by Md. Rezaul Karim | Released August 2018 | ISBN: 9781789615494

Accomplish the power of data in your business by building advanced predictive modelling applications with TensorflowAbout This VideoA quick guide to gaining hands-on experience with deep learning in different domains such as digit/image classification and textBuild your own smart, predictive models with TensorFlow using an easy-to-follow approachUnderstand deep learning and predictive analytics along with its challenges and best practicesIn DetailPredictive analytics discovers hidden patterns in structured and unstructured data for automated decision-making in business intelligence. This course will help you build, tune, and deploy predictive models with TensorFlow in three main divisions. The first division covers linear algebra, statistics, and probability theory for predictive modeling. The second division covers developing predictive models via supervised (classification and regression) and unsupervised (clustering) algorithms. It then explains how to develop predictive models for NLP and covers reinforcement learning algorithms. Lastly, this division covers developing a factorization machine-based recommendation system. The third division covers deep learning architectures for advanced predictive analytics, including deep neural networks and recurrent neural networks for high-dimensional and sequence data. Finally, you'll use convolutional neural networks for predictive modeling for emotion recognition, image classification, and sentiment analysis. Show and hide more

1. Chapter 1 : Basic Python and Linear Algebra for Predictive Analytics
   • The Course Overview 00:07:02
   • A Basic Introduction to Predictive Analytics 00:07:57
   • Installing Python in Windows 00:06:57
   • Vectors, Matrices, and Graphs 00:05:11
2. Chapter 2 : Statistics, Probability, and Information Theory for Predictive Modeling
   • Using Statistics in Predictive Modeling 00:13:21
   • Basic Probability for Predictive Modeling 00:05:58
   • Using Information Theory in Predictive Modeling 00:05:45
3. Chapter 3 : From Data to Decisions – Getting Started with TensorFlow
   • Taking Decisions Based on Data – Titanic Example 00:05:38
   • TensorFlow Computational Graph 00:04:34
   • TensorFlow Programming Model 00:03:40
   • Data Model in TensorFlow 00:06:06
   • Getting Started with Tensorflow – Linear Regression and Beyond 00:04:07
4. Chapter 4 : Putting Data in Place – Supervised Learning for Predictive Analytics
   • Supervised Learning for Predictive Analytics 00:09:57
   • From Disaster to Decision –Titanic Example Revisited 00:14:33
5. Chapter 5 : Clustering Your Data – Unsupervised Learning for Predictive Analytics
   • Using K-means for Predictive Analytics 00:10:45
   • Using kNN for Predictive Analytics 00:08:07
6. Chapter 6 : Predictive Analytics Pipelines for NLP
   • NLP Analytics Pipelines 00:04:18
   • Transformers and Estimators 00:04:14
   • Using BOW for Predictive Analytics 00:11:37
   • TF-IDF Model for Predictive analytics 00:08:07
   • Using Word2vec for Sentiment Analysis 00:09:44
7. Chapter 7 : Using Deep Neural Networks for Predictive Analytics
   • Deep Learning for Better Predictive Analytics 00:10:24
   • Fine-tuning DNN Hyperparameters 00:07:00
   • Using Multilayer Perceptrons for Predictive Analytics 00:11:51
   • Deep Belief Networks 00:03:51
8. Chapter 8 : Using Convolutional Neural Networks for Predictive Analytics
   • CNNs and the Drawbacks of Regular DNNs 00:05:35
   • Pooling Layer and Padding Operations 00:04:11
   • Tuning CNN Hyperparameters 00:03:57
   • CNN-based Predictive Model for Sentiment Analysis 00:08:43
   • CNN Model for Emotion Recognition 00:09:50
   • CNN Predictive Model for Image Classification 00:13:43
9. Chapter 9 : Using Recurrent Neural Networks for Predictive Analytics
   • Using BRNN for Image Classification 00:07:52
   • Implementing an RNN for Spam Prediction 00:03:39
   • Developing a Predictive Model for Time Series Data 00:04:24
   • An LSTM Predictive Model for Sentiment Analysis 00:15:40
10. Chapter 10 : Recommendation Systems for Predictive Analytics
    • Recommendation Systems 00:21:31
    • Factorization Machines for Recommendation Systems 00:06:22
    • Improved Factorization Machines for Predictive Analytics 00:05:11
11. Chapter 11 : Using Reinforcement Learning for Predictive Analytics
    • Reinforcement Learning 00:05:02
    • Developing a Multiarmed Bandit's Predictive Model 00:10:03
    • Developing a Stock Price Predictive Model 00:04:48
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