Live training with Dr. Jon Krohn is starting on July 29th at 12 PM (ET)

Training duration is 4 hours

For 30% discount use the code: jon30 at the checkout.

Regular Price

Instructor

Instructor Bio:

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the 2019 book Deep Learning Illustrated, an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy. Jon holds a Ph.D. in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010; his papers have been cited over a thousand times.

Dr. Jon Krohn

Chief Data Scientist, Author of Deep Learning Illustrated | Untapt

What will you learn?

  • Understand the essential theory of artificial neural networks, including which deep learning approach is most appropriate for solving a given problem

  • Build production-ready deep neural networks with the NumPy-esque PyTorch library as well as with the heavyweight TensorFlow 2 library (by taking advantage of its in-built, easy-to-use Keras module)

  • Interpret the output of deep learning models to troubleshoot and improve results

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Course Abstract

Relatively obscure a few short years ago, deep learning is ubiquitous today across data-driven applications as diverse as machine vision, natural language processing, artistic creativity, and complex sequential decision-making. 

This deep learning primer brings the revolutionary approach behind contemporary artificial intelligence to life with interactive demos featuring TensorFlow 2 and PyTorch, the two leading deep learning libraries. 

To facilitate an intuitive understanding of deep learning’s artificial-neural-network foundations, essential theory will be introduced visually and pragmatically. Paired with tips for overcoming common pitfalls and hands-on code run-throughs provided in Python Jupyter notebooks, this foundational knowledge empowers you to build powerful state-of-the-art deep neural network models. Many resources will be provided for digging further into any deep learning-related topic that piques your interest. 

Course Schedule

Segment 1: The Unreasonable Effectiveness of Deep Learning (40 min)


  • Training Overview 

  • 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

  • Deep Reinforcement Learning for Sequential Decision-Making

  • Generative Adversarial Networks for Creativity

  • Overview of the Leading Deep Learning Libraries, including TensorFlow 2, Keras, PyTorch, MXNet, CNTK, and Caffe


Segment 2: Essential Deep Learning Theory (80 min)


  • An Artificial Neural Network with Keras

  • The Essential Math of Artificial Neurons

  • The Essential Math of Neural Networks

  • Activation Functions

  • Cost Functions, including Cross-Entropy

  • 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 


Segment 3: TensorFlow 2 and PyTorch (90 min)


  • Revisiting our Shallow Neural Network

  • Deep Neural Nets in TensorFlow 2

  • Deep Neural Nets in PyTorch

  • Tuning Model Hyperparameters

  • Creating Your Own Deep Learning Project

  • What to Study Next, Depending on Your Interests

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Who will be interested in this course?

  • Software developers, data scientists, analysts, statisticians and other data-related professionals are the core target audience for this training. Moreover, this training is for anyone:

  • Who would like to be exposed to the range of applications of deep learning approaches.

  • Who yearn to understand how deep learning works.

  • Who would like to create state-of-the-art machine-learning models well-suited to solving a broad range of problems, including complex, non-linear problems with large, high-dimensional data sets.

Which knowledge and skills you should have?

  • Some experience with machine learning would make this workshop easier to follow, but is by no means necessary.

  • All code demos during the training will be in Python, so experience with it or another object-oriented programming language would be helpful.

What is included in your ticket?

  • Access to live training and QA session with the Instructor

  • Certification of completion

Testimonials

Dr. Jon Krohn's previous students

“"Jon Krohn is great! His knowledge is so 'deep' and he covers basics and then quickly gets into sophisticated concepts; and, he has an awesome attitude and energy. The material that he has prepared is totally fantastic. His structural framework for understanding; the way he builds on the concepts; and the notebooks to experiment and get started confidently; is a fast and reliable way to get a handle on these technologies and their capabilities" ”

Student

“"Very knowledgeable and experienced instructor. Very good offline materials to follow-up on all of the topics discussed during the training in greater detail."”

Data Engineer

“Jon managed to finally break the ice into Deep Learning for me that has been hanging around for quite enough time. Good balance of theory, hands-on modeling and action was the key to grasping the big picture of deep learning overall”