Deep Learning (with TensorFlow, Keras and PyTorch)

Deep Learning (with TensorFlow, Keras and PyTorch)

Deep Learning (with TensorFlow, Keras and PyTorch)

This course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs featuring TensorFlow, Keras, and PyTorch — the leading Deep Learning libraries. 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 run-throughs in Jupyter notebooks as well as strategic advice for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of neural networks to build production-ready Deep Learning applications across all of the contemporary families, including:

  • Convolutional Networks for machine vision
  • Long Short-Term Memory Recurrent Nets for natural language processing and time series analysis
  • Generative Adversarial Networks for producing jaw-dropping synthetic data
  • Reinforcement Learning for complex sequential decision-making
Course Overview

This course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs featuring TensorFlow, Keras, and PyTorch — the leading Deep Learning libraries. 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 run-throughs in Jupyter notebooks as well as strategic advice for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of neural networks to build production-ready Deep Learning applications across all of the contemporary families, including:

  • Convolutional Networks for machine vision
  • Long Short-Term Memory Recurrent Nets for natural language processing and time series analysis
  • Generative Adversarial Networks for producing jaw-dropping synthetic data
  • Reinforcement Learning for complex sequential decision-making
October Session
$2990.00
Early bird pricing
$2840.50
October Session
Oct 19 - Dec 7, 2019, 11:00am-5:00pm

Date and Time

October Session Early-bird Pricing!

Oct 19 - Dec 7, 2019, 11:00am-5:00pm
Day 1: October 19, 2019
Day 2: October 26, 2019
Day 3: November 16, 2019
Day 4: November 23, 2019
Day 5: December 7, 2019
$2990.00$2840.50
Add to Cart

Instructors

Jon Krohn
Jon Krohn
Jon Krohn is Chief Data Scientist at the machine-learning startup untapt. He presents an acclaimed series of tutorials on artificial neural networks, including Deep Learning with TensorFlow LiveLessons. Jon holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading academic journals since 2010. His forthcoming book, Deep Learning Illustrated, is being published on Pearson's Addison-Wesley imprint in 2019.

Product Description


Overview

 

Facilitated by the confluence of inexpensive computing power, unprecedentedly large data sets, and clever theoretical advances, Deep Learning algorithms are driving the contemporary revolution in Artificial Intelligence. Deep Learning has emerged as uniquely influential across a broad range of applications, including classification (e.g., visual recognition, sentiment analysis), prediction (e.g., stock markets, health outcomes), generation (e.g., creating works of art, composing music), and sequential decision-making (e.g., games,robotics). In the past few years, Deep Neural Networks have made their way into countless everyday applications, including Tesla’s Autopilot, Amazon’s Alexa, and Google’s suggested email replies. Indeed, Deep Learning algorithms have exceeded human performance on previously intractable computational problems like language translation, object detection, and the game of Go.

This course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs featuring TensorFlow, Keras, and PyTorch — the three principal Deep Learning libraries. 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 run-throughs in Jupyter notebooks as well as strategic advice for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of neural networks to build production-ready Deep Learning applications across all of the contemporary families, including:

  • Convolutional Networks for machine vision
  • Long Short-Term Memory Recurrent Nets 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

Details


Instructor Interview


Jon Krohn, Instructor for Deep Learning course at NYC Data Science Academy on Vimeo.


Goals

 

This is a “short course” of five weeks, with six hours of class per week. Classes will be given in a lab setting, with hands-on exercises mixed with lectures. Students should bring a laptop to class. You will have the option of creating and completing your own major Deep Learning project over the duration of the program.

Who Is This Course For?

 

This course is perfect for software engineers, data scientists, analysts, and statisticians with an interest in Deep Learning. No previous knowledge of Deep Learning is assumed. Previous experience with statistics or machinelearning is not necessary, but will be an asset if you have it. Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful.

Prerequisites

 

It would be challenging to follow along through the code demos and exercises without some experience in object-oriented programming, ideally Python (introductory course here). Students with experience in other languages (e.g., R) have, however, been very successful.


Outcomes

 

By the end of the course, you will be able to:
  • Build Deep Learning models in all of the major libraries: TensorFlow, Keras and PyTorch
  • Understand the language and theory of Artificial Neural Networks
  • Excel across a broad range of computational problems including Machine Vision, Natural Language Processing and Reinforcement Learning
  • Create algorithms with state-of-the-art performance by fine-tuning model architectures
  • Self-direct and complete your own Deep Learning projects

Certificate

Certificates are awarded at the end of the program at the satisfactory completion of the course.

Students are evaluated on a pass/fail basis for their performance on the required homework and final project (where applicable). Students who complete 80% of the homework and attend a minimum of 85% of all classes are eligible for the certificate of completion.

 

Syllabus

 
Unit 1: The Unreasonable Effectiveness of Deep Learning

  • An Introduction to Neural Networks and Deep Learning
  • Interactive Visualization of an Artificial Neural Network
  • Hardware Options for Deep Learning, including How to Build a Deep Learning Server
  • Running Jupyter Notebooks within a Docker Container
  • The Families of Deep Neural Nets and their Applications
  • A Shallow TensorFlow Neural Network with Keras Layers

Unit 2: How Deep Learning Works

  • Essential Theory I: Neural Units
  • Interactive Visualization of Neural Units
  • Essential Theory II: Cost Functions, Gradient Descent, and Backpropagation
  • Interactive Visualization of Neural Networks
  • An Intermediate Neural Network
  • Data Sets for Deep Learning
  • Your Deep Learning Project: Ideating

Unit 3: Building and Training a Deep Learning Network

  • Review Content and Take-Home Exercises
  • Essential Theory III: Weight Initialization and Mini-Batches
  • Essential Theory IV: Unstable Gradients and Avoiding Overfitting
  • A Deep TensorFlow Neural Network with Keras Layers
  • TensorBoard and the Interpretation of Model Outputs

Unit 4: Machine Vision

  • Introduction to Convolutional Neural Networks for Visual Recognition
  • Classic ConvNet Architectures: LeNet-5 and AlexNet
  • Object Detection
  • Image Segmentation
  • Transfer Learning
  • Your Deep Learning Project: Formulating

Unit 5: Natural Language Processing

  • Reviewing Content and Take-Home Exercises
  • Word Vectors: word2vec and Vector-Space Embedding
  • Recurrent Neural Networks
  • Long Short-Term Memory Units
  • Gated Recurrent Units
  • Classifying Documents: Sentiment Analysis

Unit 6: Time Series Analysis

  • Autoencoders: Encoder-Decoder Structures
  • Sequence-to-Sequence Models and Attention
  • Financial Forecasting
  • Hyperparameter Tuning
  • Non-Sequential Models
  • Your Deep Learning Project: Assessing

Unit 7: Advanced TensorFlow

  • Introducing TensorFlow Graphs
  • Representing Neurons as TensorFlow Graphs
  • Optimizing TensorFlow Graphs
  • Deep Learning with TensorFlow 1.x
  • Deep Learning with TensorFlow 2.x

Unit 8: PyTorch

  • Comparison of the Leading Deep Learning Libraries
  • Autodifferentiation
  • Sequential Deep Learning Models in PyTorch
  • Forward Propagation and Optimization in PyTorch
  • Model Validation in PyTorch
  • Your Deep Learning Project: Improving

Unit 9: Generative Adversarial Networks

  • GAN Applications
  • Essential GAN Theory
  • Simulating Artistic Creativity with a GAN
  • Resources for Deep Learning Self-Study

Unit 10: Reinforcement Learning

  • Applications of Reinforcement Learning
  • Reinforcement Learning Environments: OpenAI Gym
  • Essential Reinforcement Learning Theory
  • Deep Q-Learning Networks
  • Policy Gradients and the Actor-Critic Algorithm
  • Jeanne Calment and Your Role in the AI Revolution
  • Your Deep Learning Project: Presentation

Reviews

There are no reviews yet.

Instructors

Jon Krohn
Jon Krohn
Jon Krohn is Chief Data Scientist at the machine-learning startup untapt. He presents an acclaimed series of tutorials on artificial neural networks, including Deep Learning with TensorFlow LiveLessons. Jon holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading academic journals since 2010. His forthcoming book, Deep Learning Illustrated, is being published on Pearson's Addison-Wesley imprint in 2019.

Product Description


Overview

 

Facilitated by the confluence of inexpensive computing power, unprecedentedly large data sets, and clever theoretical advances, Deep Learning algorithms are driving the contemporary revolution in Artificial Intelligence. Deep Learning has emerged as uniquely influential across a broad range of applications, including classification (e.g., visual recognition, sentiment analysis), prediction (e.g., stock markets, health outcomes), generation (e.g., creating works of art, composing music), and sequential decision-making (e.g., games,robotics). In the past few years, Deep Neural Networks have made their way into countless everyday applications, including Tesla’s Autopilot, Amazon’s Alexa, and Google’s suggested email replies. Indeed, Deep Learning algorithms have exceeded human performance on previously intractable computational problems like language translation, object detection, and the game of Go.

This course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs featuring TensorFlow, Keras, and PyTorch — the three principal Deep Learning libraries. 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 run-throughs in Jupyter notebooks as well as strategic advice for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of neural networks to build production-ready Deep Learning applications across all of the contemporary families, including:

  • Convolutional Networks for machine vision
  • Long Short-Term Memory Recurrent Nets 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

Details


Instructor Interview


Jon Krohn, Instructor for Deep Learning course at NYC Data Science Academy on Vimeo.


Goals

 

This is a “short course” of five weeks, with six hours of class per week. Classes will be given in a lab setting, with hands-on exercises mixed with lectures. Students should bring a laptop to class. You will have the option of creating and completing your own major Deep Learning project over the duration of the program.

Who Is This Course For?

 

This course is perfect for software engineers, data scientists, analysts, and statisticians with an interest in Deep Learning. No previous knowledge of Deep Learning is assumed. Previous experience with statistics or machinelearning is not necessary, but will be an asset if you have it. Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful.

Prerequisites

 

It would be challenging to follow along through the code demos and exercises without some experience in object-oriented programming, ideally Python (introductory course here). Students with experience in other languages (e.g., R) have, however, been very successful.


Outcomes

 

By the end of the course, you will be able to:
  • Build Deep Learning models in all of the major libraries: TensorFlow, Keras and PyTorch
  • Understand the language and theory of Artificial Neural Networks
  • Excel across a broad range of computational problems including Machine Vision, Natural Language Processing and Reinforcement Learning
  • Create algorithms with state-of-the-art performance by fine-tuning model architectures
  • Self-direct and complete your own Deep Learning projects

Certificate

Certificates are awarded at the end of the program at the satisfactory completion of the course.

Students are evaluated on a pass/fail basis for their performance on the required homework and final project (where applicable). Students who complete 80% of the homework and attend a minimum of 85% of all classes are eligible for the certificate of completion.

 

Syllabus

 
Unit 1: The Unreasonable Effectiveness of Deep Learning

  • An Introduction to Neural Networks and Deep Learning
  • Interactive Visualization of an Artificial Neural Network
  • Hardware Options for Deep Learning, including How to Build a Deep Learning Server
  • Running Jupyter Notebooks within a Docker Container
  • The Families of Deep Neural Nets and their Applications
  • A Shallow TensorFlow Neural Network with Keras Layers

Unit 2: How Deep Learning Works

  • Essential Theory I: Neural Units
  • Interactive Visualization of Neural Units
  • Essential Theory II: Cost Functions, Gradient Descent, and Backpropagation
  • Interactive Visualization of Neural Networks
  • An Intermediate Neural Network
  • Data Sets for Deep Learning
  • Your Deep Learning Project: Ideating

Unit 3: Building and Training a Deep Learning Network

  • Review Content and Take-Home Exercises
  • Essential Theory III: Weight Initialization and Mini-Batches
  • Essential Theory IV: Unstable Gradients and Avoiding Overfitting
  • A Deep TensorFlow Neural Network with Keras Layers
  • TensorBoard and the Interpretation of Model Outputs

Unit 4: Machine Vision

  • Introduction to Convolutional Neural Networks for Visual Recognition
  • Classic ConvNet Architectures: LeNet-5 and AlexNet
  • Object Detection
  • Image Segmentation
  • Transfer Learning
  • Your Deep Learning Project: Formulating

Unit 5: Natural Language Processing

  • Reviewing Content and Take-Home Exercises
  • Word Vectors: word2vec and Vector-Space Embedding
  • Recurrent Neural Networks
  • Long Short-Term Memory Units
  • Gated Recurrent Units
  • Classifying Documents: Sentiment Analysis

Unit 6: Time Series Analysis

  • Autoencoders: Encoder-Decoder Structures
  • Sequence-to-Sequence Models and Attention
  • Financial Forecasting
  • Hyperparameter Tuning
  • Non-Sequential Models
  • Your Deep Learning Project: Assessing

Unit 7: Advanced TensorFlow

  • Introducing TensorFlow Graphs
  • Representing Neurons as TensorFlow Graphs
  • Optimizing TensorFlow Graphs
  • Deep Learning with TensorFlow 1.x
  • Deep Learning with TensorFlow 2.x

Unit 8: PyTorch

  • Comparison of the Leading Deep Learning Libraries
  • Autodifferentiation
  • Sequential Deep Learning Models in PyTorch
  • Forward Propagation and Optimization in PyTorch
  • Model Validation in PyTorch
  • Your Deep Learning Project: Improving

Unit 9: Generative Adversarial Networks

  • GAN Applications
  • Essential GAN Theory
  • Simulating Artistic Creativity with a GAN
  • Resources for Deep Learning Self-Study

Unit 10: Reinforcement Learning

  • Applications of Reinforcement Learning
  • Reinforcement Learning Environments: OpenAI Gym
  • Essential Reinforcement Learning Theory
  • Deep Q-Learning Networks
  • Policy Gradients and the Actor-Critic Algorithm
  • Jeanne Calment and Your Role in the AI Revolution
  • Your Deep Learning Project: Presentation

Reviews

There are no reviews yet.

Testimonials View All Student Testimonials

Sudhanshu Chib, Lead Analyst
Sudhanshu Chib, Lead Analyst
at
CRISIL Global Research & Analytics

"Great introduction to Deep Learning "

I completed the deep learning course offered by NYDS in April 2018. Course curriculum provides a solid understanding of core theoretical concepts and their practical applications. Best part about the course was that latest papers/ research work (as resent as Jan 2018) were discussed by Jon Krohn who also shared his experience and best practices. Would strongly recommend the course for anyone who has some background in data/programming related work and wants to break into deep learning.
Christian Urrea, Machine Learning Engineer
Christian Urrea, Machine Learning Engineer
at
Shoodoo Analytics

"The place to begin your journey into Deep Learning/AI"

For those looking to break into the field of AI/Deep Learning - I highly recommend this course as the starting point, andthe initial stages of your journey. Although I had previously studied the theory and learned to build DL models on my own — I still thought it critical to establish a solid understanding of both the theoretical and practical knowledge necessary to build the best deep learning models I could. I was a student in Jon Krohn's Deep Learning course in March 2018. Building a foundational base in DL was what I was looking for from this course; and it was what this course delivered. You will touch all the core areas of DL, from feedforward neural nets to RNNs, CNNs even GANs and RL all while covering applications such as computer vision and natural language processing. Most importantly, throughout the course Jon covers the nuts and bolts of each model in such a way that you can understand the theory without a PhD— all the while implementing DL models yourself in class to cement what you learn as you go. Best of all, you’ll be in a group with bright individuals as eager to learn as you. With some work and effort out of class, with this course you can go from zero to confidently building you own deep learning models within a month.
Alexey Malafeyev, Business Intelligence Developer
Alexey Malafeyev, Business Intelligence Developer
at
APG

"Deep Learning course by Jon Krohn was very good..."

Thank you to Jon Krohn for his approach to teach the lectures and for all his systematic organization of all his materials. Also, for being very attentive to the questions and trying to get back with the constructive help ASAP. And a bit of a personal impression: the mashine vision part of the course was better prepared with the interesting and easy to get examples whereas the NLP part could have been more elaborated, but it is by default the case if you compare these two areas. But, knowing how easy Jon was able to organize the steps for the first noticed I trully believe it is just a matter of a few more thoughts for him and his Co. It may be also true that one more day of classes would be very nice addition to cover the materials better. The other missing thing was a simple example on how you would deploy the network in any environment to be used in production.
Zach McCormick, Senior Software Engineer
Zach McCormick, Senior Software Engineer
at
Braze

"Deep Learning w Jon Krohn"

Jon's course on Deep Learning was great. It started with the basics including the background theory, then progressed to looking at concepts in different fields like computer vision, NLP, reinforcement learning, etc. It required a bit of programming experience, but not too much for those worried about their experience level. Likewise for math chops - having a basic understanding of linear algebra helps in understanding the theory, but it's not required to use it in practice. Following along in class was great, and the materials he had available for learning outside of the classroom were fantastic as well (i.e. his materials on GitHub, links from his presentations to outside materials, etc.) If Jon does a course on Intermediate/Advanced Deep Learning, or deep dives on topics within deep learning, I'll definitely be on the waiting list!
Ha Seon Yun
Ha Seon Yun

"Jon Krohn: Deep Learning "

The Deep Learning course by Jon Krohn at the NYCDSA has been one of the best courses I've taken. With a focus on projects, Jon teaches students the tools they need to create their own deep learning project at any level. When I say at any level I really mean at any level. I'm a biology major originally and printed my first 'hello world' a year ago. Even with my very limited coding and programing background, I was able to complete a deep learning project involving creating my own labeled dataset and a convolutional network classifier. You can see it here on my medium page, https://medium.com/@jhaseon/glut1ko-genotyping-classifier-e49f0e5a4ca9. I really appreciated his course structure as he drills in the beginning of class what he terms 'arsenal' deep learning terms and theories which I believe played a huge role in my ability to even create a project. The course is just 5 weeks so a lot of information is packed in weekly, but it really is for deep learning hopefuls of all levels from the basics of keras in machine vision or natural language processing to the intricacies underneath tensorflow. Jon was insightful, responsive, and encouraging to his students throughout the course. He often broke down difficult theories and concepts on the whiteboard with easy to understand examples and drawings which I found very helpful. I am looking forward to more classes from him!
Navin Krishnakumar, Data Scientist
Navin Krishnakumar, Data Scientist
at
Siemens

"Deep Learning by Jon Krohn"

Deep learning course conducted by Jon offers a great learning experience for people starting with their journey on deep learning. Jon starts with the basics and gradually moves on the advance topics. The topics are shared well in advance so that we can prep ourselves before the class. Jon mixes the intuitiveness and the mathematics on the topic in a balanced way. As part of the course, Jon also encourages everyone to do a project and offers great support. My only piece of constructive criticism (which by the way is not at all a criticism) would that the last class is a bit heavy content wise and hence breaking it down a little would be something to consider. Overall, I would highly recommend this course to someone who wants to start with deep learning.
Ilya Fischhoff, Postdoctoral Researcher
Ilya Fischhoff, Postdoctoral Researcher
at
Cary Institute of Ecosystem Studies

"Great intro to Deep Learning!"

I took NYC Data Science Academy's deep learning course, taught by Jon Krohn. Jon is a terrific teacher, and I would heartily recommend this course! Jon had tremendous enthusiasm and patience for questions while still keeping us on track with the schedule. He really broke things down into bite-size, understandable pieces, while still covering a lot of breadth and depth. I appreciated Jon making available his draft book, this really complemented the lectures. I liked the format of once-a-week lessons because it gave time for concepts to sink in and to practice things with my own data in between sessions. I appreciated that Jon made time to troubleshoot challenges we experienced in our own projects. The course was both a great introduction to concepts and to some of the ways people are applying deep learning; here examples from Jon's day job were valuable. One aspect of the course that was very helpful was that Jon set up a Docker environment for us, and shared very clear instructions for getting our computers set up with it in advance. We were all ready to go from the start. I'm all the more grateful for Jon having set that up after recently spending half of a workshop (run by a different data science academy) wrestling with Anaconda. The Jupyter notebooks all just worked, so we could focus on learning.
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Richard Sheng, Co-Founder & CEO
Richard Sheng, Co-Founder & CEO
at
QuantumViz

"Highly Recommend Jon Krohn's Deep Learning Course"

Going into the course, I was slightly afraid that I would get lost in the mathematical concepts, and may not have the necessary technical know-how. Turns out I actually had a ton of fun with this class. Jon's class preparation goes beyond any educator I have worked with. He is able to illustrate the complex concepts with super-easy-to-understand visulizations and/or videos. When it came to application, he walked us through, line-by-line, all the code, and incrementally built up the complexity. Honestly, I can't believe we were able to cover convolutional models, recurrent models, generative adversarial networks, and deep reinforcement learning in such a short time. There was ample amount of sample code in Jupyter Notebooks to follow. Aside from that, Jon was just super nice guy and very humble. The course is beneficial to those that just want to learn in more detail how Artificial Intelligence can be applied, and also those that are more senior data scientists that want to add Deep Learning techniques to their tool-belt. My only recommendation for the school is that it would have been nicer for a cloud environment to be set up for us, or to have very specific instructions on how we can do so. It turned out to be a great learning experience anyway, but for a non-technical person, it was slightly daunting at first.
Khanan Grauer
Khanan Grauer

" Deep learning is an amazing class"

I took the class with Jon Krohn and could not be more pleased. Jon has a rare ability to take a complicated subject and reduce it to its fundamental elements. Instead of approaching the problems with math alone, Jon spends time crafting examples and analogies to make sure students understand the actual building blocks conceptually. That produces an epiphanic experience where we were able to visualize what is happening behind the scenes. The concepts I’ve learned in this class is something I plan to apply to real-world problems and therefore highly recommend this class to anyone who wants to learn about this field.
Michael Roman
Michael Roman

"This Deep Learning Course Was a Joy"

I recently graduated from Jon's Deep Learning course at the New York Data Science Academy and was a big fan. I think the course did a great job of introducing students to the deep learning landscape, helping us understand what makes deep learning techniques excel at a wide range of tasks and then diving into the code (Keras, TensorFlow) to show us how to spin up various networks (NN Regressors, NN Classifiers, Convolutional NN, RNN's, Reinforcement Learning networks, GANs). Jon is a PHD neuroscientist and one thing I particularly enjoyed about the course was that Jon drew upon his background to connect the artificial neural network content with its biological inspiration. To highlight an example, he weaves in the Cambrian Explosion and Hubel & Wiesel's breakthrough experiment on vision in cats to provide context and color on the emergence of complex biological vision systems on earth, its role in the explosion in speciation that occurred ~540 million years ago, and how in the (super recent, relatively) 1950's the scientific community finally began to understand the neural basis for visual perception. It was breakthroughs in scientific understanding like these that motivate certain machine vision architectures we explore in the course. It felt, at many moments, like really mind-bending stuff. Students are encouraged to complete a deep learning capstone project for the course and there were plenty of opportunities to get valuable feedback and mentorship when we got stuck, which was great. This course is probably best suited for folks with some background in coding and a familiarity with foundational machine learning concepts. Overall, it was time and money well spent.
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Mahipal S, Associate Director of Development
Mahipal S, Associate Director of Development
at
KPMG
I attended the Deep Learning course at the NY Data Science Academy that was taught by Jon Krohn during October 2017 - December 2017. Overall it was exactly what I hoped it would be. It gave me a strong foundation of all the core deep learning concepts. The class was hosted on every other Saturday which allowed enough time to fully explore a particular topic between classes. Jon made a particular effort to keep the material simple and explained the concepts intuitively rather than with complicated math. Jon has great understanding of the content and is well prepared for each lesson. I thoroughly enjoyed the class and it has motivated me to pivot my career into deep learning. I would recommend this class to anyone who is passionate about deep learning but don't know where to begin. It is also great for anyone who has worked with some but not all of the deep learning techniques.

Date and Time

October Session Early-bird Pricing!

Oct 19 - Dec 7, 2019, 11:00am-5:00pm
Day 1: October 19, 2019
Day 2: October 26, 2019
Day 3: November 16, 2019
Day 4: November 23, 2019
Day 5: December 7, 2019
$2990.00$2840.50
Register before Sep 19th to take advantage of this price!
Add to Cart