Gain insights into advanced deep learning and NLP by building 12 real-world projects using tools like TensorFlow and scikit-learn. Enhance your portfolio with industry-relevant skills.

dockerfile.tar.gz

python3

spa-job

In this course, you'll not only learn advanced deep learning concepts, but you'll also practice building some advanced deep learning and Natural Language Processing (NLP) projects. By the end, you will be able to utilize deep learning algorithms that are used at large in industry.

 

This is a project-based course with 12 projects in total. This will get you used to building real-world applications that are being used in a wide range of industries. You will be exposed to the most common tools used for machine learning projects including: NumPy, Matplotlib, scikit-learn, Tensorflow, and more.

 

It’s recommended that you have a firm grasp in these topic areas: Python basics, Numpy and Pandas, and Artificial Neural Networks.

 

Once you’re finished, you will have the experience to start building your own amazing projects, and some great new additions to your portfolio.

Building Advanced Deep Learning and NLP Projects

# Introduction to the project
Welcome to our first project in the course.

The project is divided into two lessons for better understanding. In this section, we will set up all the resources, the dataset, and the preprocessing of the dataset so that it becomes compatible with our ConvNet Model, *ResNet50*.

Before starting the project, please read the guidelines below so that it will be easy to work along with the course:
* Download the dataset [here](https://www.dropbox.com/sh/frb9uzc6eiwqlco/AABvu06Cbj497NmkCu_oW6bSa?dl=1)
* The dataset contains 10 different Pokemons. But if you want to build a model on a larger dataset, you can get the download from [here](https://www.kaggle.com/thedagger/pokemon-generation-one). It contains around 150 types of Pokemons.
* If you do not have a nicely configured CPU, then we suggest using Google Colab for building the model. 
* The following packages are used:
   * `keras`
   * `numpy`
   * `matplotlib`


You are now ready to start the project.

# Creating the dataset
The first step is to create the dataset. We will create a list, `image_data` that contains the images in a `numpy` array format. We will also convert the image labels to `One Hot Encodings`.

> *One hot encoding: it produces a vector with the length equal to the number of categories in the data set. If a data point belongs to the $i^{th}$ category, then components of this vector are assigned the value 0 except for the $i^{th}$ component, which is assigned a value of 1.*

In this lesson, we will start our project to classify different Pokemons.

Welcome to the Course

Project: Build a COVID-19 Detection System Using X-Rays

Project: Building a Pokemon Classifier Using Transfer Learning

Project: Text Generation Using Markov Chains

Word Embedding: Two Mini Projects

Project: IMDB Reviews Sentiment Analysis

Project: Deciphering Text Using Character-Level RNNs

Project: Emoji Predictor Using Transfer Learning in NLP

Final Exam

Where to Go Next?

Project Creation: Part One

Introduction to the project

Creating the dataset