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Go deep on AI/ML

The premium program for experienced engineers to level up in machine learning.

2 or 5 Weeks
Full or part-time
Live Online
Instructor-led
From the founders of

Become an architect of the future

Software engineers with machine learning skills are in high demand as companies race to train and deploy models.


At Deep Atlas, we equip software engineers with a first-principles understanding of ML from concept to production so they can build the future:

Natural Language

Self-Driving Cars

Healthcare

Robotics

Generative AI

There is much to learn

The AI/ML landscape is complex and rapidly evolving. Without a guide, it's easy to get lost in the forest of tools and terms.


To build ML pipelines you'll need to acquire rarified skills. In class, we cover:

Data processing

Compile, clean, and optimize data for training.

Model construction

Select, train, and evaluate models.

Model deployment

Productionize models for consumption.

THE ML ECOSYSTEM

Superb peers and curriculum

Join talented peers and go deep on machine learning. Designed specifically for experienced software engineers.

Our curriculum is a tour de force of knowledge compression: optimally sequenced for maximum efficiency.

Cohort Schedule

Class Schedule (Central Time)

Full-Time, 2 Weeks (Online)Mon - Fri, 9:30am - 8:00pm
Part-Time, 5 Weeks (Online)Fri - Sat, 9:30am - 5:30pm
Mon - Wed, 6:30pm - 8:00pm

After completion, you'll be able to:

Explain with technical precision the training and inference processes of various ML models and algorithms, including Large Language Models like ChatGPT.
Recognize which problems can be solved using machine learning, and compare the benefits and drawbacks of simpler (shallow) versus more complex (deep) approaches.
Use Retrieval-Augmented Generation (RAG) to dynamically integrate external knowledge into Large Language Model inference.
Apply Prompt Engineering techniques to systematically guide Large Language Models for maximum performance.
Build intelligent agents on top of Large Language Models that can provide context-aware responses and take real-world action.
Apply Transfer Learning and Fine-Tuning methods to make general-purpose models work for specific tasks more effectively, using techniques like LoRA.
Contribute to end-to-end model development: from data cleaning and feature engineering to training, evaluation, and deployment.
Understand and experiment with various neural network architectures (like GANs, Transformers, RNNs, LSTMs, CNNs, and AEs) to solve different classes of problems.

After completion, you'll be able to:

Explain with technical precision the training and inference processes of various ML models and algorithms, including Large Language Models like ChatGPT.
Recognize which problems can be solved using machine learning, and compare the benefits and drawbacks of simpler (shallow) versus more complex (deep) approaches.
Use Retrieval-Augmented Generation (RAG) to dynamically integrate external knowledge into Large Language Model inference.
Apply Prompt Engineering techniques to systematically guide Large Language Models for maximum performance.
Build intelligent agents on top of Large Language Models that can provide context-aware responses and take real-world action.
Apply Transfer Learning and Fine-Tuning methods to make general-purpose models work for specific tasks more effectively, using techniques like LoRA.
Contribute to end-to-end model development: from data cleaning and feature engineering to training, evaluation, and deployment.
Understand and experiment with various neural network architectures (like GANs, Transformers, RNNs, LSTMs, CNNs, and AEs) to solve different classes of problems.

What students are saying

Demystifying AI

We are a multidisciplinary team of engineering and education experts. We live at the edge of emerging technology and thrive on making it legible for others.

Marcus Phillips

Sr. Engineer atTwitter

Joseph Martin

Instructor atHack Reactor

Anthony Phillips

Founder atHack Reactor

Tyler Lambe

President atGalvanize

Advisors & Guest Speakers

Rachel Hovde
Stacey Svetlichnaya
John Dvorak
Andrew Lee
William Huang

Speak truth to hype

In recent years, machine learning has changed our expectations of what is possible with computers. The rate of acceleration is dizzying.


Equip yourself to join the conversation that's shaping the world.


The latest beats in AI

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May 13th, 2024

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Meta Announces Llama 3

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Llama 3 demonstrates SOTA performance on many industry benchmarks and is now the most powerful open-source model.

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Anthropic Announces Claude 3

March 4th, 2024

The Claude 3 Opus model outperforms its peers on most common evaluation benchmarks for AI systems.

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OpenAI Unveils Sora

February 15th, 2024

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Google Announces AlphaGeometry

January 17th, 2024

AlphaGeometry's language model guides its symbolic deduction engine towards likely solutions to geometry problems.

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Google Announces AutoRT

January 4th, 2024

AutoRT is a system that harnesses large foundation models to create robots that can understand practical human goals.

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Intel debuts new chips to power AI

December 14th, 2023

Intel highlights its expansive AI footprint and future plans, including the introduction of the Intel Gaudi3 AI accelerator next year.

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Meta Announces Purple Llama

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The Purple Llama project from Meta AI provides cybersecurity and input/output filtering tools to help developers deploy generative AI responsibly.

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Google Announces Gemini

December 6th, 2023

Gemini is a new SOTA model built from the ground up for multimodality — reasoning seamlessly across text, images, video, audio, and code.

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Google Announces GNoME

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GNoME discovered millions of new materials with deep learning. It dramatically increases the speed and efficiency of discovery by predicting the stability of new materials.

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Google Announces GraphCast

November 14th, 2023

GraphCast is a state-of-the-art AI model built with Graph Neural Nets and is able to make medium-range weather forecasts with unprecedented accuracy.

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OpenAI Announces GPTs

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GPTs are customizable instances of ChatGPT which can be created for specific purposes and published for use by others.

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Frequently Asked Questions

Who is the course designed for?

We built the course for professional software engineers who want to pick up ML. The typical applicant has 5+ years of software engineering experience, though applicants from other disciplines (e.g. data scientists, hardware engineers, researchers) are also welcome.

What are the outcomes from the course?

The majority of students return to their engineering jobs with a newfound capacity to contribute to AI/ML initiatives at their company. Others go on to work at AI companies in SWE roles, and in some cases, continue their studies to recruit for dedicated machine learning roles.

We'll launch you into the equivalent of Low Earth Orbit by accelerating you through the thickest layers of the machine learning atmosphere. After completing our course, the possibilities are endless.

Why is the course worth my time and money?

We carefully examined the AI/ML field and distilled the essential knowledge required to be a productive contributor. Our curriculum will guide you in the most time-efficient manner to a foundational knowledge of machine learning. Other paid courses and free resources do not provide a straight path to productivity or a community of like-minded peers for you to immerse with.

Alumni of our program often report that the highest density of value comes from (a) skipping the inefficiencies of self-study, (b) working intimately with like-minded peers, and (c) receiving on-demand help from knowledgable instructors.

What if I have some ML experience?

Great! The students who succeed wildly in our course are curious tinkerers that have done some self-study. While no prior experience with ML is required to join, you will enjoy pushing the boundaries of our curriculum if you have prior experience.

Will my employer pay the tuition?

Most likely, yes. About half of our students receive some assistance from their employer to cover the cost of the course. We recommend proceeding with your application; we can help you through the process to receive reimbursement.

Are there math prerequisites?

Yes, however, we have prepared resources for you to get up to speed on the math necessary to succeed in the course. We will share ~30-50 hours of materials for you to complete prior to beginning the course which will help you build the necessary foundation. Prior experience with linear algebra, calculus, and statistics is a huge plus.