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

Until now, we have built a model using the pre-trained model as a feature Extractor and connected our own classifier on top of that. This is one of the strategies of transfer learning. Before we move to the fine-tuning, let's try to do some predictions from the `model_new` we built in the previous lesson.

# Make predictions
To make predictions, we would require a sample image to be loaded. Here, we have taken a Pikachu image; and let's see what output we will get.

from keras.applications.resnet50 import preprocess_input

image_path = 'pikachu.png'
img = image.load_img(image_path,target_size = (224,224))
x = image.img_to_array(img)
x = np.expand_dims(x,axis=0)
x = preprocess_input(x)

pred = model_new.predict(x)
print(np.argmax(pred))

**Explanation:**

* This is the same code we used in the lesson *Use a Pre-Trained Model*; the only difference is that we cannot use the `decode_predictions` because we added our own classifier instead of using the ResNet50 classifier.

* The output would be: 
  ```
  9
  ```
  As you can see from the mapping, `9` belonged to the class `Pikachu`, which means our classifier is working pretty well.


In this lesson, we will perform some fine-tuning of the model and observe the accuracy of our model.

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 Three

Make predictions