English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 45m | 893 MB
Apply Deep Learning to different data types and solve real-world problems with TensorFlow
We will not only get you up-and-running with deep learning, but also equip you with the skills to implement your own neural networks and apply them to the real world.
We will use TensorFlow, an efficient Python library used to create and train our neural networks. You’ll learn the skills to implement their architecture quickly and efficiently without having to deal with minutiae.
You can rely on our expert guidance while learning the basic theory, backed up with relevant examples. We provide examples of neural networks, which you can use to highlight the key features. We then build up to more advanced networks. You’ll learn to utilize a Convolutional Neural Network to classify images of handwritten text and then take your CNN further to perform object detection and localization in an image.
This course will breeze through some essential textbook knowledge when it comes to machine learning. Following a brief math section, we get started with deep learning straight away.
What You Will Learn
- Properly understand the meaning of deep learning
- Train a neural network and understand the often complicated process of backpropogation.
- Create datasets in the correct format for use with TensorFlow—a key step when it comes to training your own models.
- Create your own neural network architecture in TensorFlow using Keras, allowing you to define any architecture for your own needs.
- Get accustomed to convolutional neural networks and understand why they are so powerful for image classification.
- Use the TensorFlow ObjectDetection API to classify and localize objects in an image
Table of Contents
An Introduction to Deep Learning and TensorFlow
1 The Course Overview
2 What Is Deep Learning
3 Why Is Deep Learning Useful
4 Activation Functions
5 Training Neural Networks
Getting Started with TensorFlow
6 Installing TensorFlow
7 Creating the Training Dataset
8 Creating the Models
9 Training the Model
10 Visualization and Evaluation
Implementing Your First Neural Network
11 Loading the Dataset
12 Defining the Model and Estimator
13 Training
14 Evaluation and Visualization
Handwritten Digit Classification
15 Introduction to CNNs
16 Loading the Dataset
17 Constructing the Classifier – Part One
18 Constructing the Classifier – Part Two
19 Training the Model
20 Testing and Results
Object Detection and Classification
21 Testing the Pre-Trained Model With Object Detection API
22 Creating a Cats and Dogs Dataset
23 Training Your New Model
24 Deploying Your New Model
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