English | MP4 | AVC 1920Ă—1080 | AAC 44KHz 2ch | 2h 44m | 606 MB

Engaging projects that will teach you how complex data can be exploited to gain the most insight

This video, with the help of practical projects, highlights how TensorFlow can be used in different scenariosâ€”this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with tensors. Simply pick a project in line with your environment and get stacks of information on how to implement TensorFlow in production.

What You Will Learn

- Load, interact, dissect, process, and save complex datasets
- Solve classification and regression problems using state-of-the-art techniques
- Predict the outcome of a simple time series using Linear Regression modeling
- Use a Logistic Regression scheme to predict the future result of a time series
- Classify images using deep neural network schemes
- Tag a set of images and detect features using a deep neural network, including a Convolutional Neural Network (CNN) layer
- Resolve character-recognition problems using the Recurrent Neural Network (RNN) model

## Table of Contents

01 The Course Overview

02 TensorFlow-s Main Data Structure Tensors

03 Handling the Computing Workflow TensorFlow-s Data Flow Graph

04 Basic Tensor Methods

05 How TensorBoard Works

06 Reading Information from Disk

07 Learning from Data Unsupervised Learning

08 Mechanics of k-Means

09 k-Nearest Neighbor

10 Project 1 k-Means Clustering on Synthetic Datasets

11 Project 2 Nearest Neighbor on Synthetic Datasets

12 Univariate Linear Modelling Function

13 Optimizer Methods in TensorFlow The Train Module

14 Univariate Linear Regression

15 Multivariate Linear Regression

16 Logistic Function Predecessor The Logit Functions

17 The Logistic Function

18 Univariate Logistic Regression

19 Univariate Logistic Regression with keras

20 Preliminary Concepts

21 First Project Non-Linear Synthetic Function Regression

22 Second Project Modeling Cars Fuel Efficiency with Non-Linear Regression

23 Third Project Learning to Classify Wines- Multiclass Classification

24 Origin of Convolutional Neural Networks

25 Applying Convolution in TensorFlow

26 Subsampling Operation Pooling

27 Improving Efficiency Dropout Operation

28 Convolutional Type Layer Building Methods

29 MNIST Digit Classification

30 Image Classification with the CIFAR10 Dataset

31 Recurrent Neural Networks

32 A Fundamental Component Gate Operation and Its Steps

33 TensorFlow LSTM Useful Classes and Methods

34 Univariate Time Series Prediction with Energy Consumption Data

35 Writing Music a la Bach

36 Deep Neural Network Definition and Architectures Through Time

37 Alexnet

38 Inception V3

39 Residual Networks (ResNet)

40 Painting with Style VGG Style Transfer

41 Windows Installation

42 mac OS Installation

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