English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 2h 47m | 1.04 GB
Create and unleash the power of neural networks using TensorFlow and deep learning concepts in a hands-on style
Ever wondered how you can solve everyday data problems quickly and easily like automating text processing, classifying images, and predicting results? Neural networks and Tensorflow, one of the most powerful technologies, will come to your rescue. These are important technologies for data scientists to know because they are often more powerful than traditional machine learning techniques.
In this course, you’ll use neural nets to solve business and other real-world problems and make predictions quickly and easily. You’ll program a machine to identify a human face, predict stock market prices, and process text as part of Natural Language Processing (NLP). You’ll also gain experience using generative models and autoencoders to create artwork and enhance images.
By the end of this course, you will be able to tackle a range of challenges beyond this course and will have a fair understanding of how you can use the power of TensorFlow to train neural networks of varying complexities, without any hassle.
The course gives you hands-on experience with neural networks and deep learning to teach you the basics so you can apply them yourself.
What You Will Learn
- Understand what neural networks and TensorFlow are and what problems they have solved and can solve.
- Build your own multilayer perceptron neural network to predict fraud and hospital patient readmission
- Build your own convolutional neural network classifier to automatically identify a photograph
- Build your own recurrent neural network to forecast time series and stock market data
- Build your own Long Short Term Memory Model (LSTM) model to classify movie reviews as positive or
- negative using Natural Language Processing (NLP).
- Build your own generative model to create computer-generated art.
- Colorize a black and white photo using an autoencoder.
Table of Contents
Artificial Neural Networks
1 The Course Overview
2 Introduction To Neural Networks
3 Setting Up Environment
4 Introduction To TensorFlow
5 TensorFlow Installation
Neural Networks for Classification
6 Multilayer Perceptron Neural Network
7 Forward Propagation and Loss Functions
8 Backpropagation
9 Creating First Neural Network to Predict Fraud
10 Testing Neural Network to Predict Fraud
Use Convolutional Neural Networks (CNNs) to Identify Faces
11 Introduction To Convolutional Neural Networks
12 Training a Convolution Neural Network
13 Testing a Convolution Neural Network
Use Recurrent Neural Networks to Forecast the Stock Market
14 Introduction To Recurrent Neural Networks
15 Training a Recurrent Neural Network
16 Testing a Recurrent Neural Network
Use LSTM Networks to Classify Movie Reviews
17 Introduction To Long Short-Term Memory Network
18 Training an LSTM Network
19 Testing a Long Short-Term Memory Network
Use Generative Models to Create Artwork from Images
20 Introduction To Generative models
21 Neural Style Transfer – Basics
22 Results – Neural Style Transfer
Use Autoencoders to Colorize Images
23 Introduction To Autoencoders
24 Autoencoder in TensorFlow
25 Training and Testing a Autoencoder
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