Price: $147

Regular price $210, discounted 10%

4 hour immersive session
Hands-on training with Q&A
Recording available on-demand
Certification of Completion

30% Discount Ends in:

Subscribe and get an additional 10% to 35% off ALL live training session

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

ON DEMAND

Get Certified in Deep Learning in ONLY 6 courses

FREE WITH THE AI+ PLANS

BEGIN

How Deep Learning Works
CLASS 1

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How Deep Learning Works

The Unreasonable Effectiveness of Deep Learning

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

Essential Neural Network Theory

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
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

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Class 2: Building and Training a Deep Learning Network
CLASS 2

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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

Softmax Layers

Dropout

Data Augmentation

TensorFlow Playground: Visualizing a Deep Net in Action

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

Revisiting our Shallow Net

A Deep Neural Net

Tuning Model Hyperparameters
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 […]

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Class 3: Machine Vision and Creativity
CLASS 3

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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 Neural Networks in Practice with Keras

Classic Model Architectures: LeNet-5, AlexNet & VGGNet

Residual Networks (ResNet)

U-Net

Image Classification

Object Detection

Semantic Image Segmentation

Transfer Learning

Generative Adversarial Networks

How GANs were Born

Applications of GANs

Essential GAN Theory

A Cartoon-Drawing GAN in Keras
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 […]

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Natural Language Processing
CLASS 4

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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

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

Modeling Natural Language Data

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 and Advanced Neural Networks

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
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 […]

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Class 5: Deep Reinforcement Learning and A.I.
CLASS 5

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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 Game

Essential Deep Reinforcement Learning Theory

Defining a DQN Agent

Interacting with an OpenAI Gym Environment

Advanced Agents

SLM-Lab for Agent Experimentation and Optimization

Policy Gradients

REINFORCE

The Actor-Critic Algorithm
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 […]

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Class 6: PyTorch and Beyond
CLASS 6

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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 Neural Networks in PyTorch

Final Topics

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
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 […]

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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 (free with the Ai+ Training Plans)
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. All the sessions are available on-demand
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

Part 1 : On-Demand

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

Part 2 : On-Demand

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

Part 3 : On-Demand

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

Part 4 : On-Demand

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

Part 5 : On-Demand

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 : On-Demand

Part 6: Live Training: April 13th, 2022

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 All 6 Recordings

Access to All AI+ course library

$147

Per Course

$699

all 6 courses

FREE WITH ANNUAL PREMIUM SUBSCRIPTION

Key Details

DATE

DURATION:

LEVEL:

On-Demand

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.

Price: $147

Regular price $210, discounted 10%

30% Discount Ends in:

Subscribe and get an additional 10% to 35% off ALL live training session

Course Outline

What’s the plan?

LIVE TRAINING: Introduction to PYTHON for Programming

October 12th @12 PM EST

Upcoming Live Training

Course Overview

LIVE DEEP LEARNING CERTIFICATION

DEEP LEARNING BOOTCAMP

ON DEMAND

Get Certified in Deep Learning in ONLY 6 courses

How Deep Learning Works

CLASS 1 How Deep Learning Works

Class 2: Building and Training a Deep Learning Network

CLASS 2 Class 2: Building and Training a Deep Learning Network

Class 3: Machine Vision and Creativity

CLASS 3 Class 3: Machine Vision and Creativity

Natural Language Processing

CLASS 4 Natural Language Processing

Class 5: Deep Reinforcement Learning and A.I.

CLASS 5 Class 5: Deep Reinforcement Learning and A.I.

Class 6: PyTorch and Beyond

CLASS 6 Class 6: PyTorch and Beyond

Jon Krohn

Part 1: Live Training: Feb 2nd, 2022

Part 1 : On-Demand

How Deep Learning Works

Part 2: Live Training: Feb 16th, 2022

Part 2 : On-Demand

Building and Training a Deep Learning Network

Part 3: Live Training: March 2nd, 2022

Part 3 : On-Demand

Machine Vision and Creativity

Part 4: Live Training: March 16th, 2022

Part 4 : On-Demand

Natural Language Processing

Part 5: Live Training: March 30th, 2022

Part 5 : On-Demand

Deep Reinforcement Learning and A.I.

Part 6 : On-Demand

Part 6: Live Training: April 13th, 2022

PyTorch and Beyond

6-weeks Deep Learning Bootcamp

<img class="lazyload" decoding="async" class="" src="https://emojipedia-us.s3.dualstack.us-west-1.amazonaws.com/thumbs/60/emojidex/112/key_1f511.png" alt="Key on emojidex " width="34" height="34" /> Key Details

Prerequisites

Key Details