糖心vlog官网观看

The content is for new comer of tf.keras OK.

When you have been in this area for a long time but still want to finish the whole specilization, pls go ahead and finish the final exam directly.

The week 2 of describing the back propagation is pretty nice, not too much detail but somehow it is using an example which is even clear at some points comparing to Prof.Ng 's course, however, wont let them competet each other

The course materials need to be updated to clearly show how to get to the labs. The video talks of one way but the real method is different. Although one is supposed to use the IBM Labs the staff gives direction to use google colabs. I downloaded the files to local machine and discovered the dependencies by accident.

One should not expect to learn much about Keras here. It is a basic introduction to DL and NN.

A very light introduction into training Neural Networks with Keras. Content is of good quality, but there is not enough of it to qualify as a course. Some weeks contain ~20 min worth of videos and a quiz. I bet all the material could have been condensed into just 1 week of video lectures and a final project week.

Final project is good, but it would be way better begin auto-graded instead of peer-graded.

A good course overall, with reasonably good explanations and step-by-step labs. However, the course did not really cover Recurrent Neural networks and Auto Encoders properly: the theory part was barely an overview and there were no code examples or labs related to them. It is a bit disappointing to see them in the syllabus but not covered in the course.

The course content is really good, because it explains a lot about the real background tech. Loved that one, also the code examples. BUT: the final exam was pretty all over the place and a big leap from what was in the course examples and notebook. Probably that should be reworked.

This course is too shallow in terms of content and too light in terms of workload, it can only serve as a stepping stone towards a more advanced course. It does accomplish its stated goal of offering an introduction to many Deep Learning concepts and Keras, but nothing beyond that.

The course content is very brief not much for getting a better understanding of topics. It can be a good starting course for someone but if you are interested in getting details of topics I recommend taking the courses of deeplearning.ai

+ Learnt a few things from the course.

+ The first lab about building a neural net from scratch was good.

- Passed the whole thing in just a few hours with no prior knowledge of Neural Networks or Keras.

- Short lectures.

- Very easy multiple choice questions.

- No presenter overlay on lecture videos.

- Overly prescriptive labs.

- Awkward and unnecessary dependency on Jupyter Notebook.

- Some poorly specified questions for labs.

Too basic and simple, especially for the different popular models. Not as good as I expected.

a bit outdated

Very Basic

Learning content was good, but making assignments was a nigthmare, I could not set up an IBM Cloud account, becasue they could not verify my payment card, when i asked them to help me, they just responded "We have reviewed your account/transaction and will not be able to offer services. No further information will be disclosed regarding this matter. Any card authorizations will reverse within 24-72 hours depending on the issuing bank.". They ghosted me completely and did not want to help. I do not recommend. It's no wonder that with this kind of customer attitude IBM is far behind the competition today.

One of my tasks is blocked by the team and I requested them to unblock it but no one has replied yet. It's the worst management. I have one day only to finish this course and get a certificate through my institutional email id because I am leaving the current institute on Monday. So i will be not able to complete the full course after this.

I want to continue my course and i have deadline until 25th of Nov. It is saying the page is under maintenance. Will you pay me that If I miss the deadline because of this meaintenance?

PLEASE CHANGE THE INSRUCTOR,WE NEED MORE DETAILS DISCUSSION

so bad

j

L'apprentissage profond (ou deep learning) est une sous-cat茅gorie de l'apprentissage automatique qui repose sur des r茅seaux de neurones artificiels. Ces r茅seaux, inspir茅s du fonctionnement du cerveau humain, sont utilis茅s pour mod茅liser des t芒ches complexes telles que la reconnaissance d'images, la traduction automatique, et la classification de texte. Keras est une biblioth猫que open-source en Python qui permet de construire et d鈥檈ntra卯ner des r茅seaux de neurones de mani猫re simple et rapide. Elle sert de couche d'abstraction au-dessus de biblioth猫ques plus bas niveau comme TensorFlow et Theano, facilitant ainsi le prototypage rapide des mod猫les d'apprentissage profond. 1. Qu'est-ce qu'un r茅seau de neurones ? Un r茅seau de neurones est compos茅 de couches qui sont form茅es de neurones (茅galement appel茅es unit茅s ou perceptrons). Ces neurones sont organis茅s en trois types de couches principales : Couche d'entr茅e : Recevoir les donn茅es d'entr茅e (par exemple, les pixels d'une image, un vecteur de texte, etc.). Couches cach茅es : Effectuer des transformations successives des donn茅es d'entr茅e via des poids et des fonctions d'activation. Couche de sortie : Produire une pr茅diction ou une classification 脿 partir des donn茅es trait茅es. 2. Principe du fonctionnement d'un r茅seau de neurones : Chaque neurone re莽oit une somme pond茅r茅e de ses entr茅es, puis applique une fonction d'activation pour produire une sortie. Par exemple, pour une couche cach茅e, la sortie 饾懄 y d'un neurone peut 锚tre calcul茅e par : 饾懄 = 饾憮 ( 饾憡 鈰� 饾懃 + 饾憦 ) y=f(W鈰厁+b) O霉 : 饾憡 W est le vecteur des poids, 饾懃 x est le vecteur des entr茅es, 饾憦 b est le biais, 饾憮 f est la fonction d'activation (comme ReLU, sigmo茂de, tanh, etc.). La fonction d'activation joue un r么le crucial en apportant de la non-lin茅arit茅 au mod猫le, permettant ainsi au r茅seau de neurones de capturer des relations complexes entre les donn茅es. 3. Keras : Pourquoi et comment l'utiliser ? Keras est une biblioth猫que qui simplifie la cr茅ation, l'entra卯nement et l'茅valuation de mod猫les de r茅seaux de neurones. Son design est tr猫s intuitif, ce qui en fait une excellente option pour les d茅butants. a. Installation de Keras Keras est int茅gr茅 dans TensorFlow, donc si vous installez TensorFlow, vous obtenez Keras automatiquement. bash Copier Modifier pip install tensorflow b. Cr茅er un mod猫le avec Keras Voici un exemple simple de cr茅ation d'un r茅seau de neurones en utilisant Keras pour r茅soudre un probl猫me de classification avec un jeu de donn茅es comme MNIST (images de chiffres manuscrits). 脡tapes : Importer les biblioth猫ques n茅cessaires python Copier Modifier import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Flatten from tensorflow.keras.datasets import mnist from tensorflow.keras.utils import to_categorical Charger et pr茅parer les donn茅es python Copier Modifier # Charger le jeu de donn茅es MNIST (x_train, y_train), (x_test, y_test) = mnist.load_data() # Normaliser les images pour qu'elles soient entre 0 et 1 x_train, x_test = x_train / 255.0, x_test / 255.0 # Convertir les labels en format one-hot (pour la classification) y_train = to_categorical(y_train, 10) y_test = to_categorical(y_test, 10) Construire le mod猫le python Copier Modifier # Cr茅er un mod猫le s茅quentiel model = Sequential() # Ajouter une couche de transformation des images en vecteurs model.add(Flatten(input_shape=(28, 28))) # Ajouter une couche dense (fully connected) avec 128 neurones model.add(Dense(128, activation='relu')) # Ajouter une couche de sortie avec 10 neurones (1 par chiffre de 0 脿 9) model.add(Dense(10, activation='softmax')) Compiler le mod猫le python Copier Modifier model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) Entra卯ner le mod猫le python Copier Modifier model.fit(x_train, y_train, epochs=5) 脡valuer le mod猫le sur les donn茅es de test python Copier Modifier test_loss, test_acc = model.evaluate(x_test, y_test) print(f"Test accuracy: {test_acc}") 4. Explication des 茅tapes cl茅s : Chargement des donn茅es : Ici, on charge le jeu de donn茅es MNIST, qui contient des images de chiffres 茅crits 脿 la main (28x28 pixels). Les labels sont les chiffres correspondants. Pr茅traitement des donn茅es : Les images sont normalis茅es pour que les valeurs des pixels soient entre 0 et 1 (cela aide le mod猫le 脿 converger plus rapidement). Cr茅ation du mod猫le : On construit un r茅seau de neurones avec une couche d'entr茅e qui a la m锚me forme que les images (28x28), suivie d'une couche dense avec 128 neurones, et une couche de sortie avec 10 neurones pour les 10 classes possibles. Compilation du mod猫le : On d茅finit l'optimiseur, la fonction de perte et les m茅triques. L'optimiseur Adam est souvent un bon choix pour les r茅seaux de neurones. Entra卯nement : Le mod猫le est entra卯n茅 sur les donn茅es d'entra卯nement pendant 5 茅poques. 脡valuation : Apr猫s l'entra卯nement, on 茅value la performance du mod猫le sur le jeu de test. 5. Concepts cl茅s 脿 comprendre : Fonction de co没t (Loss function) : Elle mesure l'茅cart entre la sortie pr茅dite et la v茅rit茅 terrain. Par exemple, pour un probl猫me de classification, la fonction de co没t pourrait 锚tre l'entropie crois茅e. Optimisation (Gradient Descent) : L'optimiseur ajuste les poids du mod猫le pour minimiser la fonction de co没t. Keras permet de choisir entre plusieurs optimisateurs comme SGD, Adam, RMSProp, etc. 脡poques et lots (Epochs & Batches) : Une 茅poque correspond 脿 un passage complet sur l'ensemble des donn茅es d'entra卯nement. Les donn茅es sont g茅n茅ralement divis茅es en lots (mini-batch) pour acc茅l茅rer l'entra卯nement. 6. Am茅liorations possibles du mod猫le : R茅seaux convolutifs (CNNs) : Pour des t芒ches comme la reconnaissance d'images, vous pouvez utiliser des r茅seaux de neurones convolutifs, qui sont plus efficaces pour capturer les caract茅ristiques spatiales des images. R茅seaux r茅currents (RNNs) : Si vous travaillez avec des s茅quences de donn茅es (comme du texte ou de la musique), vous pouvez utiliser des r茅seaux de neurones r茅currents (LSTM, GRU). R茅gularisation : L'ajout de techniques comme la r茅gularisation L2 ou Dropout peut aider 脿 茅viter le surapprentissage (overfitting). 7. Conclusion : Keras est un excellent outil pour d茅buter avec l'apprentissage profond gr芒ce 脿 sa simplicit茅 et sa flexibilit茅. En quelques lignes de code, vous pouvez cr茅er des mod猫les de r茅seaux de neurones efficaces pour des t芒ches vari茅es. L'un des avantages majeurs de Keras est son int茅gration avec TensorFlow, ce qui vous permet de passer 脿 des t芒ches plus complexes tout en profitant de la puissance de TensorFlow pour la production et l'optimisation des mod猫les.

A Truly Transformative Learning Experience The "Introduction to Deep Learning & Neural Networks with Keras" course on 糖心vlog官网观看 is an absolute game-changer for anyone serious about mastering AI. This course delivers a perfect balance of theory and hands-on practice, making complex deep learning concepts incredibly intuitive. The instructor鈥檚 clarity and expertise elevate the learning experience, breaking down neural networks, activation functions, and optimization techniques with unparalleled precision. The seamless integration of Keras and TensorFlow allows for immediate practical application, reinforcing concepts through real-world projects. What sets this course apart is its structured approach鈥攆rom foundational principles to implementing deep learning models with ease. Every lecture is engaging, insightful, and meticulously designed to ensure maximum retention and skill development. As someone committed to AI excellence, this course has sharpened my understanding and accelerated my journey toward becoming a top-tier AI engineer. I highly recommend it to anyone looking to build a strong foundation in deep learning.

I really like this course as it has only provided me with the tools that I need to understand neural networks without overhelming myself with abstract mathematics or theories. It is indeed good to be equipped with foundational theories and mathematics, but they do not build much intuition without direct practice. I believe that theories can be learned along the process rather than being stagnant because you cannot grasp them initially. With this course, I have become proactive because this is more concept-based and the mathematics has a good visualization to it. I like the way it is designed and how the final requirement aids you while allowing flexibility.

This course is highly recommended to those who have a background in Python programming, and interested in the basics of neural networks without the exhaustive mathematics.

This was a good course and well paced. But I have a comment about the final project. It was not clear to me if the same model was being re-trained and improved upon in 50 trials - or if the idea was to do 50 trials to capture just how good a model it is, using different splits of the data. Since we were asked to find the mean and sigma of 50 trials - I assumed those trials would be independent and each one performed with a fresh model - otherwise it is odd to find a mean and sigma of a system that is converging to a final result. In that case it's only the final mean and sigma of the final trial that matters. So I think some clarity would help there - without giving it all away with regard to how to do the project.

Everything depends on AI & ML now. Before enrolling in 糖心vlog官网观看鈥檚 IBM AI Engineering Professional Certificate Course, I had zero knowledge about Neural Networks & Deep learning. After completing the course, I am able to use these applications properly. It proved to be a total career game-changer.

糖心vlog官网观看 has proven to be a wonderful experience. I have taken classes from other organizations, but there鈥檚 no comparison to 糖心vlog官网观看.

Highly relevant examples were given which made me get a clear idea of the concepts explained. Deeply appreciate 糖心vlog官网观看 & IBM for designing this course very well.

Kudos to Instructor Alex Aklson, Ph.D., Data Scientist for providing such a wonderful learning聽Journey.

This course offers a thorough introduction to deep learning and neural networks with a focus on practical application using Keras. It is well-suited for anyone looking to get started with deep learning or deepen their understanding of neural networks. The combination of clear instruction, hands-on practice, and real-world examples makes it an invaluable learning resource. I highly recommend this course to anyone interested in deep learning and neural networks. Whether you are a beginner or have some prior experience, this course will provide you with the knowledge and skills necessary to excel in the field of deep learning.

IBM's "Introduction to Deep Learning & Neural Networks with Keras" course is a game-changer for anyone stepping into the realm of deep learning. From start to finish, it's a journey packed with clarity, hands-on practice, and real-world relevance. The course breaks down complex concepts into easier and digestible parts, making it perfect for beginners while still engaging for those with some background. What sets it apart is the practical approach using Keras, a user-friendly tool that simplifies deep learning implementation. I learnt so much from the final project, which was fascinating implementing machine learning!

I loved the Introduction to Deep Learning & Neural Networks with Keras course! It was a great way to learn the basics of deep learning and how to use Keras, a popular and user-friendly framework for building neural networks. The course was well-structured, with clear and engaging lectures, quizzes, assignments and peer reviews. The instructor was very knowledgeable and helpful, and the practical elements of the course were very helpful. I learned a lot from this course and I highly recommend it to anyone who wants to start their journey in deep learning!