Remove 2019 Remove Building Remove Deep Learning
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Deep Learning for NLP: ANNs, RNNs and LSTMs explained!

KDnuggets

Learn about Artificial Neural Networks, Deep Learning, Recurrent Neural Networks and LSTMs like never before and use NLP to build a Chatbot!

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Deep Learning for NLP: Creating a Chatbot with Keras!

KDnuggets

Learn how to use Keras to build a Recurrent Neural Network and create a Chatbot! Who doesn’t like a friendly-robotic personal assistant?

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How to Become a Deep Learning Engineer in 2023?

ProjectPro

Deep learning was developed in the early 1940s to mimic the neural networks of the human brain. However, in the last few decades, deep learning has unleashed itself into the world. 85% of data science platform vendors have the first version of deep learning in products. What does a Deep Learning Engineer do?

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Advice on building a machine learning career and reading research papers by Prof. Andrew Ng

KDnuggets

This blog summarizes the career advice/reading research papers lecture in the CS230 Deep learning course by Stanford University on YouTube, and includes advice from Andrew Ng on how to read research papers.

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There is No Such Thing as a Free Lunch: Part 2 – Building an intelligent Digital Assistant

KDnuggets

In this second part we want to outline our own experience building an AI application and reflect on why we chose not to utilise deep learning as the core technology used.

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Data Science vs Artificial Intelligence [Top 10 Differences]

Knowledge Hut

Data Scientists, also touted as the "sexiest job of the 21st century", have seen job postings for it rise by 256% over the year 2019. It is an interdisciplinary science with multiple approaches, and advancements in Machine Learning and deep learning are creating a paradigm shift in many sectors of the IT industry across the globe.

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Three Methods of Data Pre-Processing for Text Classification

KDnuggets

This blog shows how text data representations can be used to build a classifier to predict a developer’s deep learning framework of choice based on the code that they wrote, via examples of TensorFlow and PyTorch projects.

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