Deep Learning is a subset of Machine Learning, which on the other hand is a subset of Artificial Intelligence. Artificial Intelligence is a general term that refers to techniques that enable computers to mimic human behavior. Machine Learning represents a set of algorithms trained on data that make all of this possible.
Deep Learning is just a type of Machine Learning, inspired by the structure of the human brain. Deep learning algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure. To achieve this, deep learning uses a multi-layered structure of algorithms called neural networks.
The human brain works similarly. Whenever we receive new information, the brain tries to compare it with known objects. The same concept is also used by deep neural networks.
Why is Deep Learning is Popular these Days?
Long before deep learning was used, traditional machine learning methods were mainly used. Such as Decision Trees, SVM, Naïve Bayes Classifier, and Logistic Regression. These algorithms are also called flat algorithms. Flat here means that these algorithms were not normally be applied directly to the raw data (such as .csv, images, text, etc.). We need a preprocessing step called Feature Extraction. The result of Feature Extraction is a representation of the given raw data that can now be used by these classic machine learning algorithms to perform a task. For example, the classification of the data into several categories or classes. Feature Extraction is usually quite complex and requires detailed knowledge of the problem domain. This preprocessing layer must be adapted, tested, and refined over several iterations for optimal results.
About this course
The Deep Learning Nano-degree program offers you a solid introduction to the world of artificial intelligence. In this program, you will master fundamentals that will enable you to go further in the field, launch or advance a career, and join the next generation of deep learning talent that will help define a beneficial, new, AI-powered future for our world.
You will study cutting-edge topics such as Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, and Network Deployment, and build projects in PyTorch and NumPy. You will learn from authorities such as Ian Good fellow and Jun-Yan Zhu, inventors of types of generative adversarial networks, as well as AI experts, Sebastian Thrun and Andrew Trask.
For anyone interested in this transformational technology, this program is an ideal point-of-entry. The program is comprised of 5 courses and 5 projects. Each project you build will be an opportunity to prove your skills and demonstrate what you’ve learned in your lessons.
What you will learn from this course?
You will 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.
Why should you enroll in this Course?
In this program, you will master deep learning fundamentals that will prepare you to launch or advance a career, and additionally pursue further advanced studies in the field of AI. You will study cutting-edge topics such as neural, convolutional, recurrent neural, and generative adversarial networks, as well as sentiment analysis model deployment. You will build projects in Keras and NumPy, in addition to TensorFlow PyTorch. You will learn from experts in the field, and gain exclusive insights from working professionals. For anyone interested in building expertise with this transformational technology, this Nano-degree program is an ideal point-of-entry.
Syllabus
Course 1: Neural Networks
LESSON ONE: Introduction to Neural Networks
In this lesson, you will learn solid foundations on deep learning and neural networks. You’ll also implement gradient descent and backpropagation in Python.
LESSON TWO: Implementing Gradient Descent
Mat and Luis will introduce you to a different error function and guide you through implementing gradient descent using NumPy matrix multiplication.
LESSON THREE: Training Neural Networks
Now that you know what neural networks are, in this lesson, you will learn several techniques to improve their training. Learn how to prevent overfitting of training data and best practices for minimizing the error of a network.
LESSON FOUR: Sentiment Analysis
In this lesson, Andrew Trask, the author of Grokking Deep Learning, will show you how to define and train neural networks for sentiment analysis (identifying and categorizing opinions expressed in the text).
LESSON FIVE: Deep Learning WIth Pytorch
Learn how to use PyTorch for building and testing deep learning models.
Course 2: Convolutional Neural Networks
LESSON ONE: Cloud Computing
Take advantage of Amazon’s GPUs to train your neural network faster. In this lesson, you’ll set up an instance on AWS and train a neural network on a GPU.
LESSON TWO: Convolutional Neural Network
Alexis and Cezanne explain how Convolutional Neural Networks can be used to identify patterns in images and how they help us dramatically improve performance in image classification tasks.
LESSON THREE: CNNs In PyTorch
In this lesson, you’ll walk through an example Convolutional Neural Network (CNN) in PyTorch. You’ll study the line-by-line breakdown of the code and can download the code and run it yourself.
LESSON FOUR: Weight Initialization
In this lesson, you’ll learn how to find good initial weights for a neural network. Having good initial weights often allows a neural network to arrive at an optimal solution, faster than without initialization.
LESSON FIVE: Autoencoders
Autoencoders are neural networks used for data compression, image denoising, and dimensionality reduction. Here, you’ll build autoencoders using PyTorch.
LESSON SIX: Transfer Learning in PyTorch
Most people don’t train their own networks on massive datasets. In this lesson, you’ll learn how to finetune and use a pre-trained network and apply it to a new task using transfer learning.
LESSON SEVEN: Deep Learning for Cancer Detection
In this lesson, Sebastian Thrun teaches us about his groundbreaking work detecting skin cancer with Convolutional Neural Networks.
Course 3: Recurrent Neural Networks
LESSON ONE: Recurrent Neural Networks
Ortal will introduce Recurrent Neural Networks (RNNs), which are machine learning models that are able to recognize and act on sequences of inputs.
LESSON TWO: Long Short-Term Memory Network
Luis explains Long Short-Term Memory Networks (LSTM), and similar architectures that form a memory about a sequence of inputs, over time.
LESSON THREE: Implementation of RNN & LSTM
Train recurrent neural networks to generate new characters, words, and bodies of text.
LESSON FOUR: Hyperparameters
In this lesson, we’ll look at a number of different hyperparameters that are important for our deep learning work, such as learning rates. We’ll discuss starting values and intuitions for tuning each hyperparameter.
LESSON FIVE: Embeddings & Word2vec
In this lesson, you’ll learn about embeddings in neural networks by implementing a word2vec model that converts words into a representative vector of numerical values.
LESSON SIX Sentiment Prediction RNN
In this lesson, you’ll learn to implement a recurrent neural network for predicting sentiment. This is intended to give you more experience building RNNs.
Course 4: Generative Adversarial Networks
LESSON ONE: Generative Adversarial Network
Ian Goodfellow, the inventor of GANs, introduces you to these exciting models. You’ll also implement your own GAN on a simple dataset.
LESSON TWO: Deep Convolutional GANs
Implement a Deep Convolutional GAN to generate complex, color images of house numbers.
LESSON THREE: PIX2PIX & Cyclegan
Jun-Yan Zhu and Cezanne lead you through a CycleGAN formulation that can learn from unlabeled sets of images.
This Nano-degree Programs Includes:
Experienced Project reviews.
Technical mentor support.
Personal career services.
How is the Nano-degree program structured?
The Deep Learning Nano-degree program is comprised of content and curriculum to support five (5) projects. Also, they estimated that students can complete the program in four (4) months working 10 hours per week. Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes
About Project description
Throughout this Nano-degree program, you will have the opportunity to prove your skills by building the projects.
Prerequisites:
Students who are interested in enrolling must have intermediate-level Python programming knowledge, and experience with NumPy and pandas. You will need to be able to communicate fluently and professionally in written and spoken English. Additionally, students must have the necessary math knowledge, including algebra and some calculus—specifically partial derivatives, and matrix multiplication (linear algebra).
If you do not meet the requirements to enroll, What should you do?
Udacity has a number of Nano-degree programs and free courses that can help you prepare, which includes:
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