1598854860

In this video, we will see how you can think of a logistic regression as a neuron. We will use insurance dataset as a sample and build a logistic regression. Logistic regression uses two step process for classification,

step 1: linear regression to find best fit line for given dataset

step 2: sigmoid or logit function to convert this line into values between 0 and 1

Using sigmoid function one can do a classification.

#deep-learning #tensorflow #keras #python

1595422560

Welcome to DataFlair Keras Tutorial. This tutorial will introduce you to everything you need to know to get started with Keras. You will discover the characteristics, features, and various other properties of Keras. This article also explains the different neural network layers and the pre-trained models available in Keras. You will get the idea of how Keras makes it easier to try and experiment with new architectures in neural networks. And how Keras empowers new ideas and its implementation in a faster, efficient way.

Keras is an open-source deep learning framework developed in python. Developers favor Keras because it is user-friendly, modular, and extensible. Keras allows developers for fast experimentation with neural networks.

Keras is a high-level API and uses Tensorflow, Theano, or CNTK as its backend. It provides a very clean and easy way to create deep learning models.

Keras has the following characteristics:

- It is simple to use and consistent. Since we describe models in python, it is easy to code, compact, and easy to debug.
- Keras is based on minimal substructure, it tries to minimize the user actions for common use cases.
- Keras allows us to use multiple backends, provides GPU support on CUDA, and allows us to train models on multiple GPUs.
- It offers a consistent API that provides necessary feedback when an error occurs.
- Using Keras, you can customize the functionalities of your code up to a great extent. Even small customization makes a big change because these functionalities are deeply integrated with the low-level backend.

The following major benefits of using Keras over other deep learning frameworks are:

- The simple API structure of Keras is designed for both new developers and experts.
- The Keras interface is very user friendly and is pretty optimized for general use cases.
- In Keras, you can write custom blocks to extend it.
- Keras is the second most popular deep learning framework after TensorFlow.
- Tensorflow also provides Keras implementation using its tf.keras module. You can access all the functionalities of Keras in TensorFlow using tf.keras.

Before installing TensorFlow, you should have one of its backends. We prefer you to install Tensorflow. Install Tensorflow and Keras using pip python package installer.

The basic data structure of Keras is model, it defines how to organize layers. A simple type of model is the Sequential model, a sequential way of adding layers. For more flexible architecture, Keras provides a Functional API. Functional API allows you to take multiple inputs and produce outputs.

It allows you to define more complex models.

#keras tutorials #introduction to keras #keras models #keras tutorial #layers in keras #why learn keras

1624291780

This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!

⭐️ Contents ⭐

⌨️ (0:00) Introduction

⌨️ (1:45) Installing Python & PyCharm

⌨️ (6:40) Setup & Hello World

⌨️ (10:23) Drawing a Shape

⌨️ (15:06) Variables & Data Types

⌨️ (27:03) Working With Strings

⌨️ (38:18) Working With Numbers

⌨️ (48:26) Getting Input From Users

⌨️ (52:37) Building a Basic Calculator

⌨️ (58:27) Mad Libs Game

⌨️ (1:03:10) Lists

⌨️ (1:10:44) List Functions

⌨️ (1:18:57) Tuples

⌨️ (1:24:15) Functions

⌨️ (1:34:11) Return Statement

⌨️ (1:40:06) If Statements

⌨️ (1:54:07) If Statements & Comparisons

⌨️ (2:00:37) Building a better Calculator

⌨️ (2:07:17) Dictionaries

⌨️ (2:14:13) While Loop

⌨️ (2:20:21) Building a Guessing Game

⌨️ (2:32:44) For Loops

⌨️ (2:41:20) Exponent Function

⌨️ (2:47:13) 2D Lists & Nested Loops

⌨️ (2:52:41) Building a Translator

⌨️ (3:00:18) Comments

⌨️ (3:04:17) Try / Except

⌨️ (3:12:41) Reading Files

⌨️ (3:21:26) Writing to Files

⌨️ (3:28:13) Modules & Pip

⌨️ (3:43:56) Classes & Objects

⌨️ (3:57:37) Building a Multiple Choice Quiz

⌨️ (4:08:28) Object Functions

⌨️ (4:12:37) Inheritance

⌨️ (4:20:43) Python Interpreter

📺 The video in this post was made by freeCodeCamp.org

The origin of the article: https://www.youtube.com/watch?v=rfscVS0vtbw&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3

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Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#python #learn python #learn python for beginners #learn python - full course for beginners [tutorial] #python programmer #concepts in python

1598854860

In this video, we will see how you can think of a logistic regression as a neuron. We will use insurance dataset as a sample and build a logistic regression. Logistic regression uses two step process for classification,

step 1: linear regression to find best fit line for given dataset

step 2: sigmoid or logit function to convert this line into values between 0 and 1

Using sigmoid function one can do a classification.

#deep-learning #tensorflow #keras #python

1599373260

We will go over what is the difference between pytorch, tensorflow and keras in this video. Pytorch and Tensorflow are two most popular deep learning frameworks. Pytorch is by facebook and Tensorflow is by Google. Keras is not a full fledge deep learning framework, it is just a wrapper around Tensorflow that provides some convenient APIs.

#pytorch #tensorflow #keras #python #deep-learning

1598682060

This video explains four reasons why deep learning has become so popular in past few years.

In this deep learning tutorial python, I will cover following things in this video,

- 00:00 Introduction
- 00:24 Data growth
- 01:25 Hardware advancements
- 02:40 Python and opensource ecosystem
- 04:00 Cloud and AI boom

#deep-learning #python #tensorflow #keras