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Udacity Deep Learning Nanodegree

N.B.: Please don't use the assignment and quiz solution. Try to solve the problem by yourself.


Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website. - Source

Books

Resources

AI, ML, DL

Backpropagation

Optimizers

Style Transfer

CNN

LSTMs

Reinforcement learning

Residual Learning

Attention

GAN

Batch Normalization

Machine Learning Workflow

Cloud ML

GitHub

Hackathons

Neural Network Frameworks

Keras

Others

Extra Projects

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.