and pooling layers
- Using CNNs for image data
Lab 3 continued: Implementing CNN using Keras B
- Recap of the main concepts
- Lab solutions
Recurrent Neural Networks
- Understanding recurrent architectures
- Elman, LSTM and GRU units
- Bi-directional architectures
- Combining RNNs with convolutional and feed-forward layers
- Applications to speech
- Biological sequences and information retrieval
Lab 4: Implementing RNNs using Keras
- Implementing and training RNNs using LSTM units on a simple natural language processing task
Practical tricks of the trade
- Using pre-trained networks
- Transfer learning
- Visual debugging of DNNs
Lab 5: Practical tricks of the trade
- Practicing concepts of the previous theoretical session
Closing remarks and feedback
- Introduction to Multi-Armed bandit and Reinforcement Learning
This course is intended for Data Scientists, Analysts and Developers who are interested in Deep Learning as way to save time and resources. It will provide all basic information to get started straight away.
Delegates should have basic python knowledge. Machine Learning knowledge is advantageous, but not required.
Bring your own hardware
In order to participate in this workshop, delegates are required to bring their own laptop, with a modern web browser and text editor installed.