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

## Forward propagation
This is the process of calculating and storing the intermediate variables (including outputs) for the neural network in order from the input layer to the output layer. We will not discuss this in too much detail, as it is assumed you already have some background knowledge on this.

A simple architecture of an RNN is shown below:

# Forward propagation
This is the process of calculating and storing the intermediate variables (including outputs) for the neural network in order from the input layer to the output layer. We will not discuss this in too much detail, as it is assumed you already have some background knowledge on this.

A simple architecture of an RNN is shown below:

In this lesson, we will discuss how the forward and backward weight updates take place in an RNN.

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?

Forward and Backward Propagation in RNNs

Forward propagation