Where to start with Data Science
There is now way to be taught to be data scientist, but you can learn how to become one yourself. There is no right way, but there is a way, which was adopted by a number of data scientists and it goes through online courses (MOOC). Following suggested order is not required, but might be helpful.
Best resources to study Data Science /Machine Learning
1. Andrew Ng’s Machine Learning (https://www.coursera.org/learn/machine-learning).
2. Geoffrey Hinton’s Neural Networks for Machine Learning (https://www.coursera.org/learn/neural-networks).
3. Probabilistic Graphical Models specialisation on Coursera from Stanford (https://www.coursera.org/specializations/probabilistic-graphical-models).
4. Learning from data by Caltech (https://work.caltech.edu/telecourse.html).
5. CS229 from Stanford by Andrew Ng (http://cs229.stanford.edu/materials.html)
6. CS224d: Deep Learning for Natural Language Processing from Stanford (http://cs224d.stanford.edu/syllabus.html).
7. CS231n: Convolutional Neural Networks for Visual Recognition from Stanford (http://cs231n.stanford.edu/syllabus.html).
8. Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville (http://www.deeplearningbook.org/).
9. Machine Learning Yearning by Andrew Ng (http://www.mlyearning.org/).
#books #wheretostart #mooc
There is now way to be taught to be data scientist, but you can learn how to become one yourself. There is no right way, but there is a way, which was adopted by a number of data scientists and it goes through online courses (MOOC). Following suggested order is not required, but might be helpful.
Best resources to study Data Science /Machine Learning
1. Andrew Ng’s Machine Learning (https://www.coursera.org/learn/machine-learning).
2. Geoffrey Hinton’s Neural Networks for Machine Learning (https://www.coursera.org/learn/neural-networks).
3. Probabilistic Graphical Models specialisation on Coursera from Stanford (https://www.coursera.org/specializations/probabilistic-graphical-models).
4. Learning from data by Caltech (https://work.caltech.edu/telecourse.html).
5. CS229 from Stanford by Andrew Ng (http://cs229.stanford.edu/materials.html)
6. CS224d: Deep Learning for Natural Language Processing from Stanford (http://cs224d.stanford.edu/syllabus.html).
7. CS231n: Convolutional Neural Networks for Visual Recognition from Stanford (http://cs231n.stanford.edu/syllabus.html).
8. Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville (http://www.deeplearningbook.org/).
9. Machine Learning Yearning by Andrew Ng (http://www.mlyearning.org/).
#books #wheretostart #mooc
Coursera
Probabilistic Graphical Models
Offered by Stanford University. Probabilistic Graphical ... Enroll for free.
Outdated, but still valuable blog to dive into deep learning:
http://deeplearning.net/tutorial/
#tutorial #wheretostart
http://deeplearning.net/tutorial/
#tutorial #wheretostart
Upcoming series of #ML lectures from Columbia Universite will be published on youtube.
If you are looking #wheretostart, this is one of the great places.
YouTuve Playlist: https://www.youtube.com/playlist?list=PL_pVmAaAnxIQGzQS2oI3OWEPT-dpmwTfA
Syllabus: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/
#beginner #novice #entrylevel
If you are looking #wheretostart, this is one of the great places.
YouTuve Playlist: https://www.youtube.com/playlist?list=PL_pVmAaAnxIQGzQS2oI3OWEPT-dpmwTfA
Syllabus: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/
#beginner #novice #entrylevel
YouTube
Applied Machine Learning - Spring 2019
Share your videos with friends, family, and the world
Great collections of Data Science learning materials
The list includes free books and online courses on range of DS-related disciplines:
Machine learning (#ML)
Deep Learning (#DL)
Reinforcement learning (#RL)
#NLP
Tutorials on #Keras, #Tensorflow, #Torch, #PyTorch, #Theano
Notable researchers, papers and even #datasets. It is a great place to start reviewing your knowledge or learning something new.
Link: https://hackmd.io/@chanderA/aiguide
#wheretostart #entrylevel #novice #studycontent #studymaterials #books #MOOC #meta
The list includes free books and online courses on range of DS-related disciplines:
Machine learning (#ML)
Deep Learning (#DL)
Reinforcement learning (#RL)
#NLP
Tutorials on #Keras, #Tensorflow, #Torch, #PyTorch, #Theano
Notable researchers, papers and even #datasets. It is a great place to start reviewing your knowledge or learning something new.
Link: https://hackmd.io/@chanderA/aiguide
#wheretostart #entrylevel #novice #studycontent #studymaterials #books #MOOC #meta
🎓CS224W: Machine Learning with Graphs
Great course from #Stanford. You still on time to jump at studying from one of the best schools.
Students are introduced to machine learning techniques and data mining tools apt to reveal insights on the social, technological, and natural worlds, by means of studying their underlying network structure and interconnections.
Topics include: robustness and fragility of food webs and financial markets; algorithms for the World Wide Web; graph neural networks and representation learning; identification of functional modules in biological networks; disease outbreak detection.
Link: http://cs224w.stanford.edu
Videos link: http://snap.stanford.edu/class/cs224w-videos-2019/
#MOOC #entrylevel #wheretostart
Great course from #Stanford. You still on time to jump at studying from one of the best schools.
Students are introduced to machine learning techniques and data mining tools apt to reveal insights on the social, technological, and natural worlds, by means of studying their underlying network structure and interconnections.
Topics include: robustness and fragility of food webs and financial markets; algorithms for the World Wide Web; graph neural networks and representation learning; identification of functional modules in biological networks; disease outbreak detection.
Link: http://cs224w.stanford.edu
Videos link: http://snap.stanford.edu/class/cs224w-videos-2019/
#MOOC #entrylevel #wheretostart
GANs from Scratch 1: A deep introduction.
Great introduction and tutorial. With code in PyTorch and TensorFlow
Link: https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f
#tensorflow #pytorch #GAN #tutorial #entrylevel #novice #wheretostart
Great introduction and tutorial. With code in PyTorch and TensorFlow
Link: https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f
#tensorflow #pytorch #GAN #tutorial #entrylevel #novice #wheretostart
Classification and Loss Evaluation - Softmax and Cross Entropy Loss
Nice notes on softmax cross entropy loss and how to implement it in numpy.
Link: https://deepnotes.io/softmax-crossentropy
#nn #entrylevel #wheretostart
Nice notes on softmax cross entropy loss and how to implement it in numpy.
Link: https://deepnotes.io/softmax-crossentropy
#nn #entrylevel #wheretostart
Parasdahal
Softmax and Cross Entropy Loss
Understanding the intuition and maths behind softmax and the cross entropy loss - the ubiquitous combination in classification algorithms.
Big scandal on popular YouTuber ML course
Siraj Raval, who raised his audience on devliering various YouTube videos, explaning #ML and #DL concepts as long with interviews with leading persons, launched his own course, but failed to provide much value.
His course was built on open and free tutorials, created by passionate enthusiasts, but he failed to attribute them properly and charged money for ununique content without any proper support for students.
He also oversold his course and tried to hide that from students, claiming to provide personal feedbacks, but failing to do so due to being too greedy.
Most of the best online courses and study programms are available online and for free, including those, we gathered here on our channel and attributed with hastags #wheretostart #entrylevel #MOOC #tutorial. Feel free to click these hashtags and browse for best available resources to start learning data science.
Link: https://www.theregister.co.uk/AMP/2019/09/27/youtube_ai_star
Siraj Raval, who raised his audience on devliering various YouTube videos, explaning #ML and #DL concepts as long with interviews with leading persons, launched his own course, but failed to provide much value.
His course was built on open and free tutorials, created by passionate enthusiasts, but he failed to attribute them properly and charged money for ununique content without any proper support for students.
He also oversold his course and tried to hide that from students, claiming to provide personal feedbacks, but failing to do so due to being too greedy.
Most of the best online courses and study programms are available online and for free, including those, we gathered here on our channel and attributed with hastags #wheretostart #entrylevel #MOOC #tutorial. Feel free to click these hashtags and browse for best available resources to start learning data science.
Link: https://www.theregister.co.uk/AMP/2019/09/27/youtube_ai_star
www.theregister.co.uk
YouTuber charged loads of fans $199 for shoddy machine-learning course that copy-pasted other people's GitHub code
Oh, and there wasn't a refund policy until folk complained
Data Science by ODS.ai 🦜
🔥🔥🔥Tomorrow we will hold an AMA session with Alexey Moiseenkov — ex-founder of #Prisma app (2016), which made neural networks popular and commodity nowadays. Now he works on #Capture app, bringing power of visual search in attempt to revolutionize messagers…
AMA today at 15:00 GMT (in 4 hours). In a couple of hours we will publish link to private chat for AMA session.
Stay tuned, prepare your questions. Please do not ask trivial and gramatically incorrect questions like 'where to start data science'.
First of all, use search, we have nice collections of resources for starting a DS career, tagged with #wheretostart #entrylevel #novice. Secondly, pay respect to our guest and ask questions more relevant to his area of experise.
Stay tuned, prepare your questions. Please do not ask trivial and gramatically incorrect questions like 'where to start data science'.
First of all, use search, we have nice collections of resources for starting a DS career, tagged with #wheretostart #entrylevel #novice. Secondly, pay respect to our guest and ask questions more relevant to his area of experise.
Simple comic on how #ML works from #Google
Make sure you save the link (or this message) to show it to people without great technical background for it is one of the best and clear explanations there is.
Link: https://cloud.google.com/products/ai/ml-comic-1/
#wheretostart #entrylevel #novice #explainingtochildren
Make sure you save the link (or this message) to show it to people without great technical background for it is one of the best and clear explanations there is.
Link: https://cloud.google.com/products/ai/ml-comic-1/
#wheretostart #entrylevel #novice #explainingtochildren
Google Cloud
Learning Machine Learning | Cloud AI | Google Cloud
Machine Learning Comic
All the vector algebra you need for understanding neural networks
Article contains great explanations and description of matrix calculus you need to know and understand to really grok neural networks.
Link: https://explained.ai/matrix-calculus/index.html
#WhereToStart #entrylevel #novice #base #DL #nn
Article contains great explanations and description of matrix calculus you need to know and understand to really grok neural networks.
Link: https://explained.ai/matrix-calculus/index.html
#WhereToStart #entrylevel #novice #base #DL #nn
explained.ai
The Matrix Calculus You Need For Deep Learning
Most of us last saw calculus in school, but derivatives are a critical part of machine learning, particularly deep neural networks, which are trained by optimizing a loss function. This article is an attempt to explain all the matrix calculus you need in…
Free eBook from Stanford: Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares
Base material you need to understand how neural networks and other #ML algorithms work.
Link: https://web.stanford.edu/~boyd/vmls/
#Stanford #MOOC #WhereToStart #free #ebook #algebra #linalg #NN
Base material you need to understand how neural networks and other #ML algorithms work.
Link: https://web.stanford.edu/~boyd/vmls/
#Stanford #MOOC #WhereToStart #free #ebook #algebra #linalg #NN
Soon we will give a try to certain solution which will allow commenting on the posts in this channel.
Therefore, we will at first release the Ultimate Post on #wheretostart with Data Science, describing various entry points, books and courses. We want to provide extensive and thorough manual (just check out the name we chose), so we would be grateful if you can submit any resourses on getting starting with DS (any sphere) through our bot @opendatasciencebot (make sure you add your username, so we can reach you back)
You are most welcome to share:
Favourite books, youtube playlists, courses or even success stories.
Therefore, we will at first release the Ultimate Post on #wheretostart with Data Science, describing various entry points, books and courses. We want to provide extensive and thorough manual (just check out the name we chose), so we would be grateful if you can submit any resourses on getting starting with DS (any sphere) through our bot @opendatasciencebot (make sure you add your username, so we can reach you back)
You are most welcome to share:
Favourite books, youtube playlists, courses or even success stories.