Download Master Deep Learning for Computer Vision with TensorFlow 2

What you would learn in Master Deep Learning for Computer Vision with TensorFlow 2 course?

This class will explore the core Deep Learning concepts and apply our knowledge to tackle real-world issues within Computer Vision using the Python Programming Language and TensorFlow 2. We will discuss the most fundamental Machine Learning topics like Linear Regression, Logistic Regression, Multi-class classification, and Neural Networks. If you've reached this point, you're keen on mastering Deep Learning For Computer Vision and using your expertise to tackle real-world issues.

You might already have experience with Machine learning, Computer Vision, Machine Learning, or Deep Learning, or you might be coming into the world of Deep Learning for the first time. It doesn't matter which side you're from, as after this class, you will be a professional with lots of practical knowledge.

You will be working on a variety of projects, including image generation, detection of objects as well as to object counting, recognition of objects as well as disease detection, and image segmentation, among others, with the knowledge you have gained in this course.

If you're ready to leap to your current career goals, this program is for you, and we're thrilled to assist you in achieving your objectives!

This course is provided via Neuralearn. Like every other course from Neuralearn, we place a lot of importance on feedback. Your comments and questions on the forum will assist us in improving this course. Please feel free to post as many questions as you can in the forum. We try our best to answer in the shortest amount of time.

These are the various concepts you'll learn after taking this course.

• Fundamentals Machine Learning.

• Essential Python Programming

• The selection of a machine model is about the job

• Error sanctioning

• Linear Regression

• Logistic Regression

• Multi-class Regression

• Neural Networks

• Training and optimization

• Performance Evaluation

• Validation and Testing

• Making Machine Learning models from scratch using Python.

• Overfitting and underfitting

• Shuffling

• Ensembling

• Weight initialization

• Data imbalance

• Learning rate decay

• Normalization

• Hyperparameter tuning

• TensorFlow Installation

• Training neural networks by using TensorFlow 2.

• Imagenet training using TensorFlow

• Convolutional Neural Networks

• VGGNets

• ResNets

• InceptionNets

• MobileNets

• EfficientNets

• Transfer Learning and FineTuning

• Data Augmentation

• Callbacks

• Monitoring using Tensorboard

• Breast cancer detection

• Object detection using YOLO

• Image segmentation using UNETs

• People who count

• Generative modeling using GANs

• Image generation

Course Content:

• An introduction to Python and more advanced concepts such as Object-Oriented Programming, decorators generators, specific libraries such as Numpy and Matplotlib
• Understanding the basics of Machine Learning and The Machine Learning Development Lifecycle.
• Linear Regression, Logistic Regression, and Neural Networks built from scratch.
• TensorFlow installation, basics, and an introduction to neural network training using TensorFlow 2.
• Convolutional Neural Networks, Modern ConvNets Training models for object recognition using TensorFlow 2.
• Breast cancer detection: counting, breast cancer detection, the number of people who have breast cancer, detecting objects with Yolo, and image segmentation.
• Generative Adversarial neural network made from scratch, and also image generation

Download Master Deep Learning for Computer Vision with TensorFlow 2 from below links NOW!