Ask Gemma, Data Science¶

In [1]:
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_colwidth', None)

import warnings
warnings.filterwarnings("ignore")

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

/kaggle/input/data-assistants-with-gemma/submission_categories.txt
/kaggle/input/data-assistants-with-gemma/submission_instructions.txt
/kaggle/input/understanding-contextual-questions-answers/train.csv
/kaggle/input/gemma/keras/gemma_2b_en/2/config.json
/kaggle/input/gemma/keras/gemma_2b_en/2/tokenizer.json
/kaggle/input/gemma/keras/gemma_2b_en/2/metadata.json
/kaggle/input/gemma/keras/gemma_2b_en/2/model.weights.h5
/kaggle/input/gemma/keras/gemma_2b_en/2/assets/tokenizer/vocabulary.spm
/kaggle/input/1000-data-science-concepts/data_science_concepts.csv

Package Installations and Importing libraries¶

keras-nlp is a Python library that provides tools and utilities for natural language processing (NLP) tasks using Keras. It offers pre-built models for text classification, sequence tagging, and language modeling. The library includes functionalities for tokenization, word embeddings, text preprocessing, and evaluation metrics. It seamlessly integrates with Keras, making it easier to build and train NLP models.

In [2]:
# Install Keras 3 last. 
!pip install -q -U keras-nlp
!pip install -q -U keras>=3

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.

tensorflow-decision-forests 1.8.1 requires wurlitzer, which is not installed.

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.

tensorflow-decision-forests 1.8.1 requires wurlitzer, which is not installed.

tensorflow 2.15.0 requires keras<2.16,>=2.15.0, but you have keras 3.2.1 which is incompatible.

Python basic module

  • os: Provides ways to interact with the operating system and its environment variables.
  • torch: PyTorch library for deep learning applications.
  • pandas: Powerful data processing tool, ideal for handling CSV files and other forms of structured data.
  • re : Provides support for working with regular expressions, enabling powerful pattern-based string operations.

Wordcloud module

  • WordCloud : Python library used for generating word clouds, which are visual representations of text data where the size of each word indicates its frequency or importance.
  • STOPWORDS : set of commonly used words that are often excluded from text analysis because they typically do not carry significant meaning or contribute to the understanding of the text.

Keras module

  • GemmaCausalLM : A module provided by 'keras-nlp' library which provides a convenient way to build and train causal language models using Keras, which can be used for various NLP tasks such as text generation, machine translation, and dialog systems.

IPython.display module

  • Markdown : Used to output text in Markdown format.
  • display : Used to display objects in Jupyter notebooks.
In [3]:
import sklearn
import pandas as pd
import numpy as np
from wordcloud import WordCloud, STOPWORDS
import keras
import keras_nlp
from IPython.display import display, Markdown
import matplotlib.pyplot as plt
from keras_nlp.models import GemmaCausalLM
import re
import warnings
warnings.filterwarnings('ignore')

2024-04-14 17:22:31.472980: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-04-14 17:22:31.473188: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-04-14 17:22:31.597863: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
In [4]:
import os
os.environ["KERAS_BACKEND"] = "jax"  # Or "torch" or "tensorflow".
# Avoid memory fragmentation on JAX backend.
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]="1.00"

Loading Dataset¶

Dataset Link: https://www.kaggle.com/datasets/hserdaraltan/1000-data-science-concepts

This dataset covers more than 1000 common data science concepts. It covers several topics related to Statistics, Machine Learning, and Artificial Intelligence. It has two columns, one of which is questions or instructions, the other is responses to these instructions.

To download a dataset, follow these simple steps:

  1. Look for the "Input" option located below the "Notebook" section in the right-side menu.
  2. Click on the "+ Add Input" button.
  3. In the search bar that appears, type "1000+-data-science-concepts".
  4. Find the dataset in the search results and click the "+" button to add it to your notebook. This action will automatically download the dataset for you.
In [5]:
data = pd.read_csv('/kaggle/input/1000-data-science-concepts/data_science_concepts.csv',nrows=200)
data.head() # First 5 rows of the dataset
Out[5]:
Question Answer
0 What is under-fitting and overfitting in machine learning? Underfitting is when a model is too simple, and overfitting is when it's too complex, making it perform poorly on new data.
1 Can you explain what a false positive and a false negative are? A false positive incorrectly indicates a condition is present when it's not, while a false negative misses detecting a condition that is there.
2 Clarify the concept of Phase IV. Phase IV studies, also known as post-marketing surveillance, are conducted after a drug or medical product is made available to the general public. They aim to monitor the product's safety, efficacy, and long-term effects in a larger and more diverse population, providing valuable insights into real-world usage. Phase IV studies help regulators, healthcare providers, and patients make informed decisions about the product's continued use by assessing its risks and benefits over an extended period outside the controlled environment of clinical trials.
3 What is semi-supervised learning described in a short description? Semi-supervised learning integrates both labeled and unlabeled data during model training. By leveraging the abundance of unlabeled data alongside limited labeled data, it enhances model performance and generalization to new examples, offering scalability and efficiency in scenarios where acquiring labeled data is resource-intensive or impractical. This approach bridges the gap between supervised and unsupervised learning, unlocking the potential of vast unlabeled datasets for training robust machine learning models.
4 Discuss the parallelization of training in gradient boosting models. Parallelizing training of a gradient boosting model is indeed possible, leveraging the parallel processing capabilities of modern hardware, such as GPUs. Frameworks like XGBoost offer options like 'tree_method = 'gpu_hist'' to utilize GPUs for faster training. By distributing computation across multiple cores or devices simultaneously, parallelization accelerates the training process, significantly reducing training time and improving efficiency. This approach is particularly beneficial for large datasets and complex models, where traditional sequential training may be computationally intensive and time-consuming.
In [6]:
data.tail() # Last 5 rows of the dataset
Out[6]:
Question Answer
195 Why are activation functions required in neural networks? Activation functions introduce nonlinearity, enabling neural networks to learn complex relationships between inputs and outputs, enhancing model capacity and expressiveness.
196 Can you explain a bidirectional search algorithm? A bidirectional search algorithm runs two simultaneous searches: one forward from the starting point and one backward from the goal. The aim is to meet in the middle, thus potentially finding a solution faster than a unidirectional search.
197 Do gradient descent methods always converge to similar points? Gradient descent methods may converge to different local optima, which depend on the starting conditions and the nature of the cost function.
198 Describe word2vec. Word2vec is a suite of models used to produce word embeddings, trained to predict surrounding words in a linguistic context.
199 What is the difference between a generative and discriminative model? Generative models learn data categories, while discriminative models learn category distinctions. Discriminative models generally outperform generative models in classification tasks.

Dataset Information and Null Value Check

  • Check the number of features and values in dataset.
  • Check for any NULL values in the dataset
In [7]:
print("Information of Dataset: ")
print(data.info(),'\n')

print("Check for NULL values: ")
print(data.isnull().sum().sum())

print("Shape of Dataset: ")
print(data.shape)

Information of Dataset: 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 2 columns):
 #   Column    Non-Null Count  Dtype 
---  ------    --------------  ----- 
 0   Question  200 non-null    object
 1   Answer    200 non-null    object
dtypes: object(2)
memory usage: 3.2+ KB
None 

Check for NULL values: 
0
Shape of Dataset: 
(200, 2)

Visualize Data using Word Cloud¶

This code generates word clouds for each column in a DataFrame (data). Here's a step-by-step explanation:

  1. Initialization: Initialize an empty string comment_words to store concatenated words from all columns of the dataset and define a set of stopwords using the STOPWORDS set from the wordcloud library.

  2. Colormap Definition: Define the colormap to be used for generating the WordCloud images. In this case, the 'viridis' colormap is chosen.

  3. Iteration through Columns: Iterate through each column in the dataset, extracting the column name and its corresponding values. Concatenate all values in the column into a single string, converting them to uppercase.

  4. WordCloud Generation: Generate a WordCloud for each column using the concatenated string of values. Customize the WordCloud's width, height, stopwords, minimum font size, and colormap. Plot each WordCloud image on the respective subplot, setting the title of each subplot to indicate the column it represents. Finally, adjust the layout and display the plot.

Overall, this code visualizes the distribution of words in each column of the DataFrame by creating word clouds, providing insights into the most frequent words or terms within each column.

In [8]:
comment_words = ''
stopwords = set(STOPWORDS)

# Define the colormap
colormap = 'viridis'
 
# iterate through the csv file
for col in data.columns:
    # Concatenate all values in the column into a single string
    # and convert to lowercase
    comment_words += ' '.join(str(val).upper() for val in data[col]) + ' '

    # Generate WordCloud for the current column
    wordcloud = WordCloud(width=500, height=600,
                          stopwords=stopwords,
                          min_font_size=8).generate(comment_words)

    # Plot the WordCloud image
    plt.figure(figsize=(5, 5), facecolor=None)
    plt.imshow(wordcloud)
    plt.axis("off")
    plt.tight_layout(pad=0)
    plt.title(f"Word Cloud for {col}")
    plt.show()

Gemma Model¶

Gemma Model is a collection of lightweight open-source generative AI (GenAI) models developed by Google DeepMind. These models are primarily aimed at developers and researchers. Gemma was released alongside Gemini, Google's closed-source generative AI chatbots.

There are two main models in the Gemma collection: Gemma 2B and Gemma 7B. These models are text-to-text decoder large language models (LLMs) with pretrained and instruction-tuned variants. Gemma 2B has a neural network with 2 billion parameters, while Gemma 7B has a neural network with seven billion parameters.

Google offers pretrained and instruction-tuned Gemma models suitable for running on laptops and workstations. These models are available to developers through various platforms. Additionally, Meta's Llama 2 is another open-source AI model designed to run on laptops, serving as more of a business tool compared to Gemma. Gemma is often favored for scientific tasks, while Llama 2 is considered more suitable for general-purpose tasks.

Inputs and Outputs

  • Input: Gemma models take in text strings, which can range from questions and prompts to longer documents that require summarization.

  • Output: In response, they generate text in English, offering answers, summaries, or other forms of text-based output, tailored to the input provided.

    image.png

The provided code snippet creates an instance of the GemmaCausalLM model and assigns it to the variable gemma_lm. It creates the model from a preset configuration named "gemma_instruct_2b_en".This preset specifies the architecture, hyperparameters, and other settings for the model.

In [9]:
#Create the model using the from_preset method
gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("gemma_2b_en")
gemma_lm.summary()

Attaching 'config.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Kaggle notebook...
Attaching 'config.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Kaggle notebook...
Attaching 'model.weights.h5' from model 'keras/gemma/keras/gemma_2b_en/2' to your Kaggle notebook...
Attaching 'tokenizer.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Kaggle notebook...
Attaching 'assets/tokenizer/vocabulary.spm' from model 'keras/gemma/keras/gemma_2b_en/2' to your Kaggle notebook...
normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.

Preprocessor: "gemma_causal_lm_preprocessor"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Tokenizer (type)                                   ┃                                             Vocab # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ gemma_tokenizer (GemmaTokenizer)                   │                                             256,000 │
└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘

Model: "gemma_causal_lm"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                  ┃ Output Shape              ┃         Param # ┃ Connected to               ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ padding_mask (InputLayer)     │ (None, None)              │               0 │ -                          │
├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
│ token_ids (InputLayer)        │ (None, None)              │               0 │ -                          │
├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
│ gemma_backbone                │ (None, None, 2048)        │   2,506,172,416 │ padding_mask[0][0],        │
│ (GemmaBackbone)               │                           │                 │ token_ids[0][0]            │
├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
│ token_embedding               │ (None, None, 256000)      │     524,288,000 │ gemma_backbone[0][0]       │
│ (ReversibleEmbedding)         │                           │                 │                            │
└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘

 Total params: 2,506,172,416 (9.34 GB)

 Trainable params: 2,506,172,416 (9.34 GB)

 Non-trainable params: 0 (0.00 B)

Test Model before Tuning¶

Before we start the finetuning process, let's see how the Gemma model performs out of the box on our dataset. This section will show you how to run a simple question-answering test.

In [10]:
gemma_lm.generate("What is supervised machine learning?", max_length=64)

WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1713115454.782714      24 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
W0000 00:00:1713115454.856710      24 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update
W0000 00:00:1713115454.900669      24 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update
Out[10]:
'What is supervised machine learning?\n\nSupervised machine learning is a type of machine learning where the algorithm is trained on a set of labeled data. The algorithm is then used to predict the outcome of new data.\n\nSupervised machine learning is a type of machine learning where the algorithm is trained on a set of labeled'
In [11]:
print(gemma_lm.generate("Can you explain neural networks?", max_length=256))

W0000 00:00:1713115475.541716      24 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update

Can you explain neural networks?

A 100-W, 120-V lightbulb has a resistance of $12 \Omega$ when cold $\left(20^{\circ} \mathrm{C}\right)$ and $140 \Omega$ when on (hot). Calculate its power consumption at the instant it is turned on.

A 100-turn, 2.0-cm-diameter coil is at rest with its axis vertical. A uniform magnetic field $60^{\circ}$ away from vertical increases from 0.50 T to 1.50 T in 0.60 s. What is the induced emf in the coil?

A 100-W, 120-V lightbulb has a resistance of $12 \Omega$ when cold $\left(20^{\circ} \mathrm{C}\right)$ and $140 \Omega$ when on (hot). a. What is the resistance of the lightbulb when hot? b. What is the change in electrical energy requred if the light is on for one hour at room temperature? c. Suppose the lightbulb is turned on when the temperature is $20
In [12]:
#Prepare the dataset for fine-tuning
dataset = []
    
for index, row in data.iterrows():
    question, answer = row['Question'], row['Answer']
    template = (f"Question:\n{question}\n\nAnswer:\n{answer}")
    dataset.append(template)

Fine tuning with LoRA¶

LoRA (Low-Rank Adaptation of Large Language Models) is a technique used to improve the efficiency and performance of large language models by significantly reducing the number of training parameters. The line enables LoRA specifically on the backbone architecture of the language model gemma_lm.

In [13]:
#Enable LoRA for the model and set the LoRA rank to 64.
gemma_lm.backbone.enable_lora(rank=64)
gemma_lm.summary()

Preprocessor: "gemma_causal_lm_preprocessor"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Tokenizer (type)                                   ┃                                             Vocab # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ gemma_tokenizer (GemmaTokenizer)                   │                                             256,000 │
└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘

Model: "gemma_causal_lm"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                  ┃ Output Shape              ┃         Param # ┃ Connected to               ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ padding_mask (InputLayer)     │ (None, None)              │               0 │ -                          │
├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
│ token_ids (InputLayer)        │ (None, None)              │               0 │ -                          │
├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
│ gemma_backbone                │ (None, None, 2048)        │   2,527,995,904 │ padding_mask[0][0],        │
│ (GemmaBackbone)               │                           │                 │ token_ids[0][0]            │
├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
│ token_embedding               │ (None, None, 256000)      │     524,288,000 │ gemma_backbone[0][0]       │
│ (ReversibleEmbedding)         │                           │                 │                            │
└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘

 Total params: 2,527,995,904 (9.42 GB)

 Trainable params: 21,823,488 (83.25 MB)

 Non-trainable params: 2,506,172,416 (9.34 GB)

Memory Control - Epochs¶

This block of code compiles the model using the specified loss function, optimizer, and metrics

  1. SparseCategoricalCrossentropy is used as the loss function to compute the cross-entropy loss between the true labels and the predicted probabilities output by the model.It measures the difference between the true distribution of the labels and the predicted distribution.

  2. Adam optimizer efficiently optimizes the parameters (weights and biases) of the neural network during training. It adapts the learning rate for each parameter individually based on the estimates of the first and second moments of the gradients.

  3. SparseCategoricalAccuracy is used as the evaluation metric, which calculates accuracy during training.

In [14]:
# Limit the input sequence length to 512 (to control memory usage).
gemma_lm.preprocessor.sequence_length = 512
# Use AdamW 
optimizer = keras.optimizers.AdamW(
    learning_rate=5e-5,
    weight_decay=0.01,
)
optimizer.exclude_from_weight_decay(var_names=["bias", "scale"])

# Compile the model
gemma_lm.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=optimizer,
    weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()]
)
In [15]:
gemma_lm.fit(dataset, epochs=6, batch_size=1)

Epoch 1/6

W0000 00:00:1713115530.386434      82 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update

200/200 ━━━━━━━━━━━━━━━━━━━━ 190s 711ms/step - loss: 0.2695 - sparse_categorical_accuracy: 0.5686
Epoch 2/6
200/200 ━━━━━━━━━━━━━━━━━━━━ 142s 711ms/step - loss: 0.2221 - sparse_categorical_accuracy: 0.6081
Epoch 3/6
200/200 ━━━━━━━━━━━━━━━━━━━━ 142s 711ms/step - loss: 0.2069 - sparse_categorical_accuracy: 0.6304
Epoch 4/6
200/200 ━━━━━━━━━━━━━━━━━━━━ 142s 711ms/step - loss: 0.1856 - sparse_categorical_accuracy: 0.6661
Epoch 5/6
200/200 ━━━━━━━━━━━━━━━━━━━━ 142s 711ms/step - loss: 0.1574 - sparse_categorical_accuracy: 0.7196
Epoch 6/6
200/200 ━━━━━━━━━━━━━━━━━━━━ 142s 711ms/step - loss: 0.1265 - sparse_categorical_accuracy: 0.7740
Out[15]:
<keras.src.callbacks.history.History at 0x7b57646789a0>

Test Model after Tuning¶

After training, let's see how much our Gemma model has improved. We'll rerun the question-answering test and compare the results to the pre-finetuning performance

In [16]:
print(gemma_lm.generate("What is supervised machine learning?", max_length=256))

W0000 00:00:1713116405.944183      24 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update

What is supervised machine learning?

Machine learning is a branch of artificial intelligence that allows computers to learn and perform tasks through experience, rather than being explicitly programmed. In supervised learning, computers are exposed to a labeled dataset of input-output pairs, where the output is known in advance. During training, the computers learn predictive models by analyzing the data patterns and relationships, and then make predictions based on new input data. This process enables computers to learn to perform tasks without explicit programming, making machine learning essential for applications like image classification, natural language processing, and recommendation systems.
In [17]:
print(gemma_lm.generate("What is regression? Which models can you use to solve a regression problem? ", max_length=256))

What is regression? Which models can you use to solve a regression problem? 

Regression analysis is a statistical technique used to determine the relationship between two or more variables, typically to predict a continuous outcome. There are various regression models available, each with its own set of assumptions and limitations. Understanding the assumptions and limitations of different regression models is crucial for correctly interpreting the results and ensuring accurate predictions.
In [18]:
print(gemma_lm.generate("What is K-fold cross-validation? ", max_length=256))

What is K-fold cross-validation? 

K-fold cross-validation involves dividing data into consecutive folds of equal size and then validating the model on each fold. This method is commonly used to assess the model's generalization ability.
In [19]:
print(gemma_lm.generate("What are the main parameters of the decision tree model?", max_length=256))

What are the main parameters of the decision tree model?

A 2-year-oldthrowaway rule for choosing leaf nodes is to choose data nodes with (a) few categories, and (rule) (b) many missing data values.

Consider the following statements:

<code> Sentiniel s = new Sentinel(5);
s.mRecyclerView. Đi
 sentinel. Đi
 return sentinel.mValue;
</code>

(c) Which data node access path is taken?

A 2-year-old throwaway rule for choosing leaf nodes is to choose data nodes with (a) few categories, and (b) many missing data values.
In [20]:
print(gemma_lm.generate("Can you explain neural networks?", max_length=256))

Can you explain neural networks?

A neural network is a network of computational nodes inspired by the human brain that processes information. It consists of multiple layers of nodes, where each node receives input from adjacent layers and processes it before passing the output to subsequent layers, mimicking the process of information transmission in the human brain. Neural networks are widely used in machine learning and artificial intelligence for tasks such as image recognition, natural language processing, and pattern recognition. By mimicking the human brain's processing capabilities, neural networks demonstrate impressive learning abilities, enabling them to perform complex tasks without explicit programming.
In [21]:
print(gemma_lm.generate("Can you explain what a false positive and a false negative are?", max_length=256))

Can you explain what a false positive and a false negative are?

A false positive and a false negative are outcomes that are erroneously classified as positive or negative, respectively, when they are actually the opposite. They occur when there is ambiguity in the classification process, leading to incorrect predictions. For example, a model may incorrectly classify a patient as healthy when they actually have symptoms of illness, resulting in suboptimal healthcare outcomes. Similarly, a false negative diagnosis can lead to delayed treatment or misdiagnosis, compromising patient health.