Udemy - The Magic of Deep Learning with TensorFlow 2 [Course Drive]

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The Magic of Deep Learning with TensorFlow 2 The Magic of Deep Learning with TensorFlow 2 1. Getting started with Tensorflow 2
  • 2. Machine Learning Algorithm Part 1.mp4 (107.7 MB)
  • 0. Introduction.pdf (110.4 KB)
  • 1. Installing TensorFlow 2.mp4 (26.6 MB)
  • 1. Installing TensorFlow 2.srt (4.1 KB)
  • 2. Machine Learning Algorithm Part 1.srt (9.5 KB)
  • 3. Machine Learning Algorithm Part 2.mp4 (95.5 MB)
  • 3. Machine Learning Algorithm Part 2.srt (7.8 KB)
  • ReadMe.txt (0.2 KB)
  • Visit Coursedrive.org.url (0.1 KB)
    • 2. Diffrence Between TensorFlow 1.x and TensorFlow 2
    • 1. Diffrence Between TensorFlow 1.x and TensorFlow 2 Part 1.mp4 (47.1 MB)
    • 1. Diffrence Between TensorFlow 1.x and TensorFlow 2 Part 1.srt (7.3 KB)
    • 2. Diffrence Between TensorFlow 1.x and TensorFlow 2 Part 2.mp4 (46.1 MB)
    • 2. Diffrence Between TensorFlow 1.x and TensorFlow 2 Part 2.srt (8.8 KB)
    • 3. Preprocessing Image Part 1.mp4 (68.6 MB)
    • 3. Preprocessing Image Part 1.srt (9.1 KB)
    • 4. Preprocessing Image Part 2.mp4 (23.2 MB)
    • 4. Preprocessing Image Part 2.srt (3.1 KB)
    3. Face Detection with TensorFlow 2
    • 1. Introduction.mp4 (15.4 MB)
    • 1. Introduction.srt (1.7 KB)
    • 2. Buidling the First Module preprocessing.py with TensorFlow 2 part 1.mp4 (31.0 MB)
    • 2. Buidling the First Module preprocessing.py with TensorFlow 2 part 1.srt (4.1 KB)
    • 3. Buidling the First Module preprocessing.py with TensorFlow 2 part 2.mp4 (74.8 MB)
    • 3. Buidling the First Module preprocessing.py with TensorFlow 2 part 2.srt (6.3 KB)
    • 4. Buidling the First Module preprocessing.py with TensorFlow 2 part 3.mp4 (69.4 MB)
    • 4. Buidling the First Module preprocessing.py with TensorFlow 2 part 3.srt (4.6 KB)
    • 5. Buidling the Second Module classifier.py with TensorFlow 2.mp4 (100.2 MB)
    • 5. Buidling the Second Module classifier.py with TensorFlow 2.srt (8.6 KB)
    • 6. Buidling the Third Module data_preprocesses.py with TensorFlow 2.mp4 (6.8 MB)
    • 6. Buidling the Third Module data_preprocesses.py with TensorFlow 2.srt (1.0 KB)
    4. Yolo Detector with TensorFlow 2
    • 1. Introduction.mp4 (4.9 MB)
    • 1. Introduction.srt (0.6 KB)
    • 2. The dataset creation.mp4 (58.1 MB)
    • 2. The dataset creation.srt (8.5 KB)
    • 3. Labelling the dataset and generate the annotation XML file.mp4 (48.4 MB)
    • 3. Labelling the dataset and generate the annotation XML file.srt (7.2 KB)
    • 4. Modifying and training your custom Yolo detector for the new object.mp4 (19.6 MB)
    • 4. Modifying and training your custom Yolo detector for the new object.srt (3.2 KB)
    5. The Magic of TensorFlow 2
    • 1. Detecting the faces in Vidoes Part 1.mp4 (82.3 MB)
    • 1. Detecting the faces in Vidoes Part 1.srt (8.9 KB)
    • 2. Detecting the faces in Vidoes Part 2.mp4 (64.0 MB)
    • 2. Detecting the faces in Vidoes Part 2.srt (6.9 KB)
    • 3. Yolo Object detector live stream.mp4 (40.2 MB)
    • 3. Yolo Object detector live stream.srt (4.2 KB)
    • Visit Coursedrive.org.url (0.1 KB)
    • ReadMe.txt (0.2 KB)

Description

⚡️⚡️For More Udemy Courses Visit ?? Course Drive



The Magic of Deep Learning with TensorFlow 2

course 1 of 5 from Deep Tensor specialization






What you'll learn

• Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
• Machine Learning Algorithms
• Learn to use TensorFlow 2.0 for Deep Learning
• TensorFlow 1.x VS TensorFlow 2
• Image Recognition
• Computer Vision
• Convolutional Neural Network
• Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance
• Learn how to train network weights and biases and select the proper transfer functions.
• Learn how to Create your own data-set
• Retrain a pretrained model
• YOLO object detector
• Apply Convolutional Neural Networks to classify images.
• build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow

Requirements

• basics in math
• basics in deep learning
• Some basic Python programming skills
• You’ll need to install Anaconda. We will show you how to do it in one of the first lectures of the course.
• All software and data used in the course are free.


Description

DL continues to take powerful steps to become a part of almost any software in the future. DL is one of the most popular research areas of machine learning. What makes it so popular is the exciting applications of recent times. Nowadays, improvement in machine learning provides the ability to determine what an object in a picture does.
this is the course one from our specialization deep tensor, in this course we will going to take multiple real-world projects using Tensorflow 2
• you will learn about Tensorflow 1.x then introduce you to TensorFlow 2 we will going to take a lot of information and intuition of how to see the difference between those two versions
• you will learn a lot of preprocessing techniques that help you to create a superior dataset that can achieve a high accuracy
• you will learn how to make a full industry project by subdividing the files into sub-files, each python file have multiple classes that can be imported in your main python script professionally
• you will learn how to design and make a classifier which is robust and achieve high accuracy with Deep convolutional Neural Networks
• you will learn how to design a classifier for face detection
• you will learn how to use a pre-trained model and customize it to your object detector
• usage of the most popular approaches for object detector that called YOLO detector algorithm
by the end of this course, you will feel comfortable and confident after learning those projects using python and deep learning, so yeah what you are waiting for let's begin this journey together and I hope to enjoy the ride thanks.

Who this course is for:

• Beginners to advanced students who want to learn about deep learning and Machine learning in Tensorflow 2.0







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Udemy - The Magic of Deep Learning with TensorFlow 2 [Course Drive]

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Udemy - The Magic of Deep Learning with TensorFlow 2 [Course Drive]

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