by Nick McClure | Released March 2018 | ISBN: 9781789343403

Explore high-level concepts such as neural networks, CNN and RNN using TensorFlow.About This VideoDevelop a strong background in neural network programming from scratch, using the popular Tensorflow library.Use Tensorflow to implement different kinds of neural networks – from simple feedforward neural networks to multilayered perceptrons, CNNs, RNNs and moreA highly practical guide including real-world datasets and use-cases to simplify your understanding of neural networks and their implementation.In DetailTensorFlow is an open source software library for Machine Intelligence. The independent solutions in this video course will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through video on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow.This guide covers important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last section will show you how to take it to production. Once you are familiar and comfortable with the TensorFlow ecosystem, the last section will show you how to take it to production.All the code and supporting files for this course are available on Github at: https://github.com/PacktPublishing/TensorFlow-for-Neural-Network-Solutions Show and hide more

1. Chapter 1 : Neural Networks
   • The Course Overview 00:01:55
   • Implementing Operational Gates 00:03:52
   • Working with Gates and Activation Functions 00:03:07
   • Implementing a One-Layer Neural Network 00:02:44
   • Implementing Different Layers 00:04:21
   • Learning to Play Tic-Tac-Toe 00:04:47
2. Chapter 2 : Natural Language Processing
   • Working with Bag-of-Words 00:06:08
   • Implementing TF-IDF 00:04:34
   • Working with Skip-Gram Embeddings 00:06:07
   • Working with CBOW Embeddings 00:04:00
3. Chapter 3 : Convolutional Neural Networks
   • Implementing a Simpler CNN 00:03:40
   • Implementing an Advanced CNN 00:04:30
   • Applying Stylenet/Neural-Style 00:04:58
4. Chapter 4 : Recurrent Neural Networks
   • Implementing RNN for Spam Prediction 00:04:45
   • Implementing an LSTM Model 00:03:20
   • Stacking Multiple LSTM Layers 00:02:13
   • Training a Siamese Similarity Measure 00:03:41
5. Chapter 5 : Taking TensorFlow to Production
   • Implementing Unit Tests 00:02:57
   • Using Multiple Executors 00:02:54
   • Parallelizing TensorFlow 00:01:28
   • Production Tips for TensorFlow 00:02:32
   • Productionalizing TensorFlow – An Example 00:02:14
6. Chapter 6 : More with TensorFlow
   • Visualizing Graphs in TensorBoard 00:03:53
   • Working with a Genetic Algorithm 00:04:21
   • Clustering Using K-Means 00:02:50
   • Solving a System of ODEs 00:02:30
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