Meet the deeplearning.ai team!
I’m Aziz and I recently joined the deeplearning.ai team as a Product Manager. I love the power of tech and entrepreneurship as a force to solve the world’s greatest challenges. I believe education is the great equalizer and am committed to making AI more accessible to people from all walks of life. My academic background is in electrical engineering and business, and this allows me to think about customer pain points and their solutions. So excited to see how we can, together, transform an AI-powered future.
Feel free to reach out!
Hi everyone, I’m Kian - I co-created the Deep Learning Specialization with Prof. Ng and Younes Bensouda Mourri. I’m a Lecturer of Computer Science at Stanford, where I co-teach the “Deep Learning” (CS230) class with Prof. Ng. I previously completed my masters in Management Science & Engineering at Stanford where I worked with Prof. Ng (ML) and Prof. Boneh (Crypto.) Prior to that, I attended École Centrale Paris where I got a masters in General Engineering. I grew up in Paris and love football (a.k.a. soccer), rap and languages.
Happy to meet you!
Hi @Kian. Quick question. A goal of mine is to be able to work in a job that is focused on research in Deep Learning. This seems like a difficult task in that my background is primarily in biomedical sciences with no formal computer science training. Do you think it is necessary to have a formal computer science education to contribute to Deep Learning research teams? Also what are your favorite 3 papers on Deep Learning (or the most recent 3 papers you've read)?
There are many ways to contribute to Machine Learning research (or industry) teams. One is bringing knowledge from your field (BioMed Sciences) in addition to your ML training. For instance, doctors with ML training contribute significantly to ML teams to solve problems in Radiology, Cardiology and Palliative Care among others.
You can also contribute to a ML team outside your area of expertise as a Data Scientist or Machine Learning Engineer. In terms of what training would be required, I'd suggest to check out the list from our Machine Learning Engineer Career Program ( https://www.deeplearning.ai/careers/). This is quite extensive and you might not need to master everything perfectly to contribute in a ML team, but it will give you an idea:
- Deep learning. You should be able to understand and apply major deep learning methods, including neural network training, regularization, optimization methods (gradient descent, Adam), and be familiar with major neural network architecture types such as Convolutional Networks, RNN/LSTM. Completion of the deeplearning.ai specialization is sufficient to meet this criteria.
- Machine learning. You should be able to understand and apply major machine learning methods, such as logistic regression, SVM, Decision Trees, Principal Component Analysis and K-means. Completion of Andrew Ng’s Machine Learning course on Coursera is sufficient to meet this criteria.
- Implementation. You should have prior experience taking a dataset, cleaning it if necessary, and applying a learning algorithm to it to get a result. You should be able to implement a learning algorithm “from scratch” using a framework such as Tensorflow, Pytorch, Caffe, etc.
- General coding. You should be able to code non-trivial functions in object oriented programming, such as popular sorting or search algorithms. You should know how to use your terminal, and work with version control systems (Git). Software engineering experience such as working with relational databases, APIs, and building the back-end of web or mobile applications is helpful but is not required.
- Mathematics (including probabilities and statistics.) You should be able to use mathematical notations and linear algebra (matrix/vector operations, dot products, etc.), and understand basic probability theory (distributions, independence, density functions, etc.) as well as statistics (mean, variance, median, quantiles, co-variance, etc.)
I've tried to learn more about DL applied to Point Clouds recently, so the 3 most recent papers I've read are:
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (Qi, Su et al.)
- PointCNN (Li et al.)
- Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs (Landrieu & Simonovsky)
3 papers I like a lot:
- Neural Machine Translation by Jointly Learning to Align and Translate (Bahdanau et al.) if you'd like to learn more about Machine Translation.
- FaceNet: A Unified Embedding for Face Recognition and Clustering (Schroff et al.) if you'd like to learn more about Face Recognition.
- YOLO9000: Better, Faster, Stronger (Redmon et al.) if you'd like to learn more about Object Detection.
Hope it helps,
Awesome thank you for the response and the resources. That is a great point about leveraging my domain specific background to contribute to research teams. One of my favorite things about Deep Learning are the papers that are coming out and people willing to share their research with everyone. I'm looking forward to taking a look at these!
I'm Andrea, your resident Marketing Specialist at deeplearning.ai! I studied English Literature at Stanford, but with loads of CS friends and two software engineering parents, I ended up taking a couple programming and data science classes, which got me interested in AI. At deeplearning.ai, I get to combine my love of writing and storytelling with AI: we’re all invested in creating educational content that explains complex technical topics in an easy-to-understand way.
I'm helping to bring the Deep Learner community together so that everyone can connect, learn, and share knowledge. Let me know what kinds of events/platforms/programs you'd like to see from us - I'm all ears!
My name is Ortal Arel, and I’m the Content Development Manager at deeplearning.ai. I’ve always had a passion for math and teaching science and engineering. I received my PhD degree in computer engineering and shortly after began teaching undergraduate and graduate courses in physics, signals and systems, logic design, and computational modeling. My initial fascination with the world of AI started with my research in embedded systems. I worked on the design and analysis of intelligent algorithms for high speed digital architectures. Learning more about intelligent algorithms led to a passion for Machine Learning (ML) and Deep Learning. I’ve been increasingly amazed with all the ML applications being built to help better society, like those used in healthcare systems, agriculture, automated driving, and more. For the past two years I've been involved in developing content and delivering lessons for MOOCs in the field of AI and machine learning. My mission is to make our learners around the world feel that our lessons not only cover state-of-the-art AI material, but are also uniquely designed for them.
Let me know what kind of content you would like to see from us in the future! Happy to meet you all.
My name is Iris Cao and I am the Recruiting Specialist at deeplearning.ai. It’s really my honor to work with people who created the deeplearning.ai’s MOOCs and so glad to see the courses have helped myriads of learners break into AI. I received my Master’s degree in Human Resources and Industrial Relations and started my career in technical recruiting with a focus on hiring for engineers in the AI field.
I work at deeplearning.ai to help bring talented learners into our AI community and happy to provide career advice and connect with everyone! If you’re interested in attending deeplearning.ai’s AI Bootcamp, you can apply here: deeplearning.ai/bootcamp
My name is Saamahn and I recently completed Andrew Ng's Deep Learning Specialization Path on Coursera. This past year I've devoted myself almost entirely to Machine Learning, Deep Learning, and AI. I've almost finished Udacity's Deep Reinforcement Learning Nanodegree, started building ML apps in Flask, enrolled in Udemy courses on data science, machine learning, and deep learning, and read up on several research papers.
I'm interested in the AI Bootcamp offered by deeplearning.ai and was wondering what the steps would be to enroll in this program so that I may apply my knowledge in ML to real world projects (and later transition to working at a startup). Like I said before, I've started web development with Flask and am familiar with libraries such as Tensorflow and Pytorch, but am now ready to take things to the next level. Thanks!
I am Raj and I am aSoftware Engineer by profession and a tech enthusiast by heart where AI has a special place. I have been on somewhat a slow journey in AI with Master Andrew Ng's courses on Machine Learning and Deep Learning as my weapons. Currently I am equipped with most of the theoretical aspects of AI and am on the way to get my hands dirty with some industry level work. But my current role doesn't have any such requirements as of now and I am actively seeking opportuninties in AI. Lastly I would really thank the deeplearning.ai community for creating such a wonderful specialization on Coursera. This has really boosted up my confidence in the field. Looking forward to learning more from such a great bunch of people.
Thanks & Regards,
You may connect me on Linkedin.
Hi every one,
Let me say again. Hi the best humans in the world
My name is Jalil Nourmohammadi Khiarak ( https://www.amber-biometrics.eu/?page_id=262) ESR 5, I recently completed Andrew Ng's Deep Learning Specialization Path on Coursera. I should say the best way to learn AI is your courses on coursera.org or www.deeplearning.ai.
I am working on Presentation attack detection (PAD) on mobile devices based on Deep learning methods ( https://www.amber-biometrics.eu/?page_id=159). I would like to ask a question about deep methods. Which ones (deep methods) can help me to make a very accurate method for PAD on mobile devices?
Dear @Kian and other dears could you please let me know how I can achieve a good result?