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Maching Learning Study Buddy
23rd July, 2024
Last date to join: 23rd August, 2024
Hey I've been working as a software engineer for around 10 years. I recently started learning ML from coursera (Deep Learning Specialization). I've almost completed it, but since its a more theoretical course than practical, I am looking for a study buddy to learn more practical stuff like maybe a hard level course or solving Kaggle Challenges or reading research papers. It can also divert a bit towards ML Ops, we can discuss all this. I am honestly not sure of the road ahead but I do know that these last few months have been fun. So if you are someone who is interested in learning this together, hit me up. I don't think someone new to Computer Science would be a good match for me, so someone either experienced or doing MS might be a good match.
Expert
English
Description
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia. Applied Learning Project By the end you’ll be able to: • Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications • Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow • Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning • Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data • Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering Read more
Deep Learning Specialization
Microcredentials 
Coursera
Free to Audit 
3 months at 10 hours a week 
Intermediate 
Paid Certificate Get notified about new study groups every week!
Maching Learning Study Buddy
23rd July, 2024
Last date to join: 23rd August, 2024
Affiliate notice
Hey I've been working as a software engineer for around 10 years. I recently started learning ML from coursera (Deep Learning Specialization). I've almost completed it, but since its a more theoretical course than practical, I am looking for a study buddy to learn more practical stuff like maybe a hard level course or solving Kaggle Challenges or reading research papers. It can also divert a bit towards ML Ops, we can discuss all this. I am honestly not sure of the road ahead but I do know that these last few months have been fun. So if you are someone who is interested in learning this together, hit me up. I don't think someone new to Computer Science would be a good match for me, so someone either experienced or doing MS might be a good match.
Expert
English
-
TypeMicrocredentials
-
ProviderCoursera
-
PricingFree to Audit
-
Duration3 months at 10 hours a week
-
DifficultyIntermediate
-
CertificatePaid Certificate
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.
In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.
AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.
Applied Learning Project
By the end you’ll be able to:
• Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications
• Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow
• Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning
• Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data
• Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering
Read more
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Frequently asked questions
Yes. Our study groups (all of them) are free to join
You join the group and study the MOOC together on a schedule. The exact dates, deadlines, are created by the host
This depends on the host of your group. Some groups have weekly video calls for accountability + doubt solving.
Moocable is a community where you can find study partners, mentors, or people to collaborate on projects. It's designed for people who want to upskill, but struggle with self-learning. Users often post about their skills, goals, and what they're looking to learn or work on, and others can respond to form partnerships or groups. You can join our community
23rd August, 2024
Get notified about new study groups every week!
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