Deep learning methods are achieving state-of-the-art results to challenging machine learning problems, such as identifying and describing photos and translating text from one language to another. In Deep Learning with Python training course, we'll cut through the excess math, research papers, and patchwork descriptions to dive deep into the technology so you gain real-world skills you can immediately leverage on the job.
Using clear explanations and standard Python libraries, you will explore a step-by-step of what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models.
By attending Deep Learning with Python workshop, delegates will learn:
- Neural Text Classification: Develop a deep learning model to classify the sentiment of movie reviews as either positive or negative.
- Neural Language Modeling: Develop a neural language model on the text of Plato in order to generate new tracts of text with the same style and flavor as the original.
- Neural Photo Captioning: Develop a model to automatically generate a concise description of ad hoc photographs.
- Neural Machine Translation: Develop a model to translate sentences of text in German to English.
- Neural Bag-of-Words: Develop neural network models that model text as a bag-of-words where word order is ignored.
- Neural Word Embedding: Develop neural network models that model text using a distributed representation.
- Embedding + CNN: Develop deep learning models that combine word embedding representations with convolutional neural networks.
- Encoder-Decoder RNN: Develop recurrent neural networks that use the encoder-decoder architecture.
- Strong Python skills
- Prior working experience with Keras is useful
- Ability to navigate the Linux command line
- Basic knowledge of Linux editors (such as VI/nano) for editing code
- Experienced Developers, Data Scientist, Data Engineer.