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Course Outline

What’s the plan? 

Matt Harrison has been working with Python and data since 2000. He has a computer science degree from Stanford. He has worked at many amazing companies, created cool products, wrote a couple books, and taught thousands Python and Data Science. Currently, he is working as a corporate trainer, author, and consultant through his company Metasnake, which provides consulting and teaches corporations how to be effective with Python and data science.

LIVE TRAINING: Introduction to PYTHON for Programming 

October 12th @12 PM EST

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Course Overview

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LIVE DEEP LEARNING CERTIFICATION

LIVE TRAINING CERTIFICATION:

Deep Learning

With Dr. Jon Krohn

DEEP LEARNING BOOTCAMP

F E B R U A R Y - A P R I L 2022

Get Certified in Deep Learning in ONLY 6-Weeks
REGISTER NOW - starts February 2
BEGIN
  • CLASS 1 How Deep Learning Works

    You’ll develop a working understanding of how deep learning works over two modules. In Module 1 you will get key insights into 'The Unreasonable Effectiveness of Deep Learning'. In module 2 you will learn Essential Neural Network Theory
  • CLASS 2 Class 2: Building and Training a Deep Learning Network

    Class 2: Building and Training a Deep Learning Network Essential Deep Learning Theory Stochastic Gradient Descent Backpropagation Mini-Batches Learning Rate Fancy Optimizers (e.g., Adam, Nadam) Glorot/He Weight Initialization Dense Layers […]
  • CLASS 3 Class 3: Machine Vision and Creativity

    Class 3: Machine Vision and Creativity Introducing Deep Learning for Machine Vision Machine Vision Applications Review of Relevant Fundamental Deep Learning Theory Essential Theory of Convolutional Neural Networks   Convolutional […]
  • CLASS 4 Natural Language Processing

     Natural Language Processing includes the following modules: The Power and Elegance of Deep Learning for NLP Introduction to Deep Learning for Natural Language Processing  Easy, Intermediate, and Complex NLP Applications […]
  • CLASS 5 Class 5: Deep Reinforcement Learning and A.I.

    Class 5: Deep Reinforcement Learning and A.I. The Foundations of Artificial Intelligence The Contemporary State of A.I. Artificial General Intelligence Applications of Deep Reinforcement Learning Deep Q-Learning Networks The Cartpole […]
  • CLASS 6 Class 6: PyTorch and Beyond

    Class 6: PyTorch and Beyond Deep Learning with PyTorch Overview of the Leading Deep Learning Libraries Detailed Comparison of TensorFlow 2 and PyTorch A Shallow Neural Network in PyTorch Deep […]
Certificate Award
CLASS 1CLASS 2CLASS 3CLASS 4CLASS 5CLASS 6

How It Works

  • Enroll in full Deep Learning Bootcamp program or choose one of the six Bootcamp courses. 

  • Each course includes exercises to improve learning outcomes.

  • Coding demos allow you to learn hands-on skills.

  • Each course includes exercises to improve learning outcomes.

  • Learn at your own pace. If you are not available for the live sessions, you can take the on-demand classes.

  • Take the Deep Learning Exam and become certified in Deep Learning. 

Meet Your Instructor

Jon Krohn

Jon Krohn is Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into six languages. He is also the host of SuperDataScience, the industry’s most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at Columbia University, New York University, and leading industry conferences, and via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010; his papers have been cited over a thousand times.

Student Testimonials

“Excellent comprehensive course covering the basics for those, like myself with no knowledge. Also includes walk through of notebooks. showing improvements that can be made to DL models to get better ones, in both TensorFlow 2 and PyTorch; includes strengths/weaknesses of TensorFlow 2 and PyTorch. Ends with additional resources for those who want to focus on particular aspects – NLP, images etc.”

Fiona Boyd, Data Science Advisor

“Jon did an excellent delivery of such a complex topic and gave a thorough, non boring, presentation within such a discreet time. He have a presentation covering from the history of deep learning to actual execution of code to presenting helpful resources. It was a true pleasure to take this course. So much information was given that I need time to process all that wealth of information but feel confident I can with all the resources Jon provided. Thank you!

Wendy Sanchez, Data Scientist

“The content was interesting, at just the right level of detail (not too over heads for people new to the topic but not so basic as to be a waste of time), and engaging. I loved the style and flow of the course, with lots of visuals and examples to keep us engaged. This is the best workshop/training/presentation I have attended in a long time.”

Indi Matthew, Analytics Consultant

What you will learn

This course is an introduction to deep neural networks that brings high-level theory to life with working, interactive examples featuring TensorFlow 2, Keras, and PyTorch — all three of the principal Python libraries for deep learning. Essential theory will be covered in a manner that provides students with a complete intuitive understanding of deep learning’s underlying foundations.

Paired with hands-on code demos in Jupyter notebooks as well as strategic advice for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of artificial neural networks to build production-ready deep learning applications across all of the contemporary families, including:

  • Convolutional Neural Networks for machine vision
  • Recurrent Neural Networks for natural language processing and time-series analysis
  • Generative Adversarial Networks for producing jaw-dropping synthetic data
  • Deep Reinforcement Learning for complex sequential decision-making

Part 1: Live Training: Feb 2nd, 2022

REGISTER NOW

Part 1 : Live Training: February 2nd, 2022

REGISTER NOW

How Deep Learning Works

The Unreasonable Effectiveness of Deep Learning

Essential Neural Network Theory

  • A Brief History of the Rise of Deep Learning

  • Deep Learning vs Other Machine Learning Approaches

  • Dense Feedforward Networks and Convolutional Networks for Machine Vision

  • Recurrent Networks for Natural Language Processing and Time-Series Predictions

  • Generative Adversarial Networks for Artistic Creativity and Deep Reinforcement Learning for Sequential Decision-Making

  • An Artificial Neural Network in TensorFlow 2

  • The Essential Math of Artificial Neurons

  • The Essential Math of Neural Networks

  • Activation Functions

  • Cost Functions, including Cross-Entropy

How Deep Learning Works

Module 1:
The Unreasonable Effectiveness of Deep Learning

Module 2:
Essential Neural Network Theory

  • A Brief History of the Rise of Deep Learning
  • Deep Learning vs Other Machine Learning Approaches
  • Dense Feedforward Networks
  • Convolutional Networks for Machine Vision
  • Recurrent Networks for Natural Language Processing and Time-Series Predictions
  • Generative Adversarial Networks for Artistic Creativity
  • Deep Reinforcement Learning for Sequential Decision-Making
  • An Artificial Neural Network in TensorFlow 2
  • The Essential Math of Artificial Neurons
  • The Essential Math of Neural Networks
  • Activation Functions
  • Cost Functions, including Cross-Entropy

Part 2: Live Training: Feb 16th, 2022

REGISTER NOW

Part 2 : Live Training: February 16th, 2022

REGISTER NOW

Building and Training a Deep Learning Network

Essential Deep Learning Theory

Deep Learning with Keras, TensorFlow’s High-Level API

  • Stochastic Gradient Descent and Backpropagation

  • Mini-Batches and Learning Rate

  • Fancy Optimizers (e.g., Adam, Nadam) and Glorot/He Weight Initialization

  • Dense Layers and Softmax Layers

  • Dropout and Data Augmentation

  • TensorFlow Playground: Visualizing a Deep Net in Action 

  • Revisiting our Shallow Net

  • A Deep Neural Net

  • Tuning Model Hyperparameters

Building and Training a Deep Learning Network

Module 1:
Essential Deep Learning Theory

Module 2:
Deep Learning with Keras, TensorFlow’s High-Level API

  • Stochastic Gradient Descent
  • Backpropagation
  • Mini-Batches
  • Learning Rate
  • Fancy Optimizers (e.g., Adam, Nadam)
  • Glorot/He Weight Initialization
  • Dense Layers
  • Softmax Layers
  • Dropout
  • Data Augmentation
  • TensorFlow Playground: Visualizing a Deep Net in Action
  • Revisiting our Shallow Net
  • A Deep Neural Net
  • Tuning Model Hyperparameters

Part 3: Live Training: March 2nd, 2022

REGISTER NOW

Part 3 : Live Training: March 2nd, 2022

REGISTER NOW

Machine Vision and Creativity

Introducing Deep Learning for Machine Vision

Convolutional Neural Networks in Practice with Keras

Generative Adversarial Networks

  • Machine Vision Applications

  • Review of Relevant Fundamental Deep Learning Theory

  • Essential Theory of Convolutional Neural Networks

  • Classic Model Architectures: LeNet-5, AlexNet & VGGNet

  • Residual Networks (ResNet) and U-Net

  • Image Classification and Object Detection

  • Semantic Image Segmentation and Transfer Learning

  • How GANs were Born

  • Applications of GANs

  • Essential GAN Theory

  • A Cartoon-Drawing GAN in Keras

Machine Vision and  Creativity

Module 1:
Introducing Deep Learning for Machine Vision

Module 2:
Convolutional Neural Networks in Practice with Keras

Module 3:
Generative Adversarial Networks

  • Machine Vision Applications
  • Review of Relevant Fundamental Deep Learning Theory
  • Essential Theory of Convolutional Neural Networks
  • Classic Model Architectures: LeNet-5, AlexNet & VGGNet
  • Residual Networks (ResNet) 
  • U-Net
  • Image Classification
  • Object Detection 
  • Semantic Image Segmentation 
  • Transfer Learning
  • How GANs were Born
  • Applications of GANs
  • Essential GAN Theory
  • A Cartoon-Drawing GAN in Keras

Part 4: Live Training: March 16th, 2022

REGISTER NOW

Part 4 : Live Training: March 16th, 2022

REGISTER NOW

Natural Language Processing

The Power and Elegance of Deep Learning for NLP

Modeling Natural Language Data

Recurrent and Advanced Neural Networks

  • Introduction to Deep Learning for Natural Language Processing

  • Easy, Intermediate, and Complex NLP Applications

  • Review of Relevant Fundamental Deep Learning Theory

  • Word Vectors: Representing Language as Embeddings

  • An Interactive Visualization of Vector-Space Embeddings

  • Best Practices for Preprocessing Natural Language Data

  • Using word2vec to Create Word Vectors

  • Document Classification with a Dense Neural Network

  • Document Classification with a Convolutional Neural Network

  • Recurrent Neural Networks (RNNs)

  • Long Short-Term Memory Units (LSTMs)

  • Gated Recurrent Units (GRUs)

  • Bi-Directional LSTMs

  • Stacked LSTMs and Parallel Network Architectures

  • Transformers: BERT, ELMo & Friends

  • Financial Time Series Applications

Natural Language Processing

Module 1:
The Power and Elegance of Deep Learning for NLP

Module 2:
Modeling Natural Language Data

Module 3:
Recurrent and Advanced Neural Networks

  • Introduction to Deep Learning for Natural Language Processing 
  • Easy, Intermediate, and Complex NLP Applications
  • Review of Relevant Fundamental Deep Learning Theory
  • Word Vectors: Representing Language as Embeddings
  • Word Vector Arithmetic
  • An Interactive Visualization of Vector-Space Embeddings
  • Vector-Based Representations vs One-Hot Encodings
  • Best Practices for Preprocessing Natural Language Data
  • Using word2vec to Create Word Vectors
  • Document Classification with a Dense Neural Network 
  • Document Classification with a Convolutional Neural Network
  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory Units (LSTMs)
  • Gated Recurrent Units (GRUs)
  • Bi-Directional LSTMs 
  • Stacked LSTMs 
  • Parallel Network Architectures 
  • Transformers: BERT, ELMo & Friends
  • Financial Time Series Applications

Part 5: Live Training: March 30th, 2022

REGISTER NOW

Part 5 : Live Training: March 30th, 2022

REGISTER NOW

Deep Reinforcement Learning and A.I.

The Foundations of Artificial Intelligence

Deep Q-Learning Networks

Advanced Agents

  • The Contemporary State of A.I.

  • Artificial General Intelligence

  • Applications of Deep Reinforcement Learning

  • The Cartpole Game

  • Essential Deep Reinforcement Learning Theory

  • Defining a DQN Agent

  • Interacting with an OpenAI Gym Environment

  • SLM-Lab for Agent Experimentation and Optimization

  • Policy Gradients

  • REINFORCE

  • The Actor-Critic Algorithm

Deep Reinforcement Learning and A.I.

Module 1:
The Foundations of Artificial Intelligence

Module 2:
Deep Q-Learning Networks

Module 3:
Advanced Agents

  • The Contemporary State of A.I. 
  • Artificial General Intelligence
  • Applications of Deep Reinforcement Learning
  • The Cartpole Game 
  • Essential Deep Reinforcement Learning Theory 
  • Defining a DQN Agent
  • Interacting with an OpenAI Gym Environment
  • SLM-Lab for Agent Experimentation and Optimization 
  • Policy Gradients
  • REINFORCE
  • The Actor-Critic Algorithm

Part 6 : Live Training: April 13th, 2022

REGISTER NOW

Part 6: Live Training: April 13th, 2022

REGISTER NOW

Pytorch and Beyond

Deep Learning with Pytorch

Final Topics

  • Overview of the Leading Deep Learning Libraries

  • Detailed Comparison of TensorFlow 2 and PyTorch

  • A Shallow Neural Network in PyTorch

  • Deep Neural Networks in PyTorch

  • Software 2.0

  • Approaching Artificial General Intelligence

  • Creating Your Own Deep Learning Project

  • What to Study Next, Depending on Your Interests

  • Jeanne Calment and Your Role in the A.I. Revolution

PyTorch and Beyond

Module 1:
Deep Learning with PyTorch

Module 2:
Final Topics

  • Overview of the Leading Deep Learning Libraries
  • Detailed Comparison of TensorFlow 2 and PyTorch
  • A Shallow Neural Network in PyTorch
  • Deep Neural Networks in PyTorch
  • Software 2.0 
  • Approaching Artificial General Intelligence
  • Creating Your Own Deep Learning Project
  • What to Study Next, Depending on Your Interests
  • Jeanne Calment and Your Role in the A.I. Revolution

6-weeks Deep Learning Bootcamp

PRICE

Pricing


BOOTCAMP

 

Access to ONE selected Deep Learning course 

Certificate of completion

Access to Live Deep Learning Bootcamp

Access to All 6 Recordings 

Access to All AI+ course library


$147


Per Course


$699


all 6 courses




FREE WITH ANNUAL PREMIUM SUBSCRIPTION


 

Key on emojidex Key Details

DATE

TIME:

DURATION:

LEVEL:

Febr-Apr 2022

TIME: 12 PM EST, 9 AM PST

3.5-hour each class

BEGINNER

Prerequisites

It may be challenging to follow along through the code demos and exercises without some experience in object-oriented programming (ideally Python).  Various Introduction to Python courses are available on AI+ to help get you up to speed. 

All of the sessions will be recorded. for your convenience.

Open Data Science

Ai+ | ODSC
One Broadway, 14th Floor
Cambridge, MA 02142
admin_aiplus@odsc.com

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