TensorFlow is an open-source machine learning platform that allows you to build and deploy machine learning models. The end-to-end ML platform provides a wide variety of tools, libraries, and resources for both beginner and advanced developers.
💡
We highly recommend that any aspiring Machine Learning Engineers & AI developers learn TensorFlow. It's a powerful ML library and highly-sought after skillset by many businesses.
TensorFlow Capabilities
A few of TensorFlow key capabilities include:
- Easy model building
- Model serving
- Tools & resources for ML in production
- Distributed Computing
- Ability to visualize machine learning models with TensorBoard
- Build quantum machine learning algorithms with TensorFlow Quantum
- And much more...
These are just a few of the capabilities, but there are many more you'll uncover as you start using it.
Applications of TensorFlow
A few of TensorFlow key applications include:
- Image and Speech Recognition: TensorFlow's libraries support machine learning algorithms that are capable of understand images and voice inputs.
- Natural Language Processing (NLP): TensorFlow provides a number of resources for NLP practicioners, and provides tools for tasks including sentiment analysis, machine translation, and language generation.
- Predictive Analytics: TensorFlow's deep learning capabilities also provide the businesses with predictive insights, enabling them to make more data-driven decisions.
In summary, TensorFlow is a powerful and flexible machine learning platform that offers a range of tools and resources for users of all levels.
If you're a beginner looking to get started with machine learning or an experienced developer looking to build and deploy more complex ML models, TensorFlow is a highly valuable resource & skillset to learn.
TensorFlow Resources
Quantum Machine Learning: Introduction to TensorFlow Quantum
In this article, we introduce key concepts of TensorFlow Quantum (TFQ), which is a framework for building near-term quantum machine learning applications.
Peter Foy
Introduction to Natural Language Processing (NLP) with TensorFlow
In this article, we introduce how to use TensorFlow and Keras for natural language processing (NLP).
Peter FoyIntroduction to Sequences and Time Series Forecasting with TensorFlow
In this article, we’ll introduce building time series models with TensorFlow, including best practices for preparing time series data.
Peter FoyRecurrent Neural Networks (RNNs) and LSTMs for Time Series Forecasting
In this article, we review how to use sequence models such as recurrent neural networks (RNNs) and LSTMs for time series forecasting with TensorFlow.
Peter FoyIntroduction to Recommendation Systems with TensorFlow
In this article, we discuss one of the most widely used applications of machine learning in our everyday lives: recommendation systems.
Peter FoyTime Series with TensorFlow: Downloading & Formatting Historical Bitcoin Data
In this article, we’ll start a new time series with TensorFlow project by importing historical Bitcoin data, visualizing it, and preparing it for modeling.
Peter Foy
Time Series with TensorFlow: Building a Naive Forecasting Model
In this article, we discuss the various modeling experiments we’ll be running and then build a naive forecasting model for our Bitcoin price data.
Peter Foy
Time Series with TensorFlow: Common Evaluation Metrics
In this article, we discuss several common evaluation metrics to evaluate our time series forecasting models.
Peter Foy
Time Series with TensorFlow: Formatting Data with Windows & Horizons
In this article, we format our time series data with windows and horizons in order to turn the task of forecasting into a supervised learning problem.
Peter Foy
Time Series with TensorFlow: Building a dense model for Bitcoin price forecasting
In this article, we’re going to create our first deep learning model for time series forecasting with Bitcoin price data.
Peter Foy
Time Series with TensorFlow: Building dense models with larger windows & horizons
In this article, we build two dense models with larger window & horizon sizes.
Peter Foy
Time Series with TensorFlow: Building a Convolutional Neural Network (CNN) for Forecasting
In this Time Series with TensorFlow article, we build a Conv1D (CNN) model for forecasting Bitcoin price data.
Peter Foy
Time Series with TensorFlow: Replicating the N-BEATS Algorithm
In this article, we’ll expand on our previous time series forecasting models and replicate the N-BEATS algorithm, which is a state-of-the-art forecasting algorithm.
Peter Foy
Time Series with TensorFlow: Building an Ensemble Model for Forecasting
In this article, we discuss how to use ensemble learning for the task of time series forecasting and combine their predictions to improve performance.
Peter Foy
Time Series with TensorFlow: Prediction Intervals for Forecasting
In this article, we discuss the concept of prediction intervals, also known as uncertainty estimates, which give a range of prediction values with upper and lower bounds.
Peter Foy
Disclaimer
Please note this pages serves informational purposes only and does not constitute an endorsement of any AI tool. Some of the company descriptions are assisted by our GPT-4 research assistant and is provided without any expressed or implied warranties.
