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In this Neural Networks Tutorial, we will use Keras AutoTuner To Find The Most Optimal Hyperparameters For A Neural Network. We only need to give it a dataset and it will find the most optimal neural network for that application and dataset. We can then use the model as we have already done in Keras. In the video, I’ll show an example of how to use the Keras autotuner on the Fashion MNIST dataset.

The code example is available on my GitHub: https://github.com/niconielsen32

Subscribe: https://www.youtube.com/channel/UCpABUkWm8xMt5XmGcFb3EFg

#keras

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When constructing a neural network, there are several optimizers available in the Keras API in order to do so.

An **optimizer** is used to minimise the loss of a network by appropriately modifying the weights and learning rate.

For regression-based problems (where the response variable is in numerical format), the most frequently encountered optimizer is the **Adam **optimizer, which uses a stochastic gradient descent method that estimates first-order and second-order moments.

The available optimizers in the Keras API are as follows:

- SGD
- RMSprop
- Adam
- Adadelta
- Adagrad
- Adamax
- Nadam
- Ftrl

The purpose of choosing the most suitable optimiser is not necessarily to achieve the highest accuracy per se — but rather to minimise the training required by the neural network to achieve a given level of accuracy. After all, it is much more efficient if a neural network can be trained to achieve a certain level of accuracy after 10 epochs than after 50, for instance.

#machine-learning #neural-network-algorithm #data-science #keras #tensorflow #neural networks

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In this article, we will discuss regularization and optimization techniques that are used by programmers to build a more robust and generalized neural network. We will study the most effective regularization techniques like L1, L2, Early Stopping, and Drop out which help for model generalization. We will take a deeper look at different optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, AdaGrad, and AdaDelta for better convergence of the neural networks.

Overfitting and underfitting are the most common problems that programmers face while working with deep learning models. A model that is well generalized to data is considered to be an optimal fit for the data. The problem of overfitting occurs when the model captures the noise of data. Precisely, overfitting occurs when a learning model has low bias and high variance. While in the case of underfitting the learning model can’t capture the inherent nature of data. The problem of underfitting persists when the model does not fit well on to the data. The underfitting problem reflects low variance and high bias.

**Regularization**, in the context of neural networks, is a process of preventing a learning model from getting overfitted over training data. It involves a mechanism to reduce generalization errors of the learning model. Look at the following image which shows underfitting, which depicts the inability of a learning model to capture the inherent nature of data. This results in erroneous outcomes for unseen data. Also, we see overfitting over training data in the following image. This image also shows an optimum fit that presents the ability of a learning model to predict correct output for previously not seen data.

#neural-networks #regularization #optimization #artificial-neural-network #machine-learning

1617952323

In this Neural Networks Tutorial, we will use Keras AutoTuner To Find The Most Optimal Hyperparameters For A Neural Network. We only need to give it a dataset and it will find the most optimal neural network for that application and dataset. We can then use the model as we have already done in Keras. In the video, I’ll show an example of how to use the Keras autotuner on the Fashion MNIST dataset.

The code example is available on my GitHub: https://github.com/niconielsen32

Subscribe: https://www.youtube.com/channel/UCpABUkWm8xMt5XmGcFb3EFg

#keras

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Neural networks have been around for a long time, being developed in the 1960s as a way to simulate neural activity for the development of artificial intelligence systems. However, since then they have developed into a useful analytical tool often used in replace of, or in conjunction with, standard statistical models such as regression or classification as they can be used to predict or more a specific output. The main difference, and advantage, in this regard is that neural networks make no initial assumptions as to the form of the relationship or distribution that underlies the data, meaning they can be more flexible and capture non-standard and non-linear relationships between input and output variables, making them incredibly valuable in todays data rich environment.

In this sense, their use has took over the past decade or so, with the fall in costs and increase in ability of general computing power, the rise of large datasets allowing these models to be trained, and the development of frameworks such as TensforFlow and Keras that have allowed people with sufficient hardware (in some cases this is no longer even an requirement through cloud computing), the correct data and an understanding of a given coding language to implement them. This article therefore seeks to be provide a no code introduction to their architecture and how they work so that their implementation and benefits can be better understood.

Firstly, the way these models work is that there is an input layer, one or more hidden layers and an output layer, each of which are connected by layers of synaptic weights¹. The input layer (X) is used to take in scaled values of the input, usually within a standardised range of 0–1. The hidden layers (Z) are then used to define the relationship between the input and output using weights and activation functions. The output layer (Y) then transforms the results from the hidden layers into the predicted values, often also scaled to be within 0–1. The synaptic weights (W) connecting these layers are used in model training to determine the weights assigned to each input and prediction in order to get the best model fit. Visually, this is represented as:

#machine-learning #python #neural-networks #tensorflow #neural-network-algorithm #no code introduction to neural networks

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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