|
Week |
Date |
Number |
Main Topic |
Details |
| BACKGROUND |
1 |
15/01/2020 |
1 |
Overview |
Introduction
to deep learning |
| |
|
17/01/2020 |
2 |
Classification
and Learning |
Using deep
networks for classification and learning
tasks |
| |
2 |
22/01/2020 |
3 |
Modularity |
Architectures
for Deep networks |
| |
|
24/01/2020 |
4 |
Mathematical
Background |
Matrices and
differential calculus |
| BASIC
INFERENCE |
3 |
29/01/2020 |
5 |
DL Inference |
Computation
Graphs: Introduction
Theory
of Inference
TensorFlow
|
| |
|
31/01/2020 |
6 |
Inference:
classification |
Computation
graph:
Backpropagation
|
| |
4 |
05/02/2020 |
7 |
TensorFlow:
practical aspects |
TensorFlow
implementation details
Programming
Problem: fully-connected MNIST
|
| |
|
07/02/2020 |
8 |
Deep Learning:
Training |
Backpropagation;
Optimisation
Mid-Term:
Practice Questions
Solutions
to Practice Questions
|
| PRACTICAL
ASPECTS |
5 |
12/02/2020 |
9 |
Practical
Aspects |
Overview:
Architectures; Activation and Loss
functions |
| |
|
14/02/2020 |
10 |
Practical
Aspects |
Weight
initialisation; Data pre-processing |
| |
6 |
19/02/2020 |
11 |
Practical
Aspects |
Deep-Network
practical issues; CPU/GPU practical issues
MidTerm
Practice problems
|
| |
|
21/02/2020 |
12 |
Practical
Aspects: Training Deep Networks |
Optimization, gradient
analysis,batch
optimization
Learning
Rate
|
| |
7 |
|
|
MIDTERM |
|
| |
|
|
|
WEEK
|
|
| CNN |
8 |
04/03/2020 |
13 |
Convolution
Neural Networks (CNNs) |
Theory of
CNNs; architectures and convolution
operations
|
| |
|
06/03/2020 |
14 |
Convolution
Neural Networks (CNNs) |
Applications
of CNNs
Programming
Assignment 1
|
| |
9 |
11/03/2020 |
15 |
|
Applications
of CNNs
|
| TEMPORAL NETWORKS |
|
13/03/2020 |
16 |
Temporal Deep Networks |
RNN |
| |
10 |
18/03/2020 |
17 |
Temporal Deep Networks |
LSTM |
| |
|
20/03/2020 |
18 |
Temporal Deep Networks |
RNN/LSTM
applications |
| REINFORCEMENT
LEARNING |
11 |
25/03/2020 |
19 |
Deep
Reinforcement Learning |
Deep
Reinforcement Learning introduction;
critic-based methods |
| |
|
27/03/2020 |
20 |
Deep
Reinforcement Learning |
Deep
Reinforcement Learning: actor-based methods |
| |
12 |
01/04/2020 |
21 |
Deep-RL-applications |
|
| |
|
03/04/2020 |
22 |
Deep
Probabilistic Networks |
Restricted
Boltzmann Machines; Deep Bayesian
Networks
|
