How has your journey been so far, learners?

Deep learning has been an exciting and applied field and I am always interested to know the journeys fellow learners took to reach so far.

I actively started in/around April this year, when I started with the Udacity’s ML and AI nanodegrees. Done with the first and about to finish the second. In between I squeezed in an internship at a startup extracting information from drone taken aerial images. I don’t have a Master’s, and don’t have a CS background either. I am a self learner and am glad that this field is accomodative of people who can show results than earn college degrees.

Super excited to get live access to this course. I have had a couple of starts with MOOC earlier, but couldn’t continue it for other priorities. has a very hands on approach, and I am glad I am in this now. The live nature will ensure I complete it with others!

Would like to get connected on linkedin.

How has your journey been?



Hi everyone,

I started with around Feb this year, completed part 1 and then followed up with part 2 later in August. I also come from a non-CS background but I took my chances and left my job in May. Focused on learning hands on and explored all possible solutions on the Internet. part 1 gave me the necessary push and belief in self learning, after sitting back for 4 months and walking through parts and experimenting with all the giveaways, I recently got an offer in ML from Amazon. So a big thanks to Jeremy and Rachel!

On a side note, I’m hearing a lot about Udacity’s self-driving cars degree and certificates, can anyone provide a review? Please compare it to the learning curve presented with

I feel lucky getting access to this course and I’m looking forward for some great projects that will allow us to learn and explore cutting edge techniques and papers. I think my journey has just begun.

Looking forward to hear yours :slight_smile:



Hi @PranY,

Congratulations on getting that offer from Amazon! That was a bold move to quit. How was your interview experience?



To pull in my letter:

As the director of the Insider Threat program at AIG, I am creating a global program that prevents our data from being stolen. Most solutions involve monitoring different data and behaviors of employees to determine their risk to the company. There are many solutions sold by vendors that claim to have AI, machine learning, and other buzz words. However, I decided to get some hands-on experience to ensure that I could ask vendors intelligent questions to make sure these solutions are effective, don’t create a bias against minorities, and don’t take in private employee information that isn’t needed (most pull in too much).

I found the Fast.AI MOOC to be the most useful teaching module for my development. For nine weeks, I followed the MOOC schedule of a lesson a week. My python was a little rough, so I probably spent 20 hours a week instead of the recommended 10, but I got through it. The last two weeks culminated with an attempt at a Kaggle competition (Web Traffic). I have a post about it here.

While it has more than met my original objectives, this course has also changed my perspective on what I want to do. The insider threat is neat, but lesson 14 discussed data scientists working with pediatricians in the PICU to create better outcomes. My wife is a pediatrician, my father-in-law a pathologist, and I am constantly wondering how I can take what I have learned to apply it to their highly specialized work. In December I plan on starting the process with the University of Houston Texas Medical Center to get patient data to help diagnose kidney biopsies.

Very excited to get started soon!

Edit: I wanted to put some additional clarification since the tweet about this post went out, and a co-worker was asking if I was leaving the company. So some more information that I would prefer to stay in the private forums.

While I work at an insurance company, I am not involved with our insurance products (for now). I prevent employees from stealing company secrets and intellectual property. We conduct forensics on employees are doing strange things with company computers and assets. While completely legal and all employees agree to the monitoring, employees continue to steal from companies, and it is a problem many companies don’t want to talk about.

I have been trying to make this program better by evaluating 3rd party vendors. The first round of the MOOC helped me ask these vendors better questions about how they use machine learning and try to ensure we are not opening ourselves up to liability by selecting a solution that uses data unethically. For example, imagine using race as a possible feature for a model detecting criminal behavior. Terrifying. But, at least one vendor stated they use unsupervised learning on every field from HR data.

The kidney work is still pending. My father in law is a pathologist and has about five years of slides from his cases. This research would not replace my job at AIG as I need it to support my family. A fascinating classification problem, I am hoping not to run into too many hurdles with my day job and healthcare regulations.


Hello all -

I started looking at deep learning and found the courses in June of this year. I’ve started with a software background going on 20 years ago, though for the last 10 years, my wife and I have run a goat dairy and cheesemaking operation. Normally this runs us ragged from about January through October when we stop milking and making cheese, so I’m really looking forwards to our slower winter period to dig into deep learning via this course and a lot of reading.



Hey Cory our toddler just LOVEs goat milk :slight_smile:


Dear Fellows,

very excited to be part of the team!
I started with DL about half a year ago working on a contract project - using Keras and TF.
Have a decent sw background - developed a bunch of telco systems (C++/Java)
But succumbed to a siren call of so called New Electricity :slight_smile:
In particular i’m very interested in synthetic data generation, GANs and style transfter.



Hello All,

Really excited to be a part of the group. I am from electronics background. I work as a Design Manager with Texas Instruments in analog circuit design. I also founded the start up in food tech in 2015 where I did apply bit of ML. I started learning about DL few months ago. I am also in process of finishing ML nanodegree from udacity. Looking forward to improving my knowledge in DL trough this course and eventually, applying making use of it.



Your toddler has quite a refined taste :slight_smile:



I have a CS background with development experience for the last ~4 years. I wanted to pursue a career in Artificial Intelligence. After working through numerous blogs caught hold of I am yet to discover its true potential.

I completed the Data Analyst Nanodegree from Udacity and mentor students from all over the world for the same nanodegree. I work on mentoring part-time(for a living) and the rest of the time I keep learning.

I look forward to working with each one of you here. For people in India, we can work on some interesting projects and take the things we learn here to the next level. I believe there are a lot of problems to solve for us in the world and especially here in India.




Hah, glad to hear! :slight_smile:

We frequently encounter folks who have either a strong psychological bias against it, or who apparently have super-anti-goat-flavour-tastebuds; so it’s always nice to hear from the pro-goat folks!

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Hi All. My ML and later DL journeys started with Jeremy`s article dated Dec 2 2012 - Specialist Knowledge Is Useless and Unhelpful, when data prediction is a game, the experts lose out: I read this article, resigned, and started coding. Okay, not like that, but yeah, I started thinking about moving from strategical and business analysis into data science.
Looking forward to study with you, guys. @PranY story is amazing - everything is possible in this life. Non-CS, 4 month of hard work and … ta da!


Hello All !

I hope this finds you all doing well.

I am from Bengaluru, India and I ventured into data science and machine learning sometime three years ago (after a couple of years stint in Software Engineering at Oracle). I still remember using h2o to build my first neural net on CPU and lasagne / nolearn for GPU. Keras was open sourced during that time. Times have changed so much since then. Deep Learning has really taken off and is truly making it accessible to many of us. Over the last two years, I tried my hands on other online courses but nothing beats in terms of the pedagogy, material and most importantly the community ( & Twitter). I participated in the v1 MOOC, won a couple of ML challenges (more here: At the same time, I struggled quite a bit on certain topics since I couldn’t collaborate and learn with the community as much as I wanted to. The news about the v2 course really excited me and here I am looking forward to try, fail, repeat, learn in the process and most importantly have a lot of fun along the way.



I started down the machine learning path back in early July and somebody recommended as a good place to get up and running quickly. I have definitely learned a lot and I really like that Jeremy comes from an Entrepreneurial background and has a great attitude about teaching. There are so many real-world applications that this can be applied to and that is what excites me the most about machine learning. Everything from anomaly detection for cyber-security to recommendation engines for Amazon, there is an algorithm that can help. My hope is that I will be able to transfer some of the knowledge I learn into the company that I currently work for and hopefully reduce our accidents (transportation). I also am looking forward to taking data from and analyzing it to see if there are areas that could be improved if they knew what parts were inefficient. I’m looking forward to learning from you all and hopefully contributing something back as well if I have any knowledge that proves useful.


Hello everyone,

It feels great to be the part of this course. I am a non-CS student (so far) in my final year of engineering undergrad in electronics domain. I started working with some basic machine learning stuff about a year back and soon fell in love with it. So far, I have completed multiple courses on ML and python from coursera, udacity, udemy and NPTEL. Now I wish to enhance my knowledge in DL.
I hope this course will be a wonderful learning experience.
Would like to get connected on Linkedin:

Regards and Best Wishes


Hi guys,

To reiterate as most, very excited to be here. I have been working in the quantitative finance space for a couple years since finishing my undergrad. Over work, I figured I really love programming and have tried to pick up computer science concepts ever since.

It is awesome to see so many of you with working experience in machine learning here (I’d admit - a bit unnerving too!). Looking forward to learn from you all and build something cool!


Hi Guys,
Myself Prabhu from Bangalore,India. I am very excited to join the course and learn with you all the practical AI stuffs.
Feel free to connect with me on linkedin:

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Hey everyone :slight_smile:

I’m normally based in NYC, but at the end of August I left the web startup I was working at and now I’m taking a few months to explore SF and machine learning. My background is mainly in programming/web development/software engineering, particularly in things like functional programming, type systems, programming languages, etc. I used to work for The Recurse Center (, formerly known as Hacker School), so I also really enjoy teaching. I’ve been studying machine learning seriously for a few months now, and I love it! I studied physics in college, and it feels really nice to be doing some math again.

I’m excited about a whole bunch of machine learning things. I’m a pretty serious student of French (any French speakers in the class?), so I’m interested in NLP, RNNs, LSTMs, etc. I used to play quite a lot of online poker, so I’m also interested in reinforcement learning, AlphaGo, etc. I’m interested in (and currently pretty fuzzy about) what practical machine learning systems look like in production. I also care a lot about elegance and nice abstractions, so I’m excited about the proliferation of machine learning libraries like Gluon, PyTorch/’s own library, and Keras.

Looking forward to meeting everyone!


We won’t be covering those in this course. However we covered them in detail in part 2 of the previous course, and the content is still very current.


oh sure Jeremy, i totally understand - as long as i have a solid foundation i won’t have trouble to pick that stuff on my own :slight_smile:

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