Choosing the right framework in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the popular machine learning framework.

Agentic AI

import [1] as ml

Drag options to blanks, or click blank then click option'

Atensorflow

Bpandas

Cmatplotlib

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Using pandas which is for data manipulation, not ML framework.

Using matplotlib which is for plotting, not ML framework.

TensorFlow is a popular framework for machine learning and AI tasks.

2fill in blank

medium

Complete the code to create a simple neural network layer using the framework.

Agentic AI

model = ml.keras.Sequential([ml.keras.layers.Dense([1], activation='relu')])

Drag options to blanks, or click blank then click option'

Aoptimizer

Binput_shape

C64

Dloss

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'input_shape' which is a parameter but not the neuron count.

Using 'optimizer' or 'loss' which are unrelated here.

The number 64 specifies the number of neurons in the Dense layer.

3fill in blank

hard

Fix the error in the code to compile the model with an optimizer.

Agentic AI

model.compile(optimizer=[1], loss='sparse_categorical_crossentropy')

Drag options to blanks, or click blank then click option'

A'relu'

B'adam'

C'accuracy'

Dadam

Attempts:

3 left

💡 Hint

Common Mistakes

Not using quotes around the optimizer name causes errors.

Using 'accuracy' or 'relu' as optimizer which are incorrect.

The optimizer name must be a string, so it needs quotes.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters features with importance above 0.1.

Agentic AI

important_features = {k: v for k, v in feature_importances.items() if v [1] [2]

Drag options to blanks, or click blank then click option'

A>

B0.1

C<

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' reverses the filter logic.

Using 1 as threshold filters out too many features.

The code keeps features with importance greater than 0.1.

5fill in blank

hard

Fill all three blanks to create a training loop that runs for 5 epochs and prints loss each epoch.

Agentic AI

for epoch in range([1]):
    history = model.fit(X_train, y_train, epochs=1, verbose=0)
    print(f"Epoch { [2] }: Loss = {{history.history['[3]'][0]}}")

Drag options to blanks, or click blank then click option'

A10

Bepoch + 1

Closs

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using 10 epochs instead of 5.

Printing epoch without adding 1 starts from 0.

Using 'accuracy' instead of 'loss' in history key.

The loop runs 5 times, printing the loss after each epoch with epoch number starting at 1.

Practice

1. Which AI framework is best for beginners who want to learn with simple projects?

easy

A. Apache MXNet

B. PyTorch Lightning

C. Caffe

D. TensorFlow with Keras

Solution

1. Step 1: Identify beginner-friendly frameworks

   TensorFlow with Keras offers simple APIs and good tutorials for beginners.

2. Step 2: Compare with other options

   PyTorch Lightning and MXNet are more advanced; Caffe is less beginner-friendly.

3. Final Answer:

   TensorFlow with Keras -> Option D

4. Quick Check:

   Beginner-friendly = TensorFlow with Keras [OK]

Hint: Pick frameworks known for easy tutorials and simple APIs [OK]

Common Mistakes:

• Choosing complex frameworks for beginners
• Ignoring community support and tutorials
• Confusing advanced features with beginner ease

2. Which of the following is the correct way to import PyTorch in Python?

easy

A. import torch

B. import pytorch

C. from torch import pytorch

D. import Torch

Solution

1. Step 1: Recall PyTorch import syntax

   The official import statement is import torch.

2. Step 2: Check other options for errors

   'import Torch' uses incorrect capitalization; 'import pytorch' uses wrong module name; 'from torch import pytorch' tries to import a non-existent submodule.

3. Final Answer:

   import torch -> Option A

4. Quick Check:

   Standard import = import torch [OK]

Hint: Use official module name exactly as documented [OK]

Common Mistakes:

• Using wrong module names
• Trying to import submodules incorrectly
• Using uncommon aliases without reason

3. What will be the output of this code snippet using TensorFlow?

import tensorflow as tf
x = tf.constant([1, 2, 3])
y = tf.constant([4, 5, 6])
z = tf.add(x, y)
print(z.numpy())

medium

A. [1 2 3 4 5 6]

B. [4 10 18]

C. [5 7 9]

D. Error: TensorFlow add requires scalar inputs

Solution

1. Step 1: Understand tf.add operation

   tf.add adds element-wise values of two tensors of the same shape.

2. Step 2: Calculate element-wise addition

   [1+4, 2+5, 3+6] = [5, 7, 9]. The print statement outputs the numpy array.

3. Final Answer:

   [5 7 9] -> Option C

4. Quick Check:

   Element-wise add = [5 7 9] [OK]

Hint: Add tensors element-wise to get sum of each position [OK]

Common Mistakes:

• Confusing concatenation with addition
• Expecting scalar inputs only
• Misreading output format

4. You wrote this PyTorch code but get an error:

import torch
x = torch.tensor([1, 2, 3])
y = torch.tensor([4, 5])
z = x + y
print(z)

What is the main issue?

medium

A. Tensors have different shapes and cannot be added directly

B. You must convert tensors to numpy arrays before adding

C. PyTorch does not support tensor addition

D. You forgot to call .item() on tensors before adding

Solution

1. Step 1: Check tensor shapes

   x has shape (3,), y has shape (2,). They differ in length.

2. Step 2: Understand addition requirements

   PyTorch requires tensors to have compatible shapes for element-wise addition; these shapes are incompatible.

3. Final Answer:

   Tensors have different shapes and cannot be added directly -> Option A

4. Quick Check:

   Shape mismatch causes addition error [OK]

Hint: Check tensor shapes before adding [OK]

Common Mistakes:

• Assuming automatic broadcasting without matching shapes
• Converting unnecessarily to numpy
• Misusing .item() which extracts single values

5. You want to build an AI agent that can learn from text and images, and you want fast prototyping with easy debugging. Which framework should you choose?

hard

A. TensorFlow with low-level API

B. PyTorch with dynamic computation graphs

C. Scikit-learn

D. Theano

Solution

1. Step 1: Identify framework features needed

   Fast prototyping and easy debugging require dynamic computation graphs.

2. Step 2: Match features to frameworks

   PyTorch supports dynamic graphs and is popular for research and prototyping; TensorFlow low-level API is more complex; Scikit-learn is for classical ML, not deep learning; Theano is outdated.

3. Final Answer:

   PyTorch with dynamic computation graphs -> Option B

4. Quick Check:

   Dynamic graphs = PyTorch [OK]

Hint: Dynamic graphs help fast prototyping and debugging [OK]

Common Mistakes:

• Choosing static graph frameworks for prototyping
• Using classical ML libraries for deep learning tasks
• Picking outdated or unsupported frameworks