by Notez | Released September 2018 | ISBN: 9781789614398

Tensors and TensorFlowAbout This VideoBasic requirements such as mathematics knowledge and usage of computers.Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of machine learning.In DetailInterested in the field of machine learning? Then this course is for you! This course has been designed by experts so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the world of machine learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative field of ML. This course is fun and exciting, but at the same time we dive deep into machine ;earning. We will be covering the following topics in a well crafted way:- Tensors and TensorFlow on the Cloud - what neural networks, machine learning and deep learning really are, how neurons work and how neural networks are trained. - Datalab, linear regressions, placeholders, variables, image processing, MNIST, K- Nearest Neighbors, gradient descent, Softmax and more Show and hide more

1. Chapter 1 : Introduction
   • Introduction 00:01:14
   • Lab 1: Setting up a GCP Account 00:07:26
   • Lab 2: How to use Cloud Shell 00:07:54
2. Chapter 2 : Datalab
   • Let's meet Datalab- Jupyter 00:01:58
3. Chapter 3 : Machine Learning
   • Machine learning - The buzz word 00:06:05
   • The ML block diagram, Deep learning & neural networks 00:03:16
   • Lab: Simple Math in TensorFlow 00:09:48
   • Understanding Tensorflow 00:04:08
   • Tensors 00:01:42
   • Lab: Tensors 00:04:55
   • Linear regression – Introduction 00:03:22
   • Lab: Regression - Simple Example 00:06:16
   • Placeholders and Variables 00:03:20
   • Lab: Simple Math with Placeholders 00:06:08
   • Lab: Dealing With Variables 00:07:57
   • Image processing in TensorFlow 00:04:19
   • Lab: Dealing with Images- 1 00:09:51
   • Image as Tensors 00:04:16
   • Lab: Dealing with Images- 2 00:05:16
   • MNIST - Introduction, K- nearest neighbors algorithm 00:05:18
   • L1 Distance 00:04:36
   • Neural Network in Real Time, Learning regression and XOR 00:05:54
4. Chapter 4 : Regression in Detail
   • Linear regression, Gradient Descent 00:03:45
   • Logistic Regression, Logit 00:04:40
   • Activation function- Softmax, Cost function -Cross entropy, Estimators 00:04:12
   • Lab: Taxidemand – 1 00:12:38
   • Lab: Taxidemand – 2 00:06:28
5. Chapter 5 : More on Gcloud
   • Lab: Creating VM Instance 00:09:19
   • Lab: Editing VM instance 00:05:06
   • Lab: VM Instance via Command Line 00:07:39
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