coursera-Tensorflow

A collection of Courses, bookmarks, resources, articles for Machine Learning Beginners.

exercises from "Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning"

Feel free to add something interesting by pull request.

Site	Course Name	Time	Status	Difficulty
	Tensorflow for Deep Learning	2 Months	OnGoing	Intermediate
	Applications of Deep Neural Networks Washington University (in St. Louis) Course T81-558 By Jeff Heaton Well Documented Notebooks.	6 Months	OnGoing	Intermediate
	Intro to Machine Learning	10 weeks	OnGoing	Intermediate
HackerRank	Liner Alegbra	Variable	To Do	Variable
	Machine Learning by GIT	4 Months	To Do	Intermediate
	Machine Learning Unsupervised by GIT	4 Months	To Do	Intermediate
	Artificial Intelligence by GIT	4 Months	To Do	Intermediate
	Reinforcement Learning by GIT	4 Months	To Do	Advanced
	Deep Learning Visual Exploration	3 hours	To Do	Beginner
	Deep Learning using Keras	3 hours	To Do	Beginner
	Machine Learning using SciKit Simple, Basic Explaination,uses 'Pandas' for importing csv dataset. Models Used : SVC , KNN, RandomForest.	1 hours	Done	Beginner
	Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning	4 weeks	To Do	Intermediate
	Convolutional Neural Networks in TensorFlow	4 weeks	To Do	Intermediate

Resources	Time
	Deep Learning Frameworks 2019 By Siraj Raval 13 mintue video
	Google Codelabs Articles on Tensorflow
Deep Learning Framework	Description
	We’ll start with TensorFlow. TensorFlow works well on images as well as sequence-based data. If you are a beginner in deep learning, or don’t have a solid understanding of mathematical concepts like linear algebra and calculus, then the steep learning curve of TensorFlow might be daunting. I totally understand that this aspect can be complex for folks who are just starting out. My suggestion would be to keep practicing, keep exploring the community, and keep reading articles to get the hang of TensorFlow. Once you have a good understanding of the framework, implementing deep learning models will be very easy for you.
	Keras is a pretty solid framework to start your deep learning journey. If you are familiar with Python and are not doing some high-level research or developing some special kind of neural network, Keras is for you. The focus is more on achieving results rather than getting bogged down by the model intricacies. So if you are given a project related to, say image classification or sequence models, start with Keras. You will be able to get a working model very quickly. Keras is also integrated in TensorFlow and hence you can also build your model using tf.keras.
	As compared to TensorFlow, PyTorch is more intuitive. One quick project with both these frameworks will make that abundantly clear. Even if you don’t have a solid mathematics or a pure machine learning background, you will be able to understand PyTorch models. You can define or manipulate the graph as the model proceeds which makes PyTorch more intuitive. PyTorch does not have any visualization tool like TensorBoard but you can always use a library like matplotlib. I wouldn’t say PyTorch is better than TensorFlow, but both these deep learning frameworks are incredibly useful.
	Scikit-learn is a Python library used for machine learning. More specifically, it’s a set of simple and efficient tools for data mining and data analysis. The framework is built on top of several popular Python packages, namely NumPy, SciPy, and matplotlib. It’s easy to use even for beginners – and a great choice for simpler data analysis tasks. On the other hand, scikit-learn is not the best choice for deep learning.
	The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.