Introduction_to_Deep_Learning.pdf

Introduction to Deep Learning and Neural Networks
Augustine Okolie
Knowledge and Skill Forum
Augustine Okolie (Knowledge and Skill Forum) 2023 1 / 30

Outline
1 Introduction
2 Deep Learning (Artificial Neural Network)
3 Required Knowledge/Skills for Computer Vision
4 Applications
5 Challenges and Complexity of Neural Networks

Introduction
Artificial intelligence (AI) has revolutionized numerous industries.
Machine learning (ML) is a subset of AI, where systems learn from data, identify patterns,
and make decisions.
Neural networks and deep learning are specialized sub-fields of ML, gaining substantial
attention due to their profound capabilities.

Outline
1 Introduction
4 Applications

Deep Learning
Deep learning is part of a broader family of machine learning methods based on artificial neural
networks with representation learning. Learning can be supervised, semi-supervised or
unsupervised

Brain Nervous System vs Artificial Neural Network
Neurons are the fundamental units of the brain and nervous system.
Neural networks are machine learning models inspired by the human brain.
Composed of interconnected layers of nodes or ”neurons”.
Each neuron applies specific transformations to input data, enabling the model to learn and understand complex
patterns.

Deep Neural Network
Deep refers to multiple hidden layers in the network that enable learning of more complex
representations.
Performs complex operations on massive amounts of structured and unstructured data.
Excels in domains such as image recognition and natural language processing.

Components of a Neural Network
Neurons: Fundamental units of a neural network that receive inputs and generate an
output based on the activation function.
Weights: Coefficients which transform the input data within the network’s layers.
Bias: Similar to the intercept in a linear equation, it is an additional parameter in the
network which helps to adjust the output along with the weighted sum of the inputs to
the neuron.
Activation Functions: Functions that decide whether a neuron should be activated or
not. They help to add non-linearity to the network and map the input between the
required values like (0, 1) or (-1, 1).

Artificial Neural Networks: Hand Written Digit Recognition

Convolution Neural Networks: Image Classification

Feature Extraction (Low Level, Mid Level, High Level, ...)

Is CNN better than ANN?
In general, CNN tends to be a more powerful and accurate way of solving classification problems. ANN is still dominant
for problems where datasets are limited, and image inputs are not necessary.

Recurrent Neural Network (RNN)
RNN has the ability to process temporal information — data that comes in sequences, such as a sentence, time series, etc.

Outline
1 Introduction
4 Applications

Mathematics in Computer Vision
1 Calculus
2 Linear Algebra
3 Probabilities and Statistics
4 Signal Processing
5 Projective Geometry
6 Computational Geometry
7 Optimization Theory
8 Control Theory

Software, Programming and Computer Vision Tools
1 Software and Programming Languages for Deep Learning: Python, C++, R, Julia,
MATLAB, etc.
2 Python Frameworks for Deep Learning: TensorFlow, PyTorch, Keras, Theano,
Caffe/Caffe2, OpenCV, Numpy, ScikitLearn, Skimage, etc.
3 Web-based Software: Flask/Django(Python), FastAPI (Python), Node.js/Express.js
(JavaScript), etc.
4 Cloud-based Machine Learning Platforms: Amazon SageMaker, Google Cloud ML
Engine, Microsoft Azure ML, IBM Watson, etc.
5 Mobile-based Software: TensorFlow Lite (Android/iOS), Core ML (iOS), ONNX
(Android/iOS), etc.
6 Containers and Orchestration: Docker/Kubernetes, Kubeflow, etc.

Outline
1 Introduction
4 Applications

Applications of Neural Networks
Image Recognition: Convolutional Neural Networks (CNNs) are widely used in digital
image processing, for tasks like object detection, facial recognition, and medical image
analysis.
Natural Language Processing: Recurrent Neural Networks (RNNs) and transformers
are commonly used for tasks such as language translation, sentiment analysis, text
generation, and speech recognition.
Time Series Prediction: RNNs are often employed to predict future values in time series
data, like stock prices or weather patterns.
Reinforcement Learning: Deep Q-Networks (DQNs) use neural networks to estimate
the quality of different actions in a given state.
Generative Models: Generative models use neural networks to generate new data that
resembles some given real data.

Object Detection

Image Segmentation

Face Detection

Medical Imaging

Language Translation

Time Series Prediction

Industrial Robots

Text Generation (ChatGPT)

Image Generation (GANS)
Source: thispersondoesnotexist.com

Applications
AND MANY MORE!

Outline
1 Introduction
4 Applications

Challenges and Complexity of Neural Networks
Requirement for large amounts of data and computational resources.
Overfitting: Fitting the training data too closely and performing poorly on unseen data.
Local minima in optimization.
Difficulty in understanding and interpreting neural networks - often referred to as ”black
box” models.
The challenge of choosing the right architecture and hyperparameters.

Introduction_to_Deep_Learning.pdf

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