English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 3 Hours | 537 MB
Ready to build the future with Deep Neural Networks? Stand on the shoulder of TensorFlow and Keras for Machine Learning.
This course provides you to be able to build Deep Neural Networks models for different business domains with one of the most common machine learning library TensorFlow provided by Google AI team. The both concept of deep learning and its applications will be mentioned in this course. Also, we will focus on Keras. This course appeals to ones who interested in Machine Learning, Data Science and AI. Also, you don’t have to be attend any ML course before.
What Will I Learn?
- You will be able to build deep learning models for different business domains in TensorFlow
- You can distinguish classification and regression problems, apply supervised learning, and can develop solutions
- You can also apply segmentation analysis through unsupervised learning and clustering
- You can consume TensorFlow via Keras in easier way.
- Finally, you will be informed about tuning machine learning models to produce more successful results
Table of Contents
Introduction
1 Installing Tensorflow and Prerequisites on Windows
2 Jupyter notebook
3 Hello TensorFlow Building Deep Neural Networks Classifier Model
Reusability in TensorFlow
4 Restoring and Working on Already Trained Deep Neural Networks In TensorFlow
5 Importing Saved TensorFlow DNN Classifier Model in Java
Monitoring and Evaluating
6 Monitoring Model Evaluation Metrics in TensorFlow and TensorBoard
Building regression and time series models
7 Building a DNN Regressor for Non-Linear Time Series in TensorFlow
8 Visualizing ML Results with matplotlib and Embedding in TensorBoard
Building Unsupervised Learning Models
9 Unsupervised learning and k-means clustering with TensorFlow
10 Applying k-means clustering to n-dimensional datasets in TensorFlow
Tuning Deep Neural Network Models
11 Optimization Algorithms in TensorFlow
12 Activation Functions in TensorFlow
Consuming TensorFlow via Keras
13 Installing Keras
14 Building DNN Classifier with Keras
15 Storing and restoring a trained neural networks model with Keras
Advanced applications
16 Handwritten Digit Recognition Using Neural Networks
17 Handwritten Digit Recognition Using Convolutional Neural Networks with Keras
18 Transfer Learning Consuming InceptionV3 to Classify Cat and Dog Images in Keras
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