Deep learning sneak peek

I try to explain various important terms of deep learning and machine learning. I will write this sort of tutorial for helping myself to build a clear understanding. If anyone get helped reading this It would be grateful for me

Deep learning (Basics)

Deep learning (Computer vision)

Audio

Deep learning (NLP)

ML Ops

ML data visualization

• https://github.com/PAIR-code/facets
• Tensorboard guide for tensorflow 1.x

CNN

• Machine Learning Foundations by google developer playlist
• Implement various CNN youtube playlist by MIT
• CNN Architectures - implementations | MLT

RNN

• A friendly introduction to Recurrent Neural Networks
• The Attention Mechanism
• attention-mechanism

Transformers

• The Illustrated Transformer

The loss functions

• Transducer
• Connectionist Temporal Classification (CTC)

NLP

• Building models with tf.text (TF World '19)
• Natural Language Processing (NLP) Zero to Hero - Play list

Datasets

• Google dataset search
• The Big Bad NLP Database

Courses

• Intro to tensorflow for deeplearning (Udacity)
• Full stack deeplearning
• Software engineering with ML (udacity)
• Machine Learning Crash Course by Google
• TensorFlow, Keras and deep learning, without a PhD
• Deep learing by stanford - CS230
• MIT deep learning lecture
• Intro to deep learning kaggle mini course
• Kaggle free data science course

Bangla ML/DL resource:

• হাতেকলমে পাইথন ডিপ লার্নিং
• সহজ বাংলায় 'বাংলা' ন্যাচারাল ল্যাঙ্গুয়েজ প্রসেসিং (এনএলপি)
• বাংলায় মেশিন লার্নিং
• ডিপ লার্নিং ও আর্টিফিশিয়াল নিউরাল নেটওয়ার্ক
• বাংলায় ব্যাসিক ডাটা সায়েন্স শেখার কোর্স

Hyper params tunes

• Effect of batch size on training dynamics
• Determining optimal batch size
• Hyperparameter Importance | PyTorch Developer Day 2020

Machine learning in production

• Rules of ML by google
• 5 Steps to take your model in production
• Building the Software 2 0 Stack (Andrej Karpathy)
• A Recipe for Training Neural Networks (Andrej Karpathy)
• Engineering Practices for Software 2.0 (PyTorch Developer Conference)
• Machine Learning Interviews: Lessons from Both Sides - FSDL
• Troubleshooting Deep Neural Networks

• Machine Learning System Design (Chip Huyen)
• Machine Learning Systems Design (Chip Huyen) CS 329S
• ML Interview book (chip Huyen)
• Real Time Machine Learning Challenges and Solution (Chip Huyen)
• Data Distributio Shifts and monittorig (Chip Huyen)

Miscellaneous

• data-science-with-dl-nlp-advanced-techniques
• 37-reasons-why-your-neural-network-is-not-working
• https://cs231n.github.io/
• tf-keras-rnn-ctc-example
• Keras debugging tips
• TF2 mixed presition training speed up
• Effect of activation fucntion
• tf2 object detection api
• Eat tensorlfow in 30 days
• Tensorflow examples
• TensorFlow Fall 2020 updates: Keynote & what’s new since TF2.2
• Explainable Ai
• The NLP Index
• Machine Learning Glossary
• Beam search, How it works
• Nvidia tao toolkit for developerhttps://developer.nvidia.com/tao-toolkit

Books

• Deep Learning with Python (Chollet) 2nd Edition
• Transfer Learning for NLP (Paul Azunre)
• Speech and Language Processing by Daniel Jurafsky
• Foundations of Statistical Natural Language Processing
• Dive into deep learning
• Natural Language Processing with Transformers
• Designing Machine Learning Systems