Books

• Grokking Deep Learning: https://www.manning.com/books/grokking-deep-learning
• Grokking Deep Learning Book's Excercise: https://github.com/iamtrask/Grokking-Deep-Learning
• Neural Networks And Deep Learning by Michael Nielsen
• The Deep Learning Textbook from Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Resources

AI, ML, DL

• AI, Deep Learning, and Machine Learning: A Primer
• DL in Cancer Detection

Backpropagation

• Medium - Andrej Karpathy
• Andrej Karpathy (a lecture from Stanford's CS231n course)

Optimizers

• An overview of gradient descent optimization algorithms

Style Transfer

• Fast style transfer

CNN

• Stanford's CS231n course

LSTMs

• A Beginner's Guide to LSTMs and Recurrent Neural Networks
• Understanding LSTM Networks - Colah's Blog

Reinforcement learning

• DeepTraffic
• Flappy Bird

Residual Learning

• An Overview of ResNet and its Variants

Attention

• Attention is all you need - Lukasz Kaiser - Youtube

GAN

• iGAN - GitHub
• Attacking Machine Learning with Adversarial Examples
• Image-to-Image Demo
• Pix2Pix and CycleGAN Github by Jun-Yan
• CycleGAN
• Implementation of StarGAN

Batch Normalization

• PyTorch BatchNorm2d documentation

Machine Learning Workflow

• AWS - Machine Learning Workflow
• GCP - Machine Learning Workflow
• Azure - Machine Learning Workflow
• Open Neural Network Exchange

Cloud ML

• XGBoost Documentation
• Machine Learning Challenge Winning Solutions

GitHub

• Getting started with writing and formatting on GitHub
• Basic writing and formatting syntax

Hackathons

• Discover Upcoming Hackathons
• Hackathons around the world
• DevPost
• Major League Hackathons

Neural Network Frameworks

• Keras
• TensorFlow
• Caffe
• Theano
• Scikit-learn

Keras

• Keras Optimizers

Others

• MIT CSAIL
• Facebook AI Research

Extra Projects

• Generate your own Bach music using like - DeepBach
• Predict seizures in intracranial EEG recordings on - Kaggle

Core Curriculum

1. Introduction to Deep Learning

Get your first taste of deep learning by applying style transfer to your own images, and gain experience using development tools such as Anaconda and Jupyter notebooks.

Lesson-1: Welcome to the Deep Learning Nanodegree Program

No	Lesson	Topic	Link/Source
1	Welcome to the Deep Learning Nanodegree Program	Application of Deep Learning	Source/GitHub
2	Meet Your Instructors	Instructors-Matt, Luis and Cezanne	Source/GitHub
3	Program Structure	Course outline, Chapter Introduction, Project Guidelines	GitHub
4	Community Guidelines	Details about the community rules	GitHub
5	Prerequisites	Required programming and math skills	GitHub
6	Getting Set Up	Required tools-Anaconda, Jupyter Notebook	Source

Lesson-2: Nanodegree Career Sevices

No	Lesson	Topic	Link/Source
1	Access the Career Portal	Career portal guidelines	GitHub
2	Prepare for the Udacity Talent Program	Requirements for udacity profile, complete udacity profile	GitHub

Lesson-3: Welcome to Udacity

No	Lesson	Topic	Link/Source
1	What It Takes	Description of Udacity Nanodegree	Source/GitHub
2	Project Reviews	Project review and feedback system	Source/GitHub
3	Knowledge	Tell about knowledge sharing website	Source
4	Mentors and Student Hub	Helping support of the course - mentors and student hub	Source
5	Community Initiatives	Community introduction, project milestone and everyday challenge	Source
6	Meet the Careers Team	Career guide lines for the mentors	Source/GitHub
7	Introduction to the Career Portal	Create my career profile	Source/GitHub
8	Access Your Career Portal	How to improve my career portal	Source/GitHub
9	Your Udacity Professional Profile	Udacity Professional Profile features important, professional information	Source/GitHub
10	Prepare for the Udacity Talent Program	Udacity Talent Program, update udacity profile	Source/GitHub

Lesson-4: Get Help with Your Account

No	Lesson	Topic	Link/Source
1	Frequently Asked Questions (FAQ)	Frequently asked question and forum	Source
2	Support	Discuss about help center	Source

Lesson-5: Anaconda

No	Lesson	Topic	Link/Source
1	Instructor	Instructor-Mat Leonard, welcome about anaconda	Source/GitHub
2	Introduction	Conda or Anaconda installation step	Source
3	What is Anaconda?	Anaconda's description, managing packages and environments	Source/GitHub
4	Installing Anaconda	Installing Anaconda	Source
5	Managing packages	Managing packages system	Source/GitHub
6	Managing environments	Managing and the using of the environments	Source/GitHub
7	More environment actions	Saving, loading, listing and removing environments	Source/GitHub
8	Best practices	Using and sharing environments	Source/GitHub
9	On Python versions at Udacity	Why Python version-3 is used this course	Source/GitHub

Lesson-6: Applying Deep Learning

No	Lesson	Topic	Link/Source
1	Introduction	Overview of the lesson	Source/GitHub
2	Style Transfer	Overview about the style transfer	Source/GitHub
3	DeepTraffic	Introduction the deepTrafic (application DL)	Source/GitHub
4	Flappy Bird	DL application in Flappy Bird	Source/GitHub
5	Books to Read	Some suggested books for DL	Source/GitHub

Lesson-7: Jupyter Notebooks

No	Lesson	Topic	Link/Source
1	Instructor	Overview of Jupyter Notebook and Introduce the Mat Leonard, instructor	Source/GitHub
2	What are Jupyter notebooks?	Introduction of jupyter notebook, Literate programming and How notebooks work	GitHub
3	Installing Jupyter Notebook	Installing process of Jupyter Notebook	Source/GitHub
4	Launching the notebook server	Launching and shutdowing the notebook server	Source/GitHub
5	Notebook interface	Notebook interface - tool bar, command palette etc	Source/GitHub
6	Code cells	What is Code cells and uses of it	Source/GitHub
7	Markdown cells	Writting procedure of markdown cells - headers, emphasis, code, math expressions	Source/GitHub
8	Keyboard shortcuts	Keyboard shortcuts	Source/GitHub
9	Magic keywords	Timing code, Embedding visualizations, Debugging	Source/GitHub
10	Converting notebooks	Converting notebooks in differents file format	Source/GitHub
11	Creating a slideshow	Creating and Running a slideshow	Source/GitHub
12	Finishing up	Summary and finish message	Source/GitHub

Lesson-8: Matrix Math and NumPy Refresher

No	Lesson	Topic	Link/Source
1	Introduction	Matrix and importance in Deep Learning	Source/GitHub
2	Data Dimensions	Data dimensions - scalers, vector, matrix, tensors	Source/GitHub
3	Data in NumPy	NumPy Introduction	Source/GitHub
4	Element-wise Matrix Operations	Matrices element wise operation	Source/GitHub
5	Element-wise Operations in NumPy	Matrices element wise operation using NumPy	Source/GitHub
6	Matrix Multiplication: Part 1	Dot multiplication, element wise multiplication in metrices	Source/GitHub
7	Matrix Multiplication: Part 2	Important note about matrix multiplication	Source/GitHub
8	NumPy Matrix Multiplication	NumPy Matrix Multiplication	Source/GitHub
9	Matrix Transposes	Matrix Transposes and where use of it	Source/GitHub
10	Transposes in NumPy	Transposes matrices in NumPy	Source/GitHub
11	NumPy Quiz	Short programming quiz that asks to use a few NumPy features	GitHub

2. Neural Networks

Learn neural networks basics, and build your first network with Python and NumPy. Use the modern deep learning framework PyTorch to build multi-layer neural networks, and analyze real data.

Lesson-1: Introduction to Neural Networks

Lesson-2: Implementing Gradient Descent

Lesson-3: Training Neural Networks

Lesson-4: GPU Workspaces Demo

Lesson-5: Sentiment Analysis

Lesson-6: Deep Learning with PyTorch

3. Convolutional Neural Networks

Learn how to build convolutional networks and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them. Use these networks to learn data compression and image denoising.

Lesson-1: Convolutional Neural Networks

Lesson-2: Cloud Computing

Lesson-3: Transfer Learning

Lesson-4: Weight Initialization

Lesson-5: Autoencoders

Lesson-6: Style Transfer

Lesson-7: Deep Learning for Cancer Detection

Lesson-8: Jobs in Deep Learning

4. Recurrent Neural Networks

Build your own recurrent networks and long short-term memory networks with PyTorch; perform sentiment analysis and use recurrent networks to generate new text from TV scripts.

Lesson-1: Recurrent Neural Networks

Lesson-2: Long Short-Term Memory Networks (LSTMs)

Lesson-3: Implementation of RNN & LSTM

Lesson-4: Hyperparameters

Lesson-5: Embedding & Word2Vec

Lesson-6: Sentiment Prediction RNN

No	Lesson	Topic	Link/Source
1	Sentiment RNN, Introduction	LSTM example, Sentiment analysis	Source/GitHub
2	Pre-Notebook: Sentiment RNN	Implementing a complete RNN that can classify the sentiment of movie reviews	Source
3	Notebook: Sentiment RNN	Implementing a complete RNN that can classify the sentiment of movie reviews	GitHub
4	Data Pre-Processing	Import data, Remove punctuation, Split data into list	GitHub
5	Encoding Words, Solution	Encoding the word & label as conver to word to integers	GitHub
6	Getting Rid of Zero-Length	Set all the input size as standard, remove zero len data and modify high len data	GitHub
7	Cleaning & Padding Data	Removing zero length data	GitHub
8	Padded Features, Solution	Pad or truncate all data to a specific length	GitHub
9	TensorDataset & Batching Data	Split the train-validation-test data, Data loaded from numpy to tensor, Data loaded with batch size	GitHub
10	Defining the Model	Introduction about the model of the network	GitHub
11	Complete Sentiment RNN	Initialize the model parameters, Feedforward network, Backpropagation, Initializes hidden state	GitHub
12	Training the Model	Hyperparameters, Loss function and Optimization	GitHub
13	Testing	Testing the model	GitHub
14	Inference, Solution	Inference	GitHub

Lesson-7: Attention

No	Lesson	Topic	Link/Source
1	Introduction to Attention	Introduction and defination of Attention, Application and where uses attention	Source/GitHub
2	Encoders and Decoders	Sequence-to-sequence models, encoders, decoders	Source/GitHub
3	Sequence to Sequence Recap	Review of sequence-to-sequence models, encoders, decoders	Source/GitHub
4	Encoding -- Attention Overview	Overview of encoding in Attention	Source/GitHub
5	Decoding -- Attention Overview	Overview of decoding in Attention	Source/GitHub
6	Attention Overview	Example and question about attention	Source/GitHub
7	Attention Encoder	Behind the scenario of encoder algorithms in attention	Source/GitHub
8	Attention Decoder	Backbone of decoder and the Attention decoder phase	Source/GitHub
9	Attention Encoder & Decoder	Quiz about encoder and decoder	Source/GitHub
10	Bahdanau and Luong Attention	Bahdanu/Additive Attention and Luong/Multiplicative Attention Model Introduction	Source/GitHub
11	Multiplicative Attention	Details architecture of Multiplicative Attention	Source/GitHub
12	Additive Attention	3-Concat attention and details architecture of Additive Attention	Source/GitHub
13	Additive and Multiplicative Attention	Quiz: Additive and Multiplicative Attention	Source/GitHub
14	Computer Vision Applications	Example of computer vision applications with attention	Source/GitHub
15	Other Attention Methods	The transformer models and indside of the model (encoder and decoder part)	Source/GitHub
16	The Transformer and Self-Attention	Full details of the Transformer and Self-Attention architecture	Source/GitHub
17	Notebook: Attention Basics	Attention Basic function-Scoring, Annotations Matrix, Softmax, Attention Context Vector	GitHub
18	[SOLUTION]: Attention Basics	Attention Basic function-Scoring, Annotations Matrix, Softmax, Attention Context Vector	GitHub
19	Outro	Ending message and remainder the most important information in that data	Source/GitHub

5. Generative Adversarial Networks

Learn to understand and implement a Deep Convolutional GAN (generative adversarial network) to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs.

Lesson-1: Generative Adversarial Networks

No	Lesson	Topic	Link/Source
1	Introducing Ian GoodFellow	Introduction about Ian Goodfellow and his experience	Source/GitHub
2	Applications of GANs	What you can do with GAN, as- text to images, art to realistic image, face to cartoon, dat to night mode, unsupervised image-to-image, Imitation learning	Source/GitHub
3	How GANs work	Autoregressive model, Process of GAN, Generator models & Discriminator	Source/GitHub
4	Games and Equilibria	Game theory, Rock-Paper-Scissors game, Equilibriam situtation	Source/GitHub
5	Tips for Training GANs	GAN layers architecture, activation and loss functions for generator & discriminator, batch normalization	Source/GitHub
6	Generating Fake Images	Excercise dataset introduction, MNIST dataset - fake or real image	Source/GitHub
7	MNIST GAN	Built a GAN to generate new images of handwritten digits	Source/GitHub
8	GAN Notebook & Data	Introduction the excercise and datasets	Source/GitHub
9	Pre-Notebook: MNIST GAN	All about generating new images of handwritten digits	Solution
10	Notebook: MNIST GAN	Excercise of GAN	GitHub
11	The Complete Model	Hints of the complete model	GitHub
12	Generator & Discriminator	Generator and Discriminator Model implementation	GitHub
13	Hyperparameters	Hyperparameters set for the excercise	GitHub
14	Fake and Real Losses	Loss calculation for the fake & real models	GitHub
15	Optimization Strategy, Solution	Adam optimization is used for D & G models	GitHub
16	Training Two Networks	Complete the training of the networks	GitHub
17	Training Solution	Solution of the training of the networks	GitHub

Lesson-2: Deep Convolutional GANs

No	Lesson	Topic	Link/Source
1	Deep Convolutional GANs	Introduction about Deep convolutional GANs	Source/GitHub
2	DCGAN, Discriminator	DCGAN Architecture basic	Source/GitHub
3	DCGAN Generator	DCGAN Generator, transpose convolutional network	Source/GitHub
4	What is Batch Normalization?	Batch normalization defination and mathematical calculation	Source/GitHub
5	Pre-Notebook: Batch Norm	Excercise of Batch Norm	Source/GitHub
6	Notebook: Batch Norm	Excercise of Batch Norm	Source/GitHub
7	Benefits of Batch Normalization	Describe the benefits of Batch Normalization	Source/GitHub
8	DCGAN Notebook & Data	DCGAN Excercise introduction	Source/GitHub
9	Pre-Notebook: DCGAN, SVHN	Excercise of DCGAN, SVHN	Source
10	Notebook: DCGAN, SVHN	Excercise of DCGAN, SVHN	GitHub
11	Scaling, Solution	Scaling calculation	GitHub
12	Discriminator	Discriminator architecture for this network	GitHub
13	Discriminator, Solution	Discriminator architecture solution for this network	GitHub
14	Generator	Describe the general structure of the generator	GitHub
15	Generator, Solution	The solution of the generator model	GitHub
16	Optimization Strategy	Optimization parameters set for the model	GitHub
17	Optimization Solution & Samples	The solution of Optimization parameters	GitHub
18	Other Applications of GANs	More about GAN - Semi-Supervised Learning, Domain Invariance, Ethical and Artistic Applications: Further Reading	Source/GitHub

Lesson-3: Pix2Pix & CycleGAN

No	Lesson	Topic	Link/Source
1	Introducing Jun-Yan Zhu	Introduction of Jun-Yan Zhu and his background, Intorduction CycleGAN, Pix2Pix	Source/GitHub
2	Image to Image Translation	Image to image translation with example	Source/GitHub
3	Designing Loss Functions	Loss are calculated using Euclidean distance	Source/GitHub
4	GANs, a Recap	Full review of Generator, Descrimenator of GAN network	GitHub
5	Pix2Pix Generator	What changes of Pix2Pix Generator	Source/GitHub
6	Pix2Pix Discriminator	What changes of Pix2Pix Discriminator	Source/GitHub
7	CycleGANs & Unpaired Data	Unpaired data, Mappings, Inverse mappings	Source/GitHub
8	Cycle Consistency Loss	Calculate the Cycle Consistency Loss	Source/GitHub
9	Why Does This Work?	Weaknesses of CycleGAN	Source/GitHub
10	Beyond CycleGANs	Augmented CycleGAN, Paired CycleGAN, Cross-domain models, StarGAN	Source/GitHub

Lesson-4: Implementing a CycleGAN

No	Lesson	Topic	Link/Source
1	CycleGAN Notebook & Data	Introduction of the excercise, datasets, objective of the excercise	Source/GitHub
2	Pre-Notebook: CycleGAN	CycleGAN Excercise	Source
3	Notebook: CycleGAN	Excercise of CycleGAN	GitHub
4	DC Discriminator	Implement the Discriminator Function	GitHub
5	DC Discriminator, Solution	Solution of the Discriminator Function	GitHub
6	Generator & Residual Blocks	Uses of Residual Blocks and it uses in the excercise	GitHub
7	CycleGAN Generator	Implement of Residual Blocks and Generator Function	GitHub
8	Blocks & Generator, Solution	Solution of Residual Blocks and Generator Function	GitHub
9	Adversarial & Cycle Consistency Losses	Description of Adversarial & Cycle Consistency Losses	GitHub
10	Loss & Optimization, Solution	Solution of Loss and optimization	GitHub
11	Training Exercise	Implement the training function for descrimenator and generator	GitHub
12	Training Solution & Generated Samples	Introduction of the excercise, datasets, objective of the excercise	GitHub

6. Deploying a Model

Train and deploy your own PyTorch sentiment analysis model. Deployment gives you the ability to use a trained model to analyze new, user input. Build a model, deploy it, and create a gateway for accessing it from a website.

Lesson-1: Introduction to Deployment

No	Lesson	Topic	Link/Source
1	Welcome!	Instructor introduction, deployment lesson introduction	Source/GitHub
2	What's Ahead	Behind the scenario of deployment, reason of using and it's characteristcs	Source/GitHub
3	Problem Introduction	How to approach a real life problem using machine learning	Source/GitHub
4	Machine Learning Workflow	Structure of machine learning workflow with example	Source/GitHub
5	Machine Learning Workflow	Machine learning structure quiz	Source/GitHub
6	What is Cloud Computing & Why	Cloud computing defination, benfits, risks and now mostly why it is used	Source/GitHub
7	Why Cloud Computing?	Cloud computing quiz	Source/GitHub
8	Machine Learning Applications	Machine Learning Applications, Example of ML in the workplace	Source/GitHub
9	Machine Learning Applications	Machine Learning Applications, Example of ML in the workplace	Source/GitHub
10	Paths to Deployment	Path of deployment, DevOps in Machine Learning	Source/GitHub
11	Paths to Deployment	Paths to Deployment quiz	Source/GitHub
12	Production Environments	Production environments of ML and how it works	Source/GitHub
13	Production Environments	Production Environments quiz	Source/GitHub
14	Endpoints & REST APIs	Endpoint and REST API description, HTTP communication and Method	Source/GitHub
15	Endpoints & REST APIs	Endpoints & REST APIs quiz	Source/GitHub
16	Containers	Container definition and structure	Source/GitHub
17	Containers	Container Quiz	Source/GitHub
18	Containers - Straight From the Experts	Container details from an expart developer - Jesse Swidler, a senior software engineer at Udacity	Source/GitHub
19	Characteristics of Modeling & Deployment	Description of characteristics of Modeling & Deployment	Source/GitHub
20	Characteristics of Modeling & Deployment	Characteristics of Modeling & Deployment quiz	Source/GitHub
21	Comparing Cloud Providers	Characteristics of Modeling & Deployment quiz	Source/GitHub
22	Comparing Cloud Providers	Comparing Cloud Providers quiz	Source/GitHub
23	Closing Statements	Learn about deployment	Source/GitHub
24	Summary	Summary of the lesson	Source/GitHub
25	[Optional] Cloud Computing Defined	Details about cloud computing defined	Source/GitHub
26	[Optional] Cloud Computing Explained	Details about cloud computing defined	Source/GitHub

Lesson-2: Building a Model using SageMaker

No	Lesson	Topic	Link/Source
1	Introduction to Amazon SageMaker	Basic understanding of the SageMaker service	Source/GitHub
2	Create an AWS Account	Procedure to open an AWS account	Source/GitHub
3	Checking GPU Access	Checking GPU Access	Source/GitHub
4	Setting up a Notebook Instance	Creating an instance in Amazon SageMaker service	Source/GitHub
5	Cloning the Deployment Notebooks	Starting instance and clone the project file	Source
6	Is Everything Set Up?	Setting up the AWS account and SageMaker instance	Source/GitHub
7	Boston Housing Example - Getting the Data Ready	Working with Boston Housing Example and Getting the Data Ready	Source/GitHub
8	Boston Housing Example - Training the Model	Training XGBoost the model	Source/GitHub
9	Boston Housing Example - Testing the Model	Test the model and clean up the data directory	Source/GitHub
10	Mini-Project: Building Your First Model	Introduction the IMDB sentiment analysis mini project	Source
11	Mini-Project: Solution	Solution of the mini project	Source/GitHub

Lesson-3: Deploying and Using a Model

Lesson-4: Hyperparameter Tuning

Lesson-5: Updating a Model

Project

Throughout this Nanodegree program, i'll have the opportunityto prove your skills by building the following projects-

1. Predicting Bike-Sharing Patterns

Build and train neural networks from scratch to predict the number of bikeshare users on a given day.

In this project, i was got to build a neural network from scratch to carry out a prediction problem on a real dataset.

The data comes from the UCI Machine Learning Database.

2. Dog-Breed Classifier

Design and train a convolutional neural network to analyze images of dogs and correctly identify their breeds. Use transfer learning and well-known architectures to improve this model - this is excellent preparation for more advanced applications.

3. Optimize Your GitHub Profile

GitHub Profiles are a key piece of "evidence" to an employer that you'd be a good job candidate, because they can see the details of your work. Recruiters use GitHub as a way to find job candidates, and many Nanodegree alumni have received work opportunities from their activity on GitHub. In addition, using GitHub is a way for you to collaborate on projects with other programmers - this will show that you are able to work well with others on an engineering team on the job.

4. Generate TV Scripts

Build a recurrent neural network on TensorFlow to process text. Use it to generate new episodes of your favorite TV show, based on old scripts.

5. Generate Faces

Build a pair of multi-layer neural networks and make them compete against each other in order to generate new, realistic faces. Try training them on a set of celebrity faces, and see what new faces the computer comes out with!

6. Improve your LinkedIn

7. Deploying a Sentiment Analysis Model

Train and deploy your own PyTorch sentiment analysis model. You'll build a model and create a gateway for accessing it from a website.

Extracurricular

1. Additional Lessons

Lesson-1: Evaluation Metrics

No	Lesson	Topic	Link/Source
1	Intro	Introduction about Evaluation Metrics	Source/GitHub
2	Confusion Matrix	Described the model of confusion matrix and how it is used	Source/GitHub
3	Confusion Matrix 2	Quiz solution about confusion matrix	Source/GitHub
4	Accuracy	Importance of accuracy in deep learning model	Source/GitHub
5	Accuracy 2	Quiz solution about accuracy	Source/GitHub
6	When accuracy won't work	In some model accuracy is not play an vital role where accuracy impact bad results	Source/GitHub
7	False Negatives and Positives	Discuss where the false negatives and positives is used	Source/GitHub
8	Precision and Recall	Introduction about precision and recall	Source/GitHub
9	Precision	Describe the law of precision with examples	Source/GitHub
10	Recall	Describe the law of recall with examples	Source/GitHub
10	ROC Curve	How to calculate ROC Curve and graph of ROC Curve	Source/GitHub

Lesson-2: Regression

No	Lesson	Topic	Link/Source
1	Intro	Introduction about Linear regression with examples	Source/GitHub
2	Quiz: Housing Prices	Regression example with housing prices	Source/GitHub
3	Solution: Housing Prices	Solution of regression problem of housing prices	Source/GitHub
4	Fitting a Line Through Data	How to fit a line through data	Source/GitHub
5	Moving a Line	Explain how the line-slope work with graph & mathematically	Source/GitHub
6	Absolute Trick	How to calculate the absolute trick	Source/GitHub
7	Square Trick	Another way to calculate come to the closer line calculation	Source/GitHub
8	Gradient Descent	Minimize the error	Source/GitHub
9	Mean Absolute Error	Law of mean absolute error	Source/GitHub
10	Mean Squared Error	Law of mean squared error	Source/GitHub
11	Minimizing Error Functions	Relation in trick & error function and how minimize error function	Source/GitHub
12	Mean vs Total Error	Difference between Mean vs Total Error	Source/GitHub
13	Mini-batch Gradient Descent	Defination of Mini-batch Gradient Descent	Source/GitHub
14	Absolute Error vs Squared Error	Differences between Absolute Error and Squared Error	Source/GitHub
15	Linear Regression in scikit-learn	Basic sckit-learn and predict data using sklearn.linear_model	GitHub
16	Higher Dimensions	Higher dimensions error calculation	Source/GitHub
17	Multiple Linear Regression	Multiple Linear Regression with Excercise	GitHub
18	Closed Form Solution	How to come closer in solution nth term	Source/GitHub
19	(Optional) Closed form Solution Math	Derivation of error for nth term for closed form solution	GitHub
20	Linear Regression Warnings	Where linear regression doesn't work well	GitHub
21	Polynomial Regression	Polynomial Regression defination with one example	Source/GitHub
22	Regularization	L1 & L2 Regularization, Simple & Complex Model	Source/GitHub
23	Neural Network Regression	Basic neural network for regression	Source/GitHub
24	Neural Networks Playground	A Visual and Interactive Guide to the Basics of Neural Networks	Source/GitHub
25	Outro	Ending summurization of lesson	Source/GitHub

Lesson-3: MiniFlow

No	Lesson	Topic	Link/Source
1	Welcome to MiniFlow	Limitation of Numpy, introduction tensorflow, miniFlow and differentiable graphs	Source/GitHub
2	Graphs	What is neural network and neural network graph, fordward propagatin	Source/GitHub
3	MiniFlow Architecture	Implement the MiniFlow Architecture	Source/GitHub
4	Forward Propagation	Forward Propagation Implemented, Change the Add() function	GitHub
5	Forward Propagation Solution	Forward Propagation Solution with proper explain	GitHub
6	Learning and Loss	Linear neural network implement	GitHub
7	Linear Transform	Linear Tranform functions are implemented	GitHub
8	Sigmoid Function	Where sigmoid function is implemented and how implemented	GitHub
9	Cost	Loss calculation using MSE	GitHub
10	Cost Solution	Solution of cost function	Source/GitHub
11	Gradient Descent	Gradient Descent, Convergence, Divergence	GitHub
12	Backpropagation	How to calculate backpropagation in the neural network	GitHub
13	Stochastic Gradient Descent	Stochastic Gradient Descent Impementation	GitHub
14	SGD Solution	Stochastic Gradient Descent Solution	GitHub
15	Outro	Ending Message	Source/GitHub

2. TensorFlow, Keras, Frameworks

Lesson-1: Introduction to Keras

No	Lesson	Topic	Link/Source
1	Intro	Introductiona and lesson structure of keras	Source/GitHub
2	Keras	Keras usefull method details with excercise	GitHub
3	Pre-Lab: Student Admissions in Keras	Excercise implementation details describe	Source/GitHub
4	Lab: Student Admissions in Keras	Excercise implementation details describe	GitHub
5	Optimizers in Keras	Different types of keras optimizers details	Source/GitHub
6	Mini Project Intro	Hints the next coming mini project	Source/GitHub
7	Pre-Lab: IMDB Data in Keras	Solution Tips	GitHub
8	Lab: Student Admissions in Keras	Excercise implementation details describe	GitHub

Lesson-2: Keras CNNs

Lesson-3: Introduction to TensorFlow

30 Days of Udacity Challenge

The premise of this challenge is to build a habit of practicing new skills by making a public commitment of practicing the topic of your program every day for 30 days.

• Me @ Honorable Mentions Wall - "Md. Mokit H."