First Technology Transfer

Deep Learning using Python

Duration: 5 Days

Course Overview, Intended Audience and Pre-requisites

• A practical deep immersion into deep learning algorithms
• Introduce the practical use of the deep learning Theano, Caffe, Keras, and TensorFlow.
• Provide an intensive introduction to the use and implementation of Auto-Encoders and Restricted Boltzmann Machines
• Provide practical teaching in the theory, training and use of Deep Belief Nets and Deep Neural Networks
• Introduce Dropout and Convolutional Neural Networks
• Overview current successes and discuss how to best apply Get deep learning algorithms and libraries in the real world

Course Contents

• What is Deep Learning?
• Artificial Intelligence and Machine Learning
• The "Deep" in Deep Learning - what is it ?
• Hardware and Software aspects of the "Deep Learning Revolution"
• Mathematical Foundations of Neural Networks
• Intensive overview of Scipy and Numpy
• Data representation - Scalars, Vectors and Matrices
• 3D tensors and higher-dimensional tensors
• Real world data examples
• Timeseries data or sequence data
• Image data
• Video data
• Geometric interpretation of tensor operations
• A geometric interpretation of deep learning
• Gradient-based optimization
• Stochastic gradient descent
• Chaining derivatives - the backpropagation algorithm
• Training neural networks using gradient descent
• Anatomy and Physiology of Neural Networks
• Layers: the Lego bricks of deep learning
• Networks of layers
• Loss functions and optimizers - the keys to configuring the learning process
• Python Neural Network Machine Learning Frameworks - An overview ofKeras, TensorFlow, Theano, and CNTK
• Practical deep learning issues
• Setting up a deep learning workstation
• Jupyter notebooks and Keras
• Running deep learning jobs in the cloud: pros and cons
• Selecting GPUs for deep learning?
• Deep Learning Workflows
• Preparing the data
• Building the network
• Validating the approach
• Using the trained network to generate predictions on new data
• Regularising the model and tuning its hyperparameters
• Foundations of machine learning
• Supervised learning.
• Unsupervised learning.
• Self-supervised learning.
• Reinforcement learning.
• Classification and regression
• Training, validation, and test sets
• Simple hold-out validation
• K-fold validation
• Iterated K-fold validation with shuffling
• Data preprocessing, feature engineering and feature learning
• Overfitting and underfitting
• Fighting overfitting
• Deep learning for computer vision
• Introduction to convnets (Convolutional Neural Networks)
• The convolution operation
• The max pooling operation
• Training a convnet from scratch on a small dataset
• The relevance of deep learning for small-data problems
• Data preprocessing
• Using data augmentation
• Using a pre-trained convnet
• Feature extraction
• Visualizing what convnets learn
• Visualizing intermediate activations
• Visualizing convnet filters
• Visualizing heatmaps of class activation
• Deep learning for text and sequences
• Working with text data
• One-shot encoding of words or characters
• Using word embeddings
• From raw text to word embeddings
• Recurrent neural networks (RNNs)
• LSTMs - Long Short Term Memory networks
• GRUs - Gated Recurrent Units
• Stacking recurrent layers
• Using bidirectional RNNs
• Convnets as an alternative to RNNs for sequence processing
• Using 1D convolution for sequence data
• Pooling for sequence data
• Combining CNNs and RNNs to process long sequences
• Advanced deep learning
• Multi-input models
• Multi-output models
• Directed acyclic graphs of layers
• Layer weight sharing
• Models as layers
• Generative deep learning
• GANs - Generative adversarial networks
• DCGANs - Deep Convolutional Generative Adversarial Networks

Call us:

Technical enqiries: 020 8669 0769
Sales enquiries: 020 8647 1939, 020 77681 40786