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Course Outline
What’s the plan?Â
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 ProgrammingÂ
October 12th @12 PM EST
Upcoming Live Training
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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
-
How Deep Learning Works
CLASS 1How 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 -
Class 2: Building and Training a Deep Learning Network
CLASS 2Class 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 ActionÂ
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 […] -
Class 3: Machine Vision and Creativity
CLASS 3Class 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)Â
- U-Net
- Image Classification
- Object DetectionÂ
- Semantic Image SegmentationÂ
- Transfer LearningÂ
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 […] -
Natural Language Processing
CLASS 4Â Natural Language Processing includes the following modules:
The Power and Elegance of Deep Learning for NLP
- Introduction to Deep Learning for Natural Language ProcessingÂ
- 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 NetworkÂ
- 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 LSTMsÂ
- Stacked LSTMsÂ
- Parallel Network ArchitecturesÂ
- 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 […] -
Class 5: Deep Reinforcement Learning and A.I.
CLASS 5Class 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 […] -
Class 6: PyTorch and Beyond
CLASS 6Class 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 2.0Â
- 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 […]
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 LearningÂ
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
REGISTER NOWHow 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 NOWPart 2 : On-Demand
REGISTER NOWBuilding 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 ActionÂ
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 NOWPart 3 : On-Demand
REGISTER NOWMachine 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)Â
- U-Net
- Image Classification
- Object DetectionÂ
- Semantic Image SegmentationÂ
- 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 NOWPart 4 : On-Demand
REGISTER NOWNatural 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 ProcessingÂ
- 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 NetworkÂ
- Document Classification with a Convolutional Neural Network
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory Units (LSTMs)
- Gated Recurrent Units (GRUs)
- Bi-Directional LSTMsÂ
- Stacked LSTMsÂ
- Parallel Network ArchitecturesÂ
- Transformers: BERT, ELMo & Friends
- Financial Time Series Applications
Part 5: Live Training: March 30th, 2022
REGISTER NOWPart 5 : On-Demand
REGISTER NOWDeep 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.Â
- Artificial General Intelligence
- Applications of Deep Reinforcement Learning
- The Cartpole GameÂ
- Essential Deep Reinforcement Learning TheoryÂ
- Defining a DQN Agent
- Interacting with an OpenAI Gym Environment
- SLM-Lab for Agent Experimentation and OptimizationÂ
- Policy Gradients
- REINFORCE
- The Actor-Critic Algorithm
Part 6 : On-Demand
REGISTER NOWPart 6: Live Training: April 13th, 2022
REGISTER NOWPytorch 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 2.0Â
- 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
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.Â
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