# DatosConsejos simples para el anÃ¡lisis de datos

Explora en detalle Pandas,nacido en el mar, Yellowbrick, Plotly y Shap, Aprenda cÃ³mo hacer hermosos grÃ¡ficos y cÃ³mo extraer informaciÃ³n de su anÃ¡lisis de datos. Un analista de datos necesita proporcionar informaciÃ³n a los socios comerciales y a un ingeniero de aprendizaje automÃ¡tico. Los conocimientos necesarios pueden ser muy diferentes y la comprensiÃ³n de los datos se utilizarÃ¡ de diferentes maneras. Perfeccionemos nuestras habilidades de anÃ¡lisis de datos de Python y parcelas connacido en el mar, Pandas, Plotly y Shap.

## Consejos para el anÃ¡lisis de datos de Seaborn en Python

SumÃ©rjase en el anÃ¡lisis de datos con Seaborn. La biblioteca de Python crea hermosos grÃ¡ficos pero tambiÃ©n mejora la capacidad de extraer informaciÃ³n de su anÃ¡lisis de datos. repasar consejos desde principiante hasta avanzado sobre cÃ³mo aprovechar al mÃ¡ximo su anÃ¡lisis de datos de Python en seaborn.

## Preprocessing

In this free how-to section, we will cover how to use TensorFlow to preprocess our data. You might be familiar with many techniques available in Pandas and DataFrame. TensorFlow has many of the same utility functions for preprocessing our data to assist our deep learning model. There are classic preprocessing functions that can be used on tabular data. There are also many many functions available for preprocessing of text data for NLP problems and image data for computer vision problems.

TensorFlow requires us to pay attention to how we put out categorical classification target into our model. The to_categorical function in TensorFlow works in a similar way to the get_dummies function in Pandas. And on a DataFrame can be used as a substitute. Hot one encoding with TensorFlow requires use to give integers as our class. If we use an sklearn preprocessing LabelEncoder and can then return these integers back into the original classes.

TensorFlow is a powerful framework for building neural networks. With TensorFlow we can build deep-learning models that predict either a continuous variable or a categorical value.

We have the choice of either using Sparse Categorical Crossentropy or Categorical Crossentropy.

Inside our model, it wouldn't make a difference for training only how we put the data into our model.

## Deep Learning Architecture

The architecture of your deep learning model is no simple thing. The reason is we have so so much flexiblity in how we design our deep learning architecture that it's help to understand popular and famous neural network architecture. In Python with Tensorflow learning how to build neural networks with the Sequential and Functional APIs.

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Whether you're a beginner or a seasoned data scientist, understanding these two paradigms will equip you with the skills to create, train, and deploy deep learning models for a wide range of applications. So, let's embark on this exciting journey of learning and discover how to harness the true potential of TensorFlow through practical examples using both the Sequential and Functional APIs.

## Sequential API

The perceptron model was the first and simplest type of neural network developed back in 1943. It works well but certainly far from modern nueral networks with advanced layers such as the recurrent layer. In Python using Tensorflow with the sequential api the the build a nueral network in the style of a perceptron.

In Python using Tensorflow design a Feed Forward neural network to predict a regression problem expand on the perceptron and by adding more dense fully connected layers. You will find that only adding more Dense fully connected layers doesn't unlock the true power of neural networks.

In Python use Tensorflow to build a deep forward deep learning architecture. The problem with such a long network is finding the correct hyperparameters that will make it work. So many dense layers has the potential problem of confusing our network and it won't be able to make good predictions.

A recurrent neural network (RNN) is a type of artificial neural network designed to process sequential data by considering the previous information along with the current input. Unlike feedforward neural networks, which process data in a single forward pass, RNNs have a feedback mechanism that allows them to maintain an internal memory or state. This memory enables RNNs to retain information about the sequence they have processed so far, making them well-suited for tasks that involve time-series data or sequences of varying lengths. We will cover the SimpleRNN, GRU and LSTM recurrent layers.

A convolutional network in TensorFlow's sequential model is a specialized type of neural network commonly used for image analysis tasks. It utilizes convolutional layers to detect patterns and features in images, which are then passed through activation functions to capture nonlinear relationships. The sequential model in TensorFlow provides a convenient and intuitive way to stack these layers sequentially, allowing for efficient training and inference on image data.

## Functional Api

The Functional API in TensorFlow offers a flexible and dynamic approach to building neural networks, enabling non-linear architectures with multiple inputs and outputs, shared layers, and skip connections. Its explicit data flow and named layers enhance model readability and maintainability, fostering collaboration and ease of modification. Moreover, the API promotes code reuse and modularity, saving time and ensuring consistency across experiments. It aligns with functional programming principles, allowing for the implementation of custom loss functions, layers, and training loops, empowering researchers and developers to explore innovative ideas. With its versatility and intuitiveness, the Functional API stands as a powerful choice for constructing diverse neural network models and efficiently addressing a wide range of machine learning tasks.

## Hugging Faces Tips

Hugging Faces which is the company behind the transformers library gives you access to many prebuilt transformer models like DistilBert, GPT2, BERT, RoFormer, and Electra and many many more. The Hugging Faces, transformers, library can be implemented with either TensorFlow or PyTorch. Hugging Faces has prebuilt models for NLP problems such as text classification and text generation and questioning and answering. Transformers also give use access to models built for computer vision classification or image generation tasks as with as audio classification. Let's explore how to implement transformer models with Hugging Faces.