1612871100

Classification allows deep neural networks to predict values that are one of a set number of classes. This video also shows common methods for evaluating Keras classification models, such as AUC, ROC, confusion matrix, and Accuracy.

Code for This Video: http://bit.ly/3rmcqn2

Subscribe: https://www.youtube.com/channel/UCR1-GEpyOPzT2AO4D_eifdw

#keras #deep-learning

1612871100

Classification allows deep neural networks to predict values that are one of a set number of classes. This video also shows common methods for evaluating Keras classification models, such as AUC, ROC, confusion matrix, and Accuracy.

Code for This Video: http://bit.ly/3rmcqn2

Subscribe: https://www.youtube.com/channel/UCR1-GEpyOPzT2AO4D_eifdw

#keras #deep-learning

1621440840

In deep learning with Keras, you don’t have to code a lot, but there are a few steps on which you need to step over slowly so that in the near future, you can create your models. The flow of modelling is to load data, define the Keras model, compile the Keras model, fit the Keras model, evaluate it, tie everything together, and make the predictions out of it.

But at times, you might find it confusing because of not having a good hold on the fundamentals of deep learning. Before starting your new deep learning with Keras project, make sure to go through this ultimate guide which will help you in revising the fundamentals of deep learning with Keras.

In the field of Artificial Intelligence, deep learning has become a buzzword which always finds its way in various conversations. When it comes to imparting intelligence to the machines, it has been since many years that we used Machine Learning (ML).

But, considering the current period, due to its supremacy in predictions, deep learning with Keras has become more liked and famous as compared to the old and traditional ML techniques.

Machine learning has a subset in which the Artificial Neural Networks (ANN) is trained with a large amount of data. This subset is nothing but deep learning. Since a deep learning algorithm learns from experience, it performs the task repeatedly; every time it tweaks it a little intending to improve the outcome.

It is termed as ‘deep learning’ because the neural networks have many deep layers which enables learning. Deep learning can solve any problem in which thinking is required to figure out the problem.

There are many APIs, frameworks, and libraries available to get started with deep learning. But here’s why deep learning with Keras is beneficial. Keras is a high-level neural network application programming interface (API) which runs on the top of TensorFlow – which is an end-to-end machine learning platform and is an open-source. Not just Tensorflow, but also CNTK, Theano, PlaidML, etc.

It helps in commoditizing artificial intelligence (AI) and deep learning. The coding in Keras is portable, it means that using Keras you can implement a neural network while using Theano as a backend and then subsequently run it on Tensorflow by specifying the backend. Also further, it is not mandatory rather, not needed at all to change the code.

If you are wondering why deep learning is an important term in Artificial Intelligence or if you are lagging motivation to start learning deep learning with Keras, this google trends snap shows how people’s interest in deep learning has been growing steadily worldwide for the last few years.

#deep learning #deep learning with neural network #neural network

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There has been hype about artificial intelligence, machine learning, and neural networks for quite a while now. I have been working on these things for over a year now so I would like to share some of my knowledge and give my point of view on Neural networks. This will not be a math-heavy introduction because I just want to build the idea here.

I will start from the neural network and then I will explain every component of a neural network. If you feel like something is not right or need any help with any of this, Feel free to contact **me**, I will be happy to help.

Let’s assume we want to solve a problem where you are given some set of images and you have to build an automated system that can categories each of those images to its correct label.

The problem looks simple but how do we come with some logic using raw pixel values and target labels. We can try comparing pixels and edges but we won’t be able to come with some idea which can do this task effectively or say the accuracy of 90% or more.

When we have this kind of problem where we have high dimensional data like Images and we don’t know the relationship between Input(*Images*) and the Output(*Labels*), In this kind of scenario we should use Neural Networks.ư

Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain

#artificial-intelligence #gradient-descent #artificial-neural-network #deep-learning #neural-networks #deep learning

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Welcome to Part 2 of the Neural Network series! In Part 1, we worked our way through an Artificial Neural Network (ANNs) using the Keras API. We talked about Sequential network architecture, activation functions, hidden layers, neurons, etc. and finally wrapped it all up in an end-to-end example that predicted whether loan application would be approved or rejected.

In this tutorial, we will be learning how to create a Convolutional Neural Network (CNN) using the Keras API. To make it more intuitive, I will be explaining what each layer of this network does and provide tips and tricks to ease your deep learning journey. Our aim in this tutorial is to build a basic CNN that can classify images of chest Xrays and establish if it is normal or has pneumonia. Given the Covid-19 pandemic, I think this would make for an interesting project even for your data science interviews!

Let’s get started!

CNNs work best when the data can be represented in a spatial manner, say an image in MxN pixels. If you data is just as useful after shuffling any of your columns with each other then youuse CNN.cannot

For instance, if you recall the loan application dataset from Part 1, it had two columns (or features), namely `age`

and `area`

, and if I were to swap the two columns (before feeding it to my network) it would make no difference whatsoever to my dataset. Hence, ANNs are preferred for such datasets. On the contrary, if I were to swap the columns (which are essentially pixel arrays) in my image, I am surely going to mess up my actual image. Hence, using ANNs is a big no-no and you must use CNNs.

We begin by installing Keras onto our machine. As I discussed in Part 1, Keras is integrated within TensorFlow, so all you have to do is `pip install tensorflow`

in your terminal (for Mac OS) to access Keras in your Jupyter notebook. To check the version of Tensorflow, use `tf.__version__`

.

```
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Dense, Flatten, BatchNormalization, Conv2D, MaxPool2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import categorical_crossentropy
from sklearn.metrics import confusion_matrix
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import itertools
import os
import random
import matplotlib.pyplot as plt
%matplotlib inline
```

#deep-learning #python #image-classification #neural-networks #keras

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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