Machine Learning with TensorFlow on Google Cloud

سرفصل های دوره

Build, train, and deploy ML models with TensorFlow: A hands-on journey through Google Cloud's powerful infrastructure

1. Introduction

1. Introduction

2. Basics of Machine Learning

1. Linear regression basics

2. Logistic regression basics

3. Perceptron - Introduction to neural network

1. Single Neural Cell

2. Example of a Perceptron

3. What are Activation Functions

4. Sigmoid Activation Function

5. Linear regression case study

6. Linear regression case study - demonstration

7. Logistic regression case study

8. Logistic regression case study - demonstration

4. Artificial neural network

1. Parallel vs Sequential Stacking

2. Important terms

3. How Neural Networks work

4. Finding the optima using Gradient Descent

5. Concept Behind Using Gradient Descent

6. Types and Uses of Activation Functions

7. Multiclass Classification

8. Difference Between Gradient Descent and Stochastic Gradient Descent

9. Epochs

5. Creating arctificial neural network on Google Colab

1. Dataset for classification

2. Normalization and Test-Train split

3. Different ways to create ANN

4. Building the Neural Network

5. Compiling and Training the Neural Network model

6. Evaluating performance and Predicting

7. Building Neural Network for Regression Problem

8. Complex ANN Architectures using Functional API

9. Understanding Checkpoints and Callbacks in Keras.html

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تولید کننده: Udemy