TensorFlow for Neural Network Solutions

TensorFlow for Neural Network Solutions
TensorFlow for Neural Network Solutions
English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 39m | 346 MB

Explore high-level concepts such as neural networks, CNN and RNN using TensorFlow.

TensorFlow 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.

This video course takes a solution-based approach where every topic is explicated with the help of a real-world example.

What You Will Learn

  • Implement neural networks and improve predictions
  • Apply NLP and sentiment analysis to your data
  • Master CNN and RNN through practical videos
  • Take TensorFlow into production
Table of Contents

Neural Networks
1 The Course Overview
2 Implementing Operational Gates
3 Working with Gates and Activation Functions
4 Implementing a One-Layer Neural Network
5 Implementing Different Layers
6 Learning to Play Tic-Tac-Toe

Natural Language Processing
7 Working with Bag-of-Words
8 Implementing TF-IDF
9 Working with Skip-Gram Embeddings
10 Working with CBOW Embeddings

Convolutional Neural Networks
11 Implementing a Simpler CNN
12 Implementing an Advanced CNN
13 Applying Stylenet_Neural-Style

Recurrent Neural Networks
14 Implementing RNN for Spam Prediction
15 Implementing an LSTM Model
16 Stacking Multiple LSTM Layers
17 Training a Siamese Similarity Measure

Taking TensorFlow to Production
18 Implementing Unit Tests
19 Using Multiple Executors
20 Parallelizing TensorFlow
21 Production Tips for TensorFlow
22 Productionalizing TensorFlow – An Example

More with TensorFlow
23 Visualizing Graphs in TensorBoard
24 Working with a Genetic Algorithm
25 Clustering Using K-Means
26 Solving a System of ODEs