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Andrew Ng, Kian Katanforoosh, and Younes Bensouda Mourri

Dans ce cours, vous apprendrez la "magie" qui permet l'efficacité de l’apprentissage profond. Plutôt que de voir le processus d’apprentissage profond comme une boîte noire, vous comprendrez ce qui commande la performance et vous pourrez ainsi obtenir systématiquement de bons résultats plus souvent. Vous apprendrez également TensorFlow.

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Dans ce cours, vous apprendrez la "magie" qui permet l'efficacité de l’apprentissage profond. Plutôt que de voir le processus d’apprentissage profond comme une boîte noire, vous comprendrez ce qui commande la performance et vous pourrez ainsi obtenir systématiquement de bons résultats plus souvent. Vous apprendrez également TensorFlow.

Au bout de 3 semaines, vous:

- Pourrez comprendre les meilleures pratiques dans le secteur de construction des applications d’apprentissage profond.

- Serez en mesure d’utiliser avec efficacité des "astuces", communes des réseaux neuronaux, qui incluent l’initialisation, la régularisation L2 et la régularisation du décrochage, la normalisation par lots, la vérification de gradients,

- Pourrez mettre en œuvre et employer une variété d’algorithmes d’optimisation, comme par exemple la descente de gradients par mini-lots, le momentum, RMSprop et Adam, et contrôler leur convergence.

- Comprendrez, à l’ère de l’apprentissage profond, les nouvelles meilleures pratiques sur la configuration des ensembles train/dev/test et comment analyser les biais/variances

- Pourrez implémenter un réseau neuronal dans TensorFlow.

Ceci est le deuxième cours de spécialisation en apprentissage profond.

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What's inside

Syllabus

Aspects pratiques de l’apprentissage profond
Algorithmes d’optimisation
Réglage des hyper-paramètres, normalisation des lots et cadres de programmation
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches optimization algorithms like batch-wise gradient descent, RMSprop and Adam, which are essential for training neural networks
Introduces regularization techniques like L2, dropout, and batch normalization, which help improve the generalization performance of neural networks
Provides hands-on experience with TensorFlow, a widely used deep learning framework
Covers practical aspects of deep learning, such as hyperparameter tuning and model evaluation, which are crucial for real-world applications
Exposes learners to best practices in industry for building and deploying deep learning applications
This is the second course in a deep learning specialization

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Améliorez les réseaux neuronaux profonds with these activities:
Compile course materials
Prepare for success by organizing the materials for this course
Show steps
  • Gather notes, assignments, and quizzes from the course website
  • Make copies of any review sheets or guides
  • Organize all materials including syllabus, schedule, and textbook
  • Establish an organized system for locating and accessing materials
Read 'Deep Learning' by Ian Goodfellow
Gain a comprehensive understanding of deep learning concepts
Show steps
  • Obtain a copy of the book 'Deep Learning'
  • Read the book thoroughly, taking notes and highlighting important concepts
  • Complete the end-of-chapter exercises to test your understanding
  • Review the book regularly to reinforce your knowledge
Join a study group
Enhance your learning through collaboration and discussion
Show steps
  • Find classmates or peers who are interested in forming a study group
  • Establish regular meeting times and a study schedule
  • Take turns presenting course material and leading discussions
  • Work together on problem sets and assignments
Five other activities
Expand to see all activities and additional details
Show all eight activities
Go through TensorFlow tutorials
Practice using the TensorFlow framework by going through tutorials
Browse courses on TensorFlow
Show steps
  • Visit the TensorFlow website and follow the tutorials
  • Complete the hands-on exercises in the tutorials
  • Experiment with different TensorFlow functions and techniques
  • Check the TensorFlow documentation for additional resources
Complete online courses on neural networks
Enhance your understanding of neural networks through guided online courses
Browse courses on Neural Networks
Show steps
  • Enroll in online courses offered by platforms like Coursera, edX, or Udemy
  • Follow the video lectures and complete the assignments
  • Participate in online forums to discuss topics with peers
  • Review the course materials regularly to reinforce your knowledge
Build a neural network project using TensorFlow
Apply your knowledge by building a neural network project from scratch
Browse courses on Neural Networks
Show steps
  • Identify a problem that can be solved using a neural network
  • Gather and prepare the necessary data
  • Design and implement the neural network architecture
  • Train and evaluate the neural network
  • Deploy the neural network and monitor its performance
Write a blog post on a topic related to the course
Demonstrate your understanding and improve your writing skills
Show steps
  • Choose a topic that you are knowledgeable about and that is relevant to the course
  • Research the topic and gather information from credible sources
  • Write a well-structured blog post that is informative and engaging
  • Proofread your blog post carefully before publishing it
  • Share your blog post on social media or other platforms
Develop a personal project using deep learning
Apply your skills and knowledge to solve a real-world problem
Show steps
  • Identify a problem that you are interested in solving using deep learning
  • Gather and prepare the necessary data
  • Design and implement the deep learning model
  • Train and evaluate the model
  • Deploy the model and monitor its performance

Career center

Learners who complete Améliorez les réseaux neuronaux profonds will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers are in very high-demand because they can handle the ever-increasing amounts of data that is being used to define intricate business processes. The course "Améliorez les réseaux neuronaux profonds" is a great way to build a foundation in the effective practices of building neural networks and implementing and employing a variety of optimization algorithms. Both of these things are important to those wanting to work in this field.
Data Scientist
Data Scientists are responsible for analyzing and making sense of data. One of the important functions of a Data Scientist is building Machine Learning models. The course "Améliorez les réseaux neuronaux profonds" will help you build a foundation in some of the important algorithms and techniques used to build these models.
Quantitative Analyst
Quantitative Analysts, or "Quants" make use of complex mathematical and statistical techniques to solve problems within the financial industry. Given the course's focus on optimization algorithms and understanding how to best configure training, development, and test sets, people aiming to become a Quant may find it helpful.
Business Analyst
Business Analysts are responsible for determining how a business process should work. They typically work with stakeholders in the business to understand what needs to be achieved and then work with IT or other teams to determine how to implement the change. The course "Améliorez les réseaux neuronaux profonds" can be helpful to those in this role because it will help them build an understanding of machine learning algorithms and how they can be used to solve complex problems.
Software Engineer
Software Engineers work to design, maintain, and improve software systems. They may work on the front-end or back-end of a system and will typically have a focus on coding. The course "Améliorez les réseaux neuronaux profonds" may be helpful to Software Engineers that are looking to specialize in developing Machine Learning or Deep Learning models.
Data Analyst
Data Analysts are responsible for working with data to gain insights and identify trends. They will typically work with large datasets using tools and technologies to clean, transform, and analyze the data. The course "Améliorez les réseaux neuronaux profonds" may be helpful for Data Analysts looking to expand their knowledge in Machine Learning or those looking to focus more on developing Machine Learning models.
Statistician
Statisticians collect and analyze data to help make informed decisions. They may work in a variety of industries solving problems such as forecasting trends or understanding customer behavior. The course "Améliorez les réseaux neuronaux profonds" may be helpful to those who want to specialize in Machine Learning or Deep Learning and who want to use those skills as a Statistician.
Research Scientist
Research Scientists work in a variety of roles across many industries. They may be responsible for conducting research using scientific methods. The course "Améliorez les réseaux neuronaux profonds" can be useful to Research Scientists who want to focus on Machine Learning or Deep Learning in their research.
Machine Learning Architect
Machine Learning Architects help to design, create, and implement Machine Learning solutions. They work to ensure that the solutions are scalable and meet the needs of the business. The course "Améliorez les réseaux neuronaux profonds" can be helpful to Machine Learning Architects who are looking to gain a deeper understanding of how to optimize and deploy Machine Learning models.
Data Engineer
Data Engineers are responsibile for building and maintaining the infrastructure that stores and processes data. They are also responsible for ensuring that the data is secure and accessible to those who need it. The course "Améliorez les réseaux neuronaux profonds" can be helpful to Data Engineers who want to gain a deeper understanding of how to optimize the performance of Machine Learning models.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to analyze and solve complex business problems. They may work in a variety of industries, helping organizations to improve their operations and make better decisions. The course "Améliorez les réseaux neuronaux profonds" can be helpful to Operations Research Analysts looking to gain a deeper understanding of the Machine Learning algorithms that can be used to improve operations and make better decisions.
Financial Analyst
Financial Analysts help companies make sound financial decisions. They do this by analyzing financial data and making recommendations about investments, spending, and other financial matters. The course "Améliorez les réseaux neuronaux profonds" may be helpful to Financial Analysts who want to use Machine Learning algorithms to help make investment decisions or to analyze market trends.
Computer Vision Engineer
Computer Vision Engineers design, build, and maintain software systems that allow computers to see and understand the world around them. The course "Améliorez les réseaux neuronaux profonds" can be helpful to Computer Vision Engineers who need to develop deep learning models to enable computer vision systems to perform tasks like object detection, image classification, and facial recognition.
Natural Language Processing Engineer
Natural Language Processing Engineers design, build, and maintain software systems that allow computers to understand and generate human language. The course "Améliorez les réseaux neuronaux profonds" may be helpful to Natural Language Processing Engineers who need to develop deep learning models that allow for tasks like machine translation, text summarization, and question answering.
Software Developer
Software Developers design, build, and maintain software systems. They may work on a variety of projects, from small personal projects to large enterprise systems. The course "Améliorez les réseaux neuronaux profonds" may be helpful to Software Developers who want to learn more about building Machine Learning models.

Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Améliorez les réseaux neuronaux profonds.
Ce livre fournit une introduction complète à l'apprentissage profond, couvrant les concepts fondamentaux, les algorithmes et les applications. Il est particulièrement utile pour ceux qui cherchent à acquérir une compréhension approfondie des principes sous-jacents de l'apprentissage profond.
Ce livre fournit une introduction pratique à l'apprentissage profond avec Python. Il couvre les concepts fondamentaux, les algorithmes et les applications dans l'apprentissage profond.
Ce livre fournit une introduction pratique à TensorFlow 2 et Keras, les frameworks d'apprentissage automatique de Google. Il couvre les bases de TensorFlow 2 et Keras et fournit des exemples concrets pour vous aider à démarrer rapidement.
Ce livre fournit une introduction complète aux algorithmes d'optimisation, qui sont essentiels pour l'apprentissage profond. Il couvre les concepts fondamentaux, les algorithmes et les applications dans l'apprentissage automatique.
Ce livre est un guide pratique sur l’apprentissage profond avec Python. Il couvre les bases théoriques et les applications pratiques de l’apprentissage profond. Il est recommandé comme lecture complémentaire pour les personnes qui souhaitent apprendre à utiliser l’apprentissage profond avec Python.

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