Deep Learning for Developers with Tensorflow (Cohort 7)

Student Reviews
In this course you will learn the fundamentals of Deep Learning primarily through a series of hands on exercises guided by the instructor. Students will learn about the foundational underpinnings of machine learning and deep learning as well as how to put that knowledge to the test with practical exercises.
The course takes project focused approach to teach you deep learning by building deep learning models with Tensorflow 2.x. The instructor will walk you through a series of curated projects, and explain the key concepts as they arise. Students will learn the theory and how these models work under the hood while writing code, and building neural networks.
The course will balance learning theory and working on projects. Students who take this course will be able to:
  • Identify and frame problems that can be solved by deep learning
  • Choose the right techniques to the problems
  • Understand key deep learning concepts and how deep learning models work
  • Build deep neural nets for classification and regression
  • Build CNN for computer vision and object detection with Tensorflow
  • Applying neural networks for NLP using TensorFlow
  • Build solid knowledge and skills for Tensorflow developer certificate, Link
    • Session 1: Jan 12, 10am~12pm PST (US Pacific, GMT-8)
    • Session 2: Jan 14, 10am~12pm PST
    • Session 3: Jan 19, 10am~12pm PST
    • Session 4: Jan 21, 10am~12pm PST
    • Session 5: Jan 26, 10am~12pm PST
    • Session 6: Jan 28, 10am~12pm PST
    • Session 7: Feb 2, 10am~12pm PST
    • Session 8: Feb 4, 10am~12pm PST

    • 16 hours/ 8 sessions
    • 10 lectures / 16 hands-on code labs
    • Live Sessions with Zoom, Real time interaction
    • Slack support in and after class
    • Capstone project, Github portfolio
    • One-year access to course materials

    Check the content tab for full course outlines.

    Developers, data scientists, students who want to get started on building deep learning projects or applications.

  • Familiarity with Python, or willingness to learn it quickly
  • Basic familiarity with statistics and probability theory is a plus, but not required
  • Comfort with the command line a plus
  • Beginner~Intermediate
  • Est. time spend per week: 4 hours live class (required) + 4 hours homework (required) + 4 hours projects (bonus, optional).
    Full refund upon request before the first session ends (Jan 4th, 2021 12:00pm PST). 5% transaction fee is not refundable.

    If miss the live sessions, you can watch recorded sessions any time, along with interactive learning tools, slides, course notes

  • Certificate of completing course
  • Scholarship is available, contact us for applications
  • Cohort 6: Aug 24, 2020 ~ Sep 16, 2020
  • Cohort 5: Apr 28, 2019 ~ May 21, 2020
  • Cohort 4: Jan 7, 2019 ~ Jan 30, 2020
  • Cohort 3: Oct 15, 2019 ~ Nov 7, 2019
  • Cohort 2: Aug 20, 2019 ~ Aug 21, 2019
  • Cohort 1: May 14, 2019 ~ Jun 6, 2019
  • Module 1: Deep Learning & Neural Network Fundamentals
    • Deep learning applications, key concepts and terminology
    • Neural network architecture and theory
    • Code lab 1: Building and training a neural network.
    • Code lab 2: Exploring different network architectures

    Module 2: Deep Neural Networks in Detail
    • Identify tasks that are well and ill suited to deep learning
    • Underfitting and overfitting, Regularization tactics
    • Feature engineering, Optimizers and training parameters
    • Code Lab 3: Exploring common activation functions
    • Code lab 4: Exploring common loss functions
    • Code lab 5: Exploring common optimizers
    • Code lab 6: Underfitting, overfitting, and regularization tactics

    Module 3: Data Cleaning and Preprocessing
    • Define and identify common problems with training data
    • Describe and apply tactics for improving data quality and improve network performance
    • Evaluate the appropriateness of applying said tactics.
    • Code Lab 7: Clean up a messy dataset
    • Code Lab 8: Improve a dataset with synthetic data and augmentation

    Module 4: Convolutional neural networks for classification and segmentation
    • Identify tasks for which CNNs are well suited
    • Compare and contrast classification with segmentation
    • Build CNNs from scratch using Keras and evaluate their performance
    • Code lab 9: Build a CNN from scratch.
    • Code lab 10: Import and use well known pre-trained CNN architectures.

    Module 5: Convolutional Neural Networks and Transfer Learning
    • Transfer learning and identify examples of situations where it might help
    • Import well known CNN architectures and leverage transfer learning using Keras
    • Code Lab 11: Transfer learning with the Cifar100 dataset
    • Code Lab 12: A complete CNN pipeline with transfer learning

    Module 6: Object Localization and Image Segmentation
    • Define object localization and image segmentation and what they’re used for
    • Code lab 13: implement object localization
    • Code lab 14: implement image segmentation

    Module 7: RNN and NLP
    • Define RNN and what they’re used for
    • Build natural language processing systems using TensorFlow
    • Use word embeddings, LSTM in you models
    • Code lab 15: implement a text classification using Tensorflow

    Module 8: ML Life Cycle and Tools For Production Systems
    • Define the product life cycle for ML products.
    • Save and restore models with Tensorflow after training.
    • Use callbacks to save checkpoints during training, create more detailed logs, and other custom behavior
    • Create custom metrics for more informative training output.
    Tyler Bettilyon

    Tyler is an educator, technologist, programmer, and all around curious human. He holds a bachelor’s degree in computer science and completed his MBA by counter example in San Francisco’s startup scene. Looking out from inside the Bay Area Bubble he realized that the world is not prepared for the future that technology is bringing. He is now focused on technology education, outreach, and policy
    (261 Ratings)

    Student Feedback


    One of the best courses I've ever taken. I like how it is set up. There is a lot of useful reading that helps with understanding the conceptual ideas in more depth. (Dan H. from Class 20200824)

    I really liked the course. This was my first exposure to ML but I think I learned a lot about how neural networks works and how to train them to do useful things. (Lindsay A. from Class 20200824)

    Course instructor is very knowledgeable about the Deep Learning subject. His teaching skill is also excellent. (Pragnesh P. from Class 20200824)

    One of the best course for developers to practically learn and implement deep learning. (Peri R. from Class 20200824)

    I certainly enjoyed the class. Tyler is a knowledgable, articulate and friendly instructor and he presented well-organized, relevant content. (Class 20200609)

    Thanks for not talking too fast, for not glossing over complex ideas, for all the great links, and for your calming demeanor. (Class 20200609)

    Good coverage and tutor. Love instructor's online teaching style (Class 20200609)

    this course is very helpful and structured by Tyler so we can understand the pace and learn a lot. looking forward for future courses from Tyler. (Class 20200609)

    I really enjoyed this camp course, I learned so much about Python and Machine Learning. The examples we did in class and the homework assignments were all super helpful and writing code for a Kaggle challenge was a great experience. I am excited to take more courses from AICamp in the future. (Class 20200609)

    Extremely helpful and a great instructor, and very relatable to the audience. Good at explaining concepts. (Class 20200609)

    The course is well structured - with good mix of instructor teaching & self learning.. (Class 20200609)

    Great course. 100% enjoyed it. (Class 20200609)

    I learned a lot during this class. The lecture and the amount of homework was perfect for learning. (Class 20200609)

    This course gave my an opportunity to confidently solve deep learning problems on my own. This instructor was great, attentive, responsive, and provided excellent education on Deep Learning. (Class 20200428)

    Several topics were covered only in this course, and were very helpful for my deep learning project. (Class 20200428)

    Compared to other courses I have had, this is well worth the money (Class 20200428)

    I thought the lecture format using a Zoom call and meeting twice per week was a good way to cover the material. (Class 20200428)

    The hands-on experience with the Jupyter notebooks, and the instructor's guidance on what tools are applicable for specific scenarios (Class 20200428)

    Clear explanations of deep learning concepts and hands-on. (Class 20200428)

    The GitHub url with the follow-up knowledge - so that I can continue to expand (Class 20200107)

    The jupyter notebooks are self explanatory and the video along with notebooks are extremely effective... (Class 20200107)

    Like the hands-on exercises (Class 20200107)

    This has been one of the best series Ive attended. Thanks so MUCH! (Class 20200107)

    Thank you Tyler. I enjoyed and learnt a lot. Particularly liked the intuition on different methodologies (Class 20200107)

    I’m happy with the course material and enjoyed the engagement and enthusiasm of the instructor. He has a lot of good quality open source learning material available and I’m glad I came across this content. (Class 20200107)

    Thank you.. this was a lot of fun. (Class 20191015)

    good class, very well done (Class 20191015)

    really enjoy the interaction of the course, ask questions and discuss with the instructor during the class. (Class 20191015)

    the coding labs are well designed and help me getting hands-on experience besides the lecture. (Class 20191015)

    great course, enjoy the 4 weeks journey. (Class 20190514)

    • Start Date: Jan 12, 10:00PST | Tue,Thu
    • Venue: Online (zoom)
    • Fee:
      $299 $249 USD
    • Status: live now
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