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

# Sampling the text
We already created our Markov model in the previous lesson. Now we need to sample the text and generate some new text based on this sampling.


def sample_next(ctx,model,k):

    ctx = ctx[-k:]
    if model.get(ctx) is None:
        return " "
    possible_Chars = list(model[ctx].keys())
    possible_values = list(model[ctx].values())
    
    print(possible_Chars)
    print(possible_values)

    return np.random.choice(possible_Chars,p=possible_values)

sample_next("commo",model,4)

**Explanation:**
* The function, `sample_next(ctx,model,k)`, accepts three parameters: the context, the model, and the value of `K`.
* The `context` is nothing but the text that will be used to generate some new text. However, only the last `K` characters from the context will be used by the model to predict the next character in the sequence.
* For example, we passed the value of context as `commo` and value of `K = 4`, so the context, which the model will look to generate the next character, is of K characters long and hence, it will be `ommo` because the Markov models only take the previous history. You can see the value of the context variable by printing it too.

* On *line 9 and 10*, we printed the possible characters and their probability values, which are also present in our model. We got the next predicted character as `n`, and its probability is `1`. It makes sense because the word `commo` is more likely to be `common` after generating the next character.

* On *line 12*, we returned a sampled character according to the probabilistic values as we discussed above.

* Run the code cell above to see the output. It prints the possible next character and the probability value for that character.


We will use this sampling function to sample our original text.

In this lesson, we will sample our text and generate some new text !

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

Sampling the text