# TensorFlow & Keras (Data Science & ML)

> Sequential API, Functional API, layers, callbacks, checkpoints, TensorBoard, model saving

- 22 interview questions
- Senior
- [Interview Questions: Data Science & ML](https://sharpskill.dev/en/technologies/data-science/interview-questions.md)

## 1. What is the main difference between Keras Sequential API and Functional API?

**Answer**

Sequential API allows creating models layer by layer in a linear fashion, where each layer has exactly one input and one output. Functional API offers more flexibility by enabling complex architectures: multiple inputs, multiple outputs, residual connections, and shared layer graphs. Use Sequential for simple architectures and Functional for more advanced cases.

## 2. How to create a Sequential model with a 64-neuron Dense layer followed by a 10-neuron output layer?

**Answer**

The standard method is to instantiate tf.keras.Sequential() then use model.add() to add layers one by one, or pass a list of layers directly to the constructor. Each Dense layer takes the number of units as parameter, and the first layer requires specifying input_shape to define the input data shape.

## 3. What is the role of the 'softmax' activation function in an output layer?

**Answer**

The softmax function transforms logits (raw outputs) into probabilities that sum to 1, which is ideal for multi-class classification. Each output represents the probability of belonging to a class. It is typically used with categorical_crossentropy loss for one-hot labels or sparse_categorical_crossentropy for integer labels.

## 19 more questions available

- How to define a model with the Functional API having two distinct inputs?
- Which callback to use to stop training when validation loss no longer improves?

Sign up for free: https://sharpskill.dev/en/login

## Other Data Science & ML interview topics

- [Python Basics](https://sharpskill.dev/en/technologies/data-science/interview-questions/python-basics.md): 25 questions, Junior
- [Python Object-Oriented Programming](https://sharpskill.dev/en/technologies/data-science/interview-questions/python-oop.md): 20 questions, Junior
- [Python Data Structures](https://sharpskill.dev/en/technologies/data-science/interview-questions/python-data-structures.md): 20 questions, Junior
- [Git Fundamentals](https://sharpskill.dev/en/technologies/data-science/interview-questions/git-fundamentals.md): 18 questions, Junior
- [SQL Basics](https://sharpskill.dev/en/technologies/data-science/interview-questions/sql-basics.md): 20 questions, Junior
- [NumPy Fundamentals](https://sharpskill.dev/en/technologies/data-science/interview-questions/numpy-fundamentals.md): 22 questions, Junior
- [Pandas Basics](https://sharpskill.dev/en/technologies/data-science/interview-questions/pandas-basics.md): 22 questions, Junior
- [Jupyter & Google Colab](https://sharpskill.dev/en/technologies/data-science/interview-questions/jupyter-colab.md): 16 questions, Junior
- [SQL Joins & Advanced Queries](https://sharpskill.dev/en/technologies/data-science/interview-questions/sql-joins-advanced.md): 22 questions, Mid-Level
- [Advanced Pandas](https://sharpskill.dev/en/technologies/data-science/interview-questions/pandas-advanced.md): 24 questions, Mid-Level
- [Visualization with Matplotlib & Seaborn](https://sharpskill.dev/en/technologies/data-science/interview-questions/matplotlib-seaborn.md): 20 questions, Mid-Level
- [Interactive Visualizations with Plotly](https://sharpskill.dev/en/technologies/data-science/interview-questions/plotly-interactive.md): 18 questions, Mid-Level
- [Descriptive Statistics](https://sharpskill.dev/en/technologies/data-science/interview-questions/statistics-descriptive.md): 20 questions, Mid-Level
- [Inferential Statistics](https://sharpskill.dev/en/technologies/data-science/interview-questions/statistics-inferential.md): 24 questions, Mid-Level
- [Web Scraping](https://sharpskill.dev/en/technologies/data-science/interview-questions/web-scraping.md): 18 questions, Mid-Level
- [BigQuery & Cloud Data](https://sharpskill.dev/en/technologies/data-science/interview-questions/bigquery-cloud.md): 18 questions, Mid-Level
- [Feature Engineering](https://sharpskill.dev/en/technologies/data-science/interview-questions/feature-engineering.md): 22 questions, Mid-Level
- [Supervised ML: Regression](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-supervised-regression.md): 24 questions, Mid-Level
- [Supervised ML: Classification](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-supervised-classification.md): 24 questions, Mid-Level
- [Decision Trees & Ensembles](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-trees-ensembles.md): 24 questions, Mid-Level
- [Unsupervised ML](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-unsupervised.md): 22 questions, Mid-Level
- [ML Pipelines & Validation](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-pipelines-validation.md): 22 questions, Mid-Level
- [Time Series & Forecasting](https://sharpskill.dev/en/technologies/data-science/interview-questions/time-series-forecasting.md): 22 questions, Mid-Level
- [Deep Learning Fundamentals](https://sharpskill.dev/en/technologies/data-science/interview-questions/deep-learning-fundamentals.md): 24 questions, Senior
- [CNN & Image Classification](https://sharpskill.dev/en/technologies/data-science/interview-questions/cnn-image-classification.md): 24 questions, Senior
- [RNN & Sequences](https://sharpskill.dev/en/technologies/data-science/interview-questions/rnn-sequences.md): 22 questions, Senior
- [Transformers & Attention](https://sharpskill.dev/en/technologies/data-science/interview-questions/transformers-attention.md): 24 questions, Senior
- [NLP & Hugging Face](https://sharpskill.dev/en/technologies/data-science/interview-questions/nlp-huggingface.md): 24 questions, Senior
- [GenAI & LangChain](https://sharpskill.dev/en/technologies/data-science/interview-questions/genai-langchain.md): 24 questions, Senior
- [MLOps & Deployment](https://sharpskill.dev/en/technologies/data-science/interview-questions/mlops-deployment.md): 24 questions, Senior

---

Source: SharpSkill (https://sharpskill.dev), tech interview preparation for your real stack.
HTML version of this page: https://sharpskill.dev/en/technologies/data-science/interview-questions/tensorflow-keras