Master Deep Learning for Computer Vision with TensorFlow 2

Implement Object detection, Image Segmentation, Image Classification, Image Generation & People Counting from scratch!

Published 04/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 103 lectures (29h 4m) | Size: 12 GB

What you’ll learn

• Introductory Python, to more advanced concepts like Object Oriented Programming, decorators, generators, and even specialized libraries like Numpy & Matplotlib
• Mastery of the fundamentals of Machine Learning and The Machine Learning Developmment Lifecycle.
• Linear Regression, Logistic Regression and Neural Networks built from scratch.
• TensorFlow installation, Basics and training neural networks with TensorFlow 2.
• Convolutional Neural Networks, Modern ConvNets, training object recognition models with TensorFlow 2.
• Breast Cancer detection, people counting, object detection with yolo and image segmentation
• Generative Adversarial neural networks from scratch and image generation

Requirements

Basic Math
No Programming experience.

Description

In this course, we shall look at core Deep Learning concepts and apply our knowledge to solve real world problems in Computer Vision using the Python Programming Language and TensorFlow 2. We shall explain core Machine Learning topics like Linear Regression, Logistic Regression, Multi-class classification and Neural Networks. If you’ve gotten to this point, it means you are interested in mastering Deep Learning For Computer Vision and using your skills to solve practical problems.

You may already have some knowledge on Machine learning, computer vision or Deep Learning, or you may be coming in contact with Deep Learning for the very first time. It doesn’t matter from which end you come from, because at the end of this course, you shall be an expert with much hands-on experience.

You shall work on several projects like object detection, image generation, object counting, object recognition, disease detection, image segmentation and more, using knowledge gained from this course.

If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!

This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum, will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.

Here are the different concepts you’ll master after completing this course.

• Fundamentals Machine Learning.
• Essential Python Programming
• Choosing Machine Model based on task
• Error sanctioning
• Linear Regression
• Logistic Regression
• Multi-class Regression
• Neural Networks
• Training and optimization
• Performance Measurement
• Validation and Testing
• Building Machine Learning models from scratch in python.
• Overfitting and Underfitting
• Shuffling
• Ensembling
• Weight initialization
• Data imbalance
• Learning rate decay
• Normalization
• Hyperparameter tuning
• TensorFlow Installation
• Training neural networks with TensorFlow 2
• Imagenet training with TensorFlow
• Convolutional Neural Networks
• VGGNets
• ResNets
• InceptionNets
• MobileNets
• EfficientNets
• Transfer Learning and FineTuning
• Data Augmentation
• Callbacks
• Monitoring with Tensorboard
• Breast cancer detection
• Object detection with YOLO
• Image segmentation with UNETs
• People counting
• Generative modeling with GANs
• Image generation

Who this course is for

• Beginner Python Developers curious about Applying Deep Learning for Computer vision
• Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning.
• Anyone who wants to master deep learning fundamentals and also practice deep learning for computer vision using best practices in TensorFlow.
• Deep Learning for Computer vision Practitioners who want gain a mastery of how things work under the hood.