Gain the tools and knowledge you need to begin developing your own Deep Learning projects in this introductory course with Leonardo De Marchi.
Take a look at Deep Learning concepts with Keras by analysing an image recognition project and learning to develop the model from start to finish.
Examine the business needs of a project and design a solution, create a multi layer network and get an intro 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.
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
Starts at 1:00 PM BST (12:00 PM UTC)
Our team is happy to discuss other options with you.
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Private tuition and large-group discounts are also available. Find out more here.
Who should take this course?
This course is intended for Data Scientists, Analysts and Developers who are interested in Deep Learning as a 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.
- DL Basics
- 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