On this path, you’ll learn all about deep learning, including how to build, train, and evaluate models with the TensorFlow framework.
You’ll then learn how to conduct forecasts on real data by applying sequential neural network models to time series forecasting.
Next, you’ll learn how to use TensorFlow tools and libraries to work on a range of NLP use cases, including text visualization, sentiment analysis models, and more.
Finally, you’ll learn how to apply convolutional neural networks (CNNs) to computer vision tasks so that you can teach computers to see and interpret digital images.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. You’ll apply your skills to several guided projects with realistic business scenarios to build your portfolio and prepare for your next interview.
- Building and evaluating deep learning regression models using TensorFlow’s Sequential and Functional APIs in Keras
- Implementing a time series forecast model using LSTM and GRU
- Building and evaluating a deep learning sentiment classification model using Transformers.
- Building and training a CNN for image classification tasks
Part 1: Deep Learning in TensorFlow [4 courses]
- Explain the major concepts and terminology used in deep learning
- Perform data preprocessing and exploratory analysis to prepare data for modeling
- Build and evaluate deep learning regression models using TensorFlow’s Sequential and Functional APIs in Keras
- Build a deep neural network to predict listing gains of IPOs on the Indian market
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Projects in this path
Guided Project: Predicting Listing Gains in the Indian IPO Market Using TensorFlow
Build a deep learning model to predict the listing gains of IPOs in the Indian market.
Guided Project: Time-Series Forecasting on the S&P 500
Build, train, and evaluate an LSTM model with a convolutional layer for S&P 500 index (stock) price prediction.
Guided Project: Classifying Disaster-Related Tweets as Real or Fake
Build a deep learning text classification model to predict whether a given tweet is about a real disaster or not.