Getting Started with Machine Learning with R
Getting Started with Machine Learning with R
Engaging projects that will teach you how complex data can be exploited to gain the most insight
About This Building Machine Learning Systems with TensorFlow Video course
- Bored with too much theory on TensorFlow? This video training is what you need! Thirteen solid projects and four examples teach you how to implement TensorFlow in production.
- This example-rich guide teaches you how to perform highly accurate and efficient numerical computing with TensorFlow.
- A practical and methodically explained guide that allows you to apply Tensorflow's features from scratch.
Machine Learning wth TensorFlow In Detail
This video course, with the help of practical projects, highlights how TensorFlow can be used in different scenarios—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 will you learn in this course?
Course plan
Chapter 1
Exploring and Transforming Data
Exploring and Transforming Data
Chapter 2
Clustering
Clustering
Chapter 3
Linear Regression
Linear Regression
Chapter 4
Logistic Regression
Logistic Regression
Chapter 5
Simple FeedForward Neural Networks
Simple FeedForward Neural Networks
Chapter 6
Convolutional Neural Networks
Convolutional Neural Networks
Chapter 7
Recurrent Neural Networks and LSTM
Recurrent Neural Networks and LSTM
Detailed course plan
Chapter 1 : Exploring and Transforming Data
33m59
Lesson 1The Course Overview
Lesson 2TensorFlow's Main Data Structure – Tensors
Lesson 3Handling the Computing Workflow – TensorFlow's Data Flow Graph
Lesson 4Basic Tensor Methods
Lesson 5How TensorBoard Works?
Lesson 6Reading Information from Disk
Chapter 2 : Clustering
17m24
Lesson 1Learning from Data –Unsupervised Learning
Lesson 2Mechanics of k-Means
Lesson 3K-Nearest Neighbor
Lesson 4Project 1 – k-Means Clustering on Synthetic Datasets
Lesson 5Project 2 – Nearest Neighbor on Synthetic Datasets
Chapter 3 : Linear Regression
18m30
Lesson 1Univariate Linear Modelling Function
Lesson 2Optimizer Methods in TensorFlow – The Train Module
Lesson 3Univariate Linear Regression
Lesson 4Multivariate Linear Regression
Chapter 4 : Logistic Regression
19m23
Lesson 1Logistic Function Predecessor – The Logit Functions
Lesson 2The Logistic Function
Lesson 3Univariate Logistic Regression
Lesson 4Univariate Logistic Regression with keras
Chapter 5 : Simple FeedForward Neural Networks
16m14
Lesson 1Preliminary Concepts
Lesson 2First Project – Non-Linear Synthetic Function Regression
Lesson 3Second Project – Modeling Cars Fuel Efficiency with Non-Linear Regression
Lesson 4Third Project – Learning to Classify Wines: Multiclass Classification
Chapter 6 : Convolutional Neural Networks
19m33
Lesson 1Origin of Convolutional Neural Networks
Lesson 2Applying Convolution in TensorFlow
Lesson 3Subsampling Operation –Pooling
Lesson 4Improving Efficiency – Dropout Operation
Lesson 5Convolutional Type Layer Building Methods
Lesson 6MNIST Digit Classification
Lesson 7Image Classification with the CIFAR10 Dataset
Chapter 7 : Recurrent Neural Networks and LSTM
20m46
Lesson 1Recurrent Neural Networks
Lesson 2A Fundamental Component – Gate Operation and Its Steps
Lesson 3TensorFlow LSTM Useful Classes and Methods
Lesson 4Univariate Time Series Prediction with Energy Consumption Data
Lesson 5Writing Music "a la" Bach
Chapter 8 : Deep Neural Networks
12m43
Lesson 1Deep Neural Network Definition and Architectures Through Time
Lesson 2Alexnet
Lesson 3Inception V3
Lesson 4Residual Networks (ResNet)
Lesson 5Painting with Style – VGG Style Transfer
Chapter 9 : Library Installation and Additional Tips
05m35
Lesson 1Windows Installation
Lesson 2Mac OS Installation
