1654052700

This is one of my neural network projects projects. In this tutorial we will cover a full process of building a neural network model to classify objects in images. Follow this tutorial and you can learn how to build your very own object model with TensorFlow.

In this deep learning tutorial I will walk you through the process and code in order to setup your own neural network , the layers , and give you the basic tools to build your own model.

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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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Hi,

This is a tutorial for building a neural network for classifying between several fruits and vegetables.

The tutorial is a coding step by step , from downloading and organizing the dataset images , planning the CNN network , and running the code.

The link for the video : https://youtu.be/w5T86Z3lod0

You can find more similar CNN video tutorials in my channel.

Enjoy

Eran

#Python #openCV #TensorFlow

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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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When discussing neural networks, most beginning textbooks create brain analogies. I can define the new neural networks simply as a mathematical function that translates a certain entry to the desired performance without going into brain analogies.

You may note that the weights W and biases b are the only variables in the equation above affecting the output of a given value. The strength of predictions naturally establishes the correct values for weights and biases. The weight and bias adjustment procedure of the input data is known as neural network training.

#neural-networks #artificial-intelligence #python #programming #technology #how to build your own neural network from scratch in python

1654052700

This is one of my neural network projects projects. In this tutorial we will cover a full process of building a neural network model to classify objects in images. Follow this tutorial and you can learn how to build your very own object model with TensorFlow.

In this deep learning tutorial I will walk you through the process and code in order to setup your own neural network , the layers , and give you the basic tools to build your own model.