Solving Machine Learning Problems in TensorFlow/Keras (Image Processing using Deep Learning

Cui i se adresează?

Acest curs se adresează Machine Learning Engineers – image processing

Ce vei învăța?

În cadrul acestui curs cursanții vor învăța despre clasificarea și localizarea imaginilor împreună cu cele mai bune practici în Computer Vision.

Cerințe preliminare:

Pentru a putea participa în cadrul acestui curs, studenții trebuie să fi parcurs modulul Basic Machine Learning în Tensorflow/Keras.

Este recomandat ca studenții să aibă următoarele cunoștințe:

Basic Deep Learning

● Neurons
● Types of Layers
● Networks
● Loss Functions
● Optimizers
● Overfitting
● Tensorflow

Basic Image Processing/Computer Vision

● Encoding
● Color Spaces
● Convolutions
● OpenCV/PIL

Agenda cursului:

Materialele de curs sunt în limba Engleză. Predarea se face în limba Română.

Module 1: Introduction to Deep Learning in Image Processing

Machine Learning and Deep Learning
Neural Network Anatomy
Types of Convolutions
Keras Workflow

Module 2: Basic Image Processing and Computer Vision

Pixels and Images
Coordinate System
Channels
OpenCV
Channel Ordering
Blur and Sharpen kernels

Hands-on Lab: Learn basic Image Processing using OpenCV, learn to apply different filter kernels on images for blur generation or basic edge detection.

Module 3: Supervised Neural Networks and Regularization

Underfitting
Overfitting
Reducing the networks size
Weight Regularization: L1, L2, Elastic
Dropout
Batch Normalization

Hands-on Lab: Implement your first basic neural network, learn how to benchmark it and learn how to avoid overfitting on a Computer Vision classification task.

Module 4: Convolutional Neural Networks

Convolutional Layers
Depthwise Convolutions
Building Convolutional Neural Networks in Keras
1×1 Convolutions
Data Augmentation

Hands-on Lab: Improve your previous neural network by adding Convolutional Layers, benchmark them and compare them with the Fully Connected ones.

Module 5: Common Convolutional Neural Networks Architectures

ImageNet
AlexNet
VGGNet
ResNet
MobileNet

Hands-on Lab: Learn how to use already state of the art models from the Keras Hub.

Module 6: Reusing Convolutional Neural Networks

Object Localization
Object Segmentation
Reusing VGG
Fine-tuning

Hands-on Lab: Learn how to fine parameter tune your already trained Convolutional Neural Network to fit your task.

Module 7: Explainable AI

Visualizing intermediate activations
Visualizing convnet
Visualizing heatmaps

Module 8: Unsupervised Generative Models for Image Processing

Autoencoders for Images
Deblurring
Image generation

Hands-on Lab: Generate a new image similar to the ones from the dataset by using a random seed. Face generation techniques

Module 9: Real World Machine Learning

Tensorboard
Deploying Deep Learning Models
Choosing the algorithm

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