/Neural-Networks-in-Python-Deep-Learning-for-Beginners

Learn Artificial Neural Networks (ANN) in Python. Build predictive deep learning models using Keras & Tensorflow. Data Preprocessing technique

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Neural-Networks-in-Python-Deep-Learning-for-Beginners

We are looking for a complete Artificial Neural Network (ANN) project that teaches you everything you need to create a Neural Network model in Python.

Create Neural network models in Python using Keras and Tensorflow libraries and analyze their results.

Part 1 - Theoretical Concepts (Perceptrons) code link

This part will give you a solid understanding of concepts involved in Neural Networks.

In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

Part 2 - Creating Regression and Classification ANN model in Python code link code link

In this part you will learn how to create ANN models in Python.

We will start this part by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.

We also understand the importance of libraries such as Keras and TensorFlow in this part.

Part 3 - Data Preprocessing code link code link code link

In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.

In this part, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split.

Part 4 - Classic ML technique - Linear Regression code link code link

This section starts with simple linear regression and then covers multiple linear regression.

We have covered the basic theory behind each concept without getting too mathematical about it so that you

understand where the concept is coming from and how it is important. But even if you don't understand

We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem.

depend on course Start-Tech Academy