Deep Learning Specialization By Andrew Ng (Top Instructor from Stanford University )

Become a Deep Learning expert. Master the fundamentals of deep learning and break into AI.

Become a Deep Learning expert. Master the fundamentals of deep learning and break into AI.

Note: You have 7 days free trial and also financial aid available!

Course Link:

What you will learn

  • Build & train deep neural networks, identify key architecture parameters, & implement efficient neural networks & deep learning to your applications
  • Train test sets & analyze variance for DL applications, use standard techniques & optimization algorithms, & build neural networks in TensorFlow
  • Build a CNN & apply it to detection & recognition tasks, use neural style transfer to generate art, & apply algorithms to image, video, & 2D/3D data
  • Build & train RNNs, work with NLP & Word Embeddings, & use HuggingFace tokenizers & transformer models to perform tasks like NER & Question Answering

Skills you will gain

  1. Artificial Neural Network
  2. Convolutional Neural Network
  3. Tensorflow
  4. Recurrent Neural Network
  5. Transformers
  6. Deep Learning
  7. Backpropagation
  8. Python Programming
  9. Neural Network Architecture
  10. Mathematical Optimization
  11. hyperparameter tuning
  12. Inductive Transfer

There are 5 Courses in this Specialization:

1. Neural Networks and Deep Learning:

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.

2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization:

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.

3. Structuring Machine Learning Projects

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.

4. Convolutional Neural Networks

In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.

5. Sequence Models

In the fifth course of the Deep Learning Specialization, you will become familiar with NLP models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and more that have become possible with the evolution of sequence algorithms thanks to deep learning.

About this Specialization

The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

In this Specialization, you will build neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. You will master these theoretical concepts and their industry applications using Python and TensorFlow. You will tackle real-world case studies such as autonomous driving, sign language reading, music generation, computer vision, speech recognition, and natural language processing.

AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI. Along the way, you will get career advice from deep learning experts from industry and academia.

Applied Learning Project

By the end of the program, you’ll be ready to

• Build and train deep neural networks, implement vectorized neural networks, identify key parameters in architecture, and apply deep learning to your applications

• Use the best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard neural network techniques, apply optimization algorithms, and implement a neural network in TensorFlow

• Diagnose and use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end learning, transfer learning, and multi-task learning

• Build a CNN, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data

  • Build and train RNNs, GRUs, and LSTMs, apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform NER and Question Answering

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you’d like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You’ll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you’ll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you’ll earn a Certificate that you can share with prospective employers and your professional network.

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