Bootcamp

Zero to Deep Learning® 1 Week Bootcamp

San francisco Feb 25 - Mar 3

Curriculum

Data Visualization, Machine Learning, and Deep Learning. These are the elements that come together to make driverless cars, to recognize faces, to market products, and to drive big decisions from big data.

    You will build:

    • A language detector
    • An image recognition engine
    • A sentiment classifier that can decipher the tone of text
    • A forecasting algorithm that predicts future energy consumption

    Updated to the very latest training curriculum, you will learn to use these Python libraries:

    • Pandas (data manipulation)
    • Matplotlib & Seaborn (data visualization)
    • Scikit-Learn (machine learning)
    • Keras (deep learning)
    • Tensorflow (deep learning)

    Weekend Courses

    Data Analytics with Python, SQL & Spark

    San francisco Apr 7-8, Apr 21-22, May 5-6, May 19-20, jun 2-3

    Curriculum

    This workshop will introduce you to essential concepts and practices for building compelling analyses and dashboards on datasets of any size. You will learn how to:

    • Use Python and Pandas to select, group and summarize your data
    • Decide what data to keep and what to ignore
    • Create compelling visualizations using Seaborn and Matplotlib
    • Connect and retrieve data from a database using Python
    • Extend your analyses to relational databases using SQL
    • Perform aggregations and combinations using SQL
    • Include unstructured data sources in your analysis using Spark
    • Scale up your analyses to Gb of data using Spark on AWS
    • Combine Spark and SQL for maximum flexibility and power

    Machine Learning with Python, Pandas & Scikit-Learn

    San francisco Apr 21-22

    Curriculum

    The workshop is meant to provide you with a solid base to build your machine learning skills. In particular you will learn to:

    • Recognize problems that can be solved with Machine Learning
    • Select the right technique (is it a classification problem? a regression? needs preprocessing?)
    • Load and manipulate data with Pandas
    • Visualize and explore data with Matplotlib and Bokeh
    • Build regression, classification and clustering models with Scikit-Learn
    • Evaluate model performance with Scikit-Learn
    • Build, train and serve a predictive model using Python, Flask and Heroku

    Intro to Deep Learning with Python & Keras

    San francisco May 5-6

    Curriculum

    The workshop is meant to introduce you to the concepts of deep learning and provide a solid base to build deeper knowledge in the field. You will learn:

    • Fundamentals of deep learning theory
    • How to approach and solve a problem with deep learning
    • Build and train a deep fully connected model with Keras
    • Build and train a Convolutional Neural Net with Keras on a cloud GPU machine
    • Build and train a Recurrent Neural Net with Keras on a cloud GPU machine
    • Application to Image processing/Text processing

    Advanced Deep Learning with Python & Tensorflow

    San francisco May 19-20

    Curriculum

    The workshop is meant to expand your skills in deep learning by exposing you to the Tensorflow and to real world case studies. You will learn:

    • Review of fundamental deep learning architectures (Fully Connected, Convolutional, Recurrent)
    • Build and train a model with pure Tensorflow
    • Online training / continuous training
    • Custom architectures and loss functions
    • Review of famous architectures (Inception, Wavenet)
    • Setting up a machine for deep learning / serving a model

    Unsupervised and Reinforcement Learning with Python, Tensorflow & OpenAI

    San francisco Apr 7-8, Apr 21-22, May 5-6, May 19-20, jun 2-3

    Curriculum

    The workshop is meant to introduce you to unsupervised deep learning and reinforcement learning. You will learn:

    • Train neural networks to play video games using Deep Q-Learning
    • Reduce the dimensionality of your data using autoencoders
    • Improve the efficiency of your algorithms with generative adversarial networks
    • Train AI agents to interact in an environment using OpenAI Gym
    • Train a Word2Vec model to encode natural language

    Bundles

    Zero to Deep Learning® 3 weekends bundle

    San francisco Apr 21-22, May 5-6, May 19-20

    First weekend:
    Intro to Machine Learning with Python & Scikit-Learn

    • Recognize problems that can be solved with Machine Learning
    • Select the right technique (is it a classification problem? a regression? needs preprocessing?)
    • Load and manipulate data with Pandas
    • Visualize and explore data with Matplotlib and Bokeh
    • Build regression, classification and clustering models with Scikit-Learn
    • Evaluate model performance with Scikit-Learn
    • Build, train and serve a predictive model using Python, Flask and Heroku

    Second weekend:
    Intro to Deep Learning with Python (Keras/Tensorflow)

    • Fundamentals of deep learning theory
    • How to approach and solve a problem with deep learning
    • Build and train a deep fully connected model with Keras
    • Build and train a Convolutional Neural Net with Keras on a cloud GPU machine
    • Build and train a Recurrent Neural Net with Keras on a cloud GPU machine
    • Application to Image processing/Text processing

    Third weekend:
    Advanced Deep Learning with Python & Tensorflow

    • Review of fundamental deep learning architectures (Fully Connected, Convolutional, Recurrent)
    • Build and train a model with Tensorflow / TFLearn
    • Build and train a model with pure Tensorflow
    • Online training / continous training
    • Custom architectures and loss functions
    • Review of famous architectures (Inception, Wavenet)
    • Setting up a machine for deep learning / serving a model

    Analytics to Reinforcement Learning 5 weekends bundle

    San francisco Apr 7-8, Apr 21-22, May 5-6, May 19-20, jun 2-3

    Curriculum

    The course provides a comprehensive introduction to data science with deep dives in data analytics, machine learning, deep learning and reinforcement learning. It is meant to provide a solid base to build deeper knowledge in the field.

    First weekend:
    Intro to Data Analytics with Python, SQL, Spark and Seaborn

    • Use Python and Pandas to select, group and summarize your data
    • Decide what data to keep and what to ignore
    • Create compelling visualizations using Seaborn and Matplotlib
    • Connect and retrieve data from a database using Python
    • Extend your analyses to relational databases using SQL
    • Perform aggregations and combinations using SQL
    • Include unstructured data sources in your analysis using Spark
    • Scale up your analyses to Gygabytes of data using Spark on AWS
    • Combine Spark and SQL for maximum flexibility and power

    Second weekend:
    Intro to Machine Learning with Python & Scikit-Learn

    • Recognize problems that can be solved with Machine Learning
    • Select the right technique (is it a classification problem? a regression? needs preprocessing?)
    • Load and manipulate data with Pandas
    • Visualize and explore data with Matplotlib and Bokeh
    • Build regression, classification and clustering models with Scikit-Learn
    • Evaluate model performance with Scikit-Learn
    • Build, train and serve a predictive model using Python, Flask and Heroku

    Third weekend:
    Intro to Deep Learning with Python (Keras/Tensorflow)

    • Fundamentals of deep learning theory
    • How to approach and solve a problem with deep learning
    • Build and train a deep fully connected model with Keras
    • Build and train a Convolutional Neural Net with Keras on a cloud GPU machine
    • Build and train a Recurrent Neural Net with Keras on a cloud GPU machine
    • Application to Image processing/Text processing

    Fourth weekend:
    Advanced Deep Learning with Python & Tensorflow

    • Review of fundamental deep learning architectures (Fully Connected, Convolutional, Recurrent)
    • Build and train a model with pure Tensorflow
    • Online training / continous training
    • Custom architectures and loss functions
    • Review of famous architectures (Inception, Wavenet)
    • Setting up a machine for deep learning / serving a model

    Fifth weekend:
    Reinforcement Learning with Python, Tensorflow and OpenAI

    • Train neural networks to play video games using Deep Q-Learning
    • Reduce the dimensionality of your data using autoencoders
    • Improve the efficiency of your algorithms with generative adversarial networks
    • Train AI agents to interact in an environment using OpenAI Gym and Universe
    • Train a Word2Vec model to encode natural language