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Deep Learning for Computer Vision with Tensor Flow and Keras

Deep Learning review, CNNs, VGG-16, Faster R-CNN, TensorFlow Object Detection API, YOLO. Train your own data.
Instructor:
CARLOS QUIROS
1,254 students enrolled
English [Auto-generated]
Learn about the state of the art in object detection and image classification models.

This course is focused in the application of Deep Learning for image classification and object detection. This course includes a review of the main lbraries for Deep Learning such as Tensor Flow and Keras, the combined application of them with OpenCV and also covers a concise review of the main concepts in Deep Learning. We will also enter in the study of Convolutional Neural Networks for image classification reviewing different robust architectures. 

We will explore the concepts of Object Detecting and Transfer Learning using the last state of the art algorithms for object detection such as Faster R-CNN, TensorFlow Object Detection API and YOLO, applying this models on images, videos, and webcam images. Finally you will learn how to construct and train your own dataset.
You will find in this course a consice review of the theory with intuitive concepts of the algorithms, and you will be able to put in practice your knowledge with many practical examples.

Installation and Setup

1
Introduction to the course Deep Learning for Computer Vision
2
Before starting read these important guidelines!!
3
Install Anaconda, OpenCV and other packages
4
Install TensorFlow and Keras for CPU and GPU

TensorFlow and Keras Overview

1
TensorFlow Introduction and Sessions
2
TensorFlow Variables, Placeholders and Tensors
3
TensorFlow Operations
4
Configuring Sessions in TensorFlow
5
Tensorboard
6
Introduction to Keras
7
Creating models with Keras
8
Logistic Regression with Keras
9
Keras Callbacks
10
Keras Checkpointing, Saving and Loading models

Deep Learning Overview

1
Deep Learning Overview
2
Gradient Descent
3
Beyond Gradient Descent
4
Loss Functions
5
Regularization
6
Activation Functions
7
Evaluating Activation Functions with Keras
8
Multilayer Perceptron with Keras

Convolutional Neural Networks

1
Image Fundamentals
2
Convolutional Neural Network model
3
Convolutional Operation
4
Convolution Operation examples
5
Max Pooling
6
ReLU function
7
Batch Normalization
8
DropOut
9
Fully Connected Layer
10
Handwritten Digits with TensorFlow part 1
11
Handwritten Digits with TensorFlow part 2
12
Handwritten Digits with TensorFlow part 3
13
Handwritten Digits with Keras
14
CIFAR10 with Keras
15
Fashion MNIST with Keras and Tensorboard
16
Embeddings Visualization with Fashion MNIST
17
Pretrained Models - VGG16

Creating an Image Dataset

1
Collecting images from the web
2
Labeling data with imglbl
3
Loading data in TensorFlow

Object Detection and Transfer Learning

1
Introduction to Object Detection
2
Object Detection with Faster R-CNN
3
Metrics in Object Detection Models
4
TensorFlow Object Detection API
5
TensorFlow OD API + OpenCV: using Webcam for object recognition
6
The Yolo (You only look once) Model
7
Running Yolo v2 on images
8
Yolo object detection on video and webcam
9
Detecting custom objects with Yolo v2
10
Resources

All Codes, images and resources of this course

Bonus Classes

1
Detecting custom objects with Yolov2 in Jupyter
2
Blood Cell Detection with Faster R-CNN part 1
3
Blood Cell Detection with Faster R-CNN part 2
4
Installing Cuda 10.0 - TensorFlow 1.13 GPU
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Includes

9 hours on-demand video
3 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion