Retentive Network: A Novel Neural Network Architecture

October 23, 2023


In the ever-evolving field of artificial intelligence and machine learning, breakthroughs and innovations are continuously pushing the boundaries of what is possible. One of the most exciting recent developments in this domain is the emergence of the Retentive Network, a groundbreaking neural network architecture that promises to revolutionize the way we approach various AI tasks. In this blog post, we will explore what the Retentive Network is, how it works, and the potential applications that make it a compelling addition to the world of neural networks.

Understanding the Retentive Network

Retentive Network (RetNet) is one such innovation, a novel neural network architecture designed to meet the demands of large language models, while simultaneously achieving training parallelism, low-cost inference, and impressive performance. In this blog post, we delve into the intricacies of RetNet, exploring its unique features and theoretical foundations, and shedding light on the potential it holds for the future of deep learning. Unlike traditional neural networks that rely on fixed input-output relationships, the Retentive Network is designed to store and remember context, allowing it to make more informed decisions and predictions.

Key Features of the Retentive Network:

  1. Temporal Context Retention: The Retentive Network excels at retaining temporal context. It can remember previous inputs, outputs, and even intermediate states, allowing it to capture long-range dependencies in sequential data. This is particularly valuable for tasks like natural language processing, where understanding the context of previous words is crucial for interpreting the meaning of the current word.
  2. Adaptive Learning: Instead of static weights and biases, the Retentive Network adapts its internal parameters as it learns. This adaptability enables it to continually adjust its internal representations to optimize performance, making it well-suited for non-stationary data distributions.
  3. Parallel Processing: The architecture of the Retentive Network allows for parallel processing of multiple streams of information, improving its efficiency and reducing training time.
  4. Attention Mechanisms: The Retentive Network incorporates attention mechanisms, enabling it to focus on specific elements of input data that are most relevant to the task at hand. This feature is particularly beneficial for tasks involving complex or large datasets.

The Theoretical Connection

At the heart of RetNet lies a groundbreaking theoretical connection between two fundamental concepts in neural network architecture: recurrence and attention mechanisms. These concepts, while seemingly distinct, have been harmoniously fused in RetNet to create a foundational building block for its design.

Recurrence, often associated with recurrent neural networks (RNNs), enables the cyclic flow of information within a network. It is a key component for handling sequences of data where the context from previous steps is essential. On the other hand, attention mechanisms, commonly used in models like Transformers, enable the model to focus on specific parts of the input sequence when processing it.

What the authors of RetNet claim is that they have successfully established a theoretical relationship between recurrence and attention mechanisms. This connection is not just a theoretical curiosity; it is at the core of RetNet's design. It promises to unlock new insights into how these two seemingly disparate architectural elements can work synergistically within a neural network, potentially leading to improved model performance, efficiency, and a deeper understanding of how neural networks process sequential data.

The Retention Mechanism

The retention mechanism is a central element of the RetNet architecture, playing a pivotal role in sequence modeling. It is designed to effectively handle sequences of data and supports three key computation paradigms: parallel, recurrent, and chunkwise recurrent


  1. Parallel Computation: The retention mechanism is proficient at processing sequences in parallel. This parallel representation allows it to handle multiple elements of a sequence simultaneously, drastically improving processing speed.
  2. Recurrent Computation: RetNet's recurrent representation excels in low-cost inference. With an O(1) complexity, it ensures that making predictions or generating sequences using the trained RetNet model is efficient and does not require extensive computational resources. This results in reduced GPU memory usage, faster decoding speed, and lower latency during inference.
  3. Chunkwise Recurrent Computation: The chunkwise recurrent representation is specifically designed for efficient long-sequence modeling. It divides lengthy sequences into smaller chunks or segments, processing them in parallel while maintaining a recurrent summary of the chunks. This approach ensures that RetNet can efficiently handle extended input sequences with linear complexity, striking a balance between efficiency and modeling accuracy.

Overall Architecture

In an L-layer Retentive Network, MSR and Feed-Forward Network (FFN) modules are stacked to process input sequences. The model begins by transforming the input sequence into vectors using a word embedding layer, and these embeddings are passed through the MSR and FFN functions sequentially, with layer normalization applied before each step.

During training, RetNet makes optimal use of GPU resources by employing both parallel and chunkwise recurrent representations, providing efficiency in terms of computation and memory usage. On the other hand, for inference, the recurrent representation is leveraged, making it particularly suitable for autoregressive decoding, significantly reducing memory requirements and inference latency.

Experimental Results

The experimental results presented by the authors of RetNet are a testament to its capabilities in various aspects of language modeling. These results provide insights into the advantages of using RetNet for natural language processing tasks. Let's break down the key aspects highlighted in these experiments:

  1. Favorable Scaling Results: RetNet demonstrates remarkable scalability, excelling as the model size or complexity increases. This scalability is critical for developing more powerful language models that can understand and generate complex language patterns.
  2. Parallel Training: RetNet exhibits high efficiency in parallel training. This means that during the model training process, multiple computations can be performed simultaneously, leveraging the power of modern hardware like GPUs and distributed computing setups. This not only speeds up the training process but also optimizes resource utilization.
  3. Low-Cost Deployment: One of RetNet's standout features is its ability to be deployed for inference at a low computational cost. This is particularly crucial for practical applications where efficiency during deployment is paramount, especially in real-time or resource-constrained environments.
  4. Efficient Inference: RetNet is adept at efficiently performing inference tasks. This means it can generate text or make predictions with minimal computational resources, reducing GPU memory usage and ensuring low latency. This efficiency is highly beneficial for applications like chatbots, translation services, and real-time response systems.

Retentive Network’s Comparison with Transformer Model

In comparison to the widely adopted Transformer model, RetNet offers several advantages:

  1. Inference Cost: RetNet outperforms Transformer in terms of inference cost. It requires fewer computational resources to make predictions, making it a more efficient choice for applications that demand real-time or resource-efficient inference.
  2. Training Parallelism: RetNet's superiority in training parallelism implies that it can be trained more efficiently. This not only saves time but also optimizes resource utilization during the training phase, making it a practical choice for large-scale model training.
  3. Long-Sequence Modeling: RetNet's robust performance in modeling long sequences surpasses the limitations faced by Transformer models, which often struggle with memory constraints when dealing with extended sequences of data. RetNet's ability to handle long sequences efficiently is a significant advantage for a wide range of tasks.

Applications of the Retentive Network

The Retentive Network's unique capabilities open the door to a wide range of applications across various domains. Here are a few areas where the Retentive Network can have a significant impact:

  1. Natural Language Processing (NLP): Understanding the context in natural language is a challenging task, but Retentive Network's ability to retain temporal context makes it an excellent choice for tasks like machine translation, sentiment analysis, and text generation.
  2. Speech Recognition: Retentive Network's memory retention and adaptability make it a powerful tool for improving the accuracy and robustness of speech recognition systems.
  3. Recommendation Systems: When recommending products, content, or services, the Retentive Network can take into account a user's historical interactions and preferences, providing more personalized and effective recommendations.
  4. Time-Series Analysis: Analyzing time-series data, such as financial data or sensor readings, often requires capturing long-term dependencies. The Retentive Network's temporal context retention makes it well-suited for these applications.
  5. Autonomous Vehicles: Autonomous vehicles require the ability to understand and react to complex and dynamic environments. The Retentive Network can play a crucial role in enhancing the decision-making processes of self-driving cars.

Tutorial: RetNet Text Generation

If you require extra GPU resources for the tutorials ahead, you can explore the offerings on E2E CLOUD, which provides a diverse selection of GPUs, making them a suitable choice for more advanced LLM-based applications as well.

Importing Libraries

The code starts by importing necessary Python libraries, including torch for PyTorch, which is a popular deep learning framework.

# Import the PyTorch library
import torch

Data Loading

  • The code loads data from an input text file named ‘input.txt’ using open() and reads the content into the variable text.
  • It calculates the length of the text data using len(text).

# Download a text file from a GitHub repository
# Open the downloaded file for reading with UTF-8 encoding
with open('input.txt', 'r', encoding='utf-8') as f:
    text =

Data Preprocessing

  • The unique characters in the text are identified and stored in the chars variable.
  • The vocabulary size is determined by calculating the length of the chars list and stored in the variable vocab_size.
  • Two dictionaries, stoi (string to index) and itos (index to string), are created to map characters to their corresponding indices and vice versa.
  • Encoding and decoding functions, encode and decode, are defined. The encode function converts a string to a list of character indices, and the decode function converts a list of character indices back into a string.

# Create a sorted list of unique characters in the text
chars = sorted(list(set(text)))
vocab_size = len(chars)

# Create character-to-index and index-to-character mappings
stoi = {ch: i for i, ch in enumerate(chars)}
itos = {i: ch for i, ch in enumerate(chars)}
# Functions to encode and decode text
encode = lambda s: [stoi[c] for c in s]
decode = lambda l: "".join([itos[x] for x in l])

encode("hi there")
decode([46, 47, 1, 58, 46, 43, 56, 43])

PyTorch Tensors

PyTorch tensors are used to represent the text data. The text data is encoded as a PyTorch tensor named data.

# Import the PyTorch library
import torch
# Convert the text to a PyTorch tensor of character indices
data = torch.tensor(encode(text), dtype=torch.long)
data.shape, type(data)

# Split the data into training and validation sets
n = int(0.9 * len(data))
train_data = data[:n]
val_data = data[n:]

# Define the block size for context
block_size = 8
# Create training examples (x) and their corresponding targets (y)
x = train_data[:block_size]
y = train_data[1:block_size+1]

x, y

for t in range(block_size):
    context = x[:t+1]
    target = y[t]
    print("ctx ", context, "target", target)

# Import PyTorch neural network modules
import torch.nn as nn
from torch.nn import functional as F

# Set batch size and block size
batch_size = 4
block_size = 8
torch.randint(6, (4,))

# Data loading function to get input (x) and target (y) batches
def get_batch(split):
    # Generate a small batch of data of inputs x and targets y
    data = train_data if split == 'train' else val_data
    ix = torch.randint(len(data) - block_size, (batch_size,))
    x = torch.stack([data[i:i+block_size] for i in ix])
    y = torch.stack([data[i+1:i+block_size+1] for i in ix])
    return x, y

xb, yb = get_batch('train')

for b in range(batch_size):
    for t in range(block_size):
        context = xb[b][:t+1]
        target = yb[b][t]
        print(context, "-----", target)


# Function to create a decay matrix with a specified dimension and gamma values
def get_decay_matrix(dim, gamma):
    d = torch.ones(dim)
    d = torch.tril(d)
    for index, head in enumerate(d):
        g = gamma[index]
        for idx, x in enumerate(torch.tril(head)):
            for idy, y in enumerate(x):
                if idx >= idy:
                    head[idx][idy] = g ** (idx-idy)
    return d

# Install the 'einops' library
!pip install einops

# Import the 'einops' library
import einops
from einops import rearrange, reduce, repeat

Chunkwise Retention

The code defines a class called ChunkwiseRetention. This class appears to be a key component of the RetNet model, responsible for handling chunkwise retention mechanisms.

  • It includes methods for calculating retention matrices.
  • It uses learnable parameters to process input data.
  • The chunkwise retention process involves chunking the input data and calculating retention values.

Gated Multi-Scale Retention

The GatedMultiScaleRetention class is defined, which appears to be another component of the RetNet model.

  • It includes methods for processing input data with a gated multi-scale retention mechanism.
  • It combines the results of chunkwise retention with weighted input data.


The FeedForward class is defined, which is a simple feedforward neural network with GELU activation.

  • It's used to transform the input data within the RetNet blocks.


The Block class is defined, representing a building block of the RetNet model.

  • It contains both the GatedMultiScaleRetention and FeedForward components.
  • The class defines the forward pass, where input data is passed through the components and residual connections are applied.


The RetNet class is defined, which represents the main model.

  • It includes an embedding layer for tokens and positions.
  • Multiple Block instances are stacked together to create the final model.
  • The model includes an output layer with a linear transformation.

class ChunkwiseRetention(nn.Module):
    def __init__(self, chunk_size, num_head, block_size):
        self.key = nn.Linear(n_embed,  chunk_size * num_head, bias = False)
        self.query = nn.Linear(n_embed,  chunk_size * num_head, bias = False)
        self.value = nn.Linear(n_embed,  chunk_size * num_head, bias = False)
        self.gamma = 1.0-2.0**(-5-torch.arange(0,num_head))
        self.decay_mask = get_decay_matrix((num_head, block_size, block_size), self.gamma)
        self.chunk_decay = self.gamma = nn.GroupNorm(1, num_head)
        self.num_head = num_head
        self.chunk_size = chunk_size

    def forward(self, x, past_kv):

        B, T, C = x.shape
        k = self.key(x)
        q = self.query(x)
        v = self.value(x)

        k = rearrange(k, ('b t (h c) -> b h t c'), t=T, h=self.num_head, c =self.chunk_size)
        q = rearrange(q, ('b t (h c) -> b h t c'), t=T, h=self.num_head, c =self.chunk_size)
        v = rearrange(v, ('b t (h c) -> b h t c'), t=T, h=self.num_head, c =self.chunk_size)

        retention = q @ k.transpose(-1, -2)

        # b h t c , b h c t -> b h t t

        retention = retention  * self.decay_mask   # b h t t* h t t
        inner_retention = retention @ v
        past_kv = repeat(past_kv, 'n q v -> B n q v', B=B)
        pb, pn, pq, pv = past_kv.shape

        padding = torch.zeros(pb, pn, pq, self.chunk_size)
        past_kv = past_kv+ padding
        dm = repeat(self.decay_mask, 'h c d -> B h c d', B=B)
        pp = q @ past_kv
        cross_retention = pp.transpose(-1, -2) @ dm
        cross_retention = cross_retention.transpose(-1, -2)
        retention = inner_retention + cross_retention
        current_kv = self.gamma.view(self.num_head, 1, 1) * past_kv + (k.transpose(-1, -2) @ v)
        output =,-2))
        output = rearrange(output, 'b c h t -> b t (c h)')
        return output, current_kv.mean(dim=0)
class GatedMultiScaleRetention(nn.Module):
    def __init__(self, chunk_size, num_head, block_size):
        self.wg = nn.Linear(n_embed,  n_embed, bias = False)
        self.act = nn.SiLU()
        self.y= ChunkwiseRetention(num_head = n_head, chunk_size = n_embed//n_head, block_size=block_size)
        self.wo = nn.Linear(n_embed,  n_embed, bias = False)
        self.past = torch.zeros(num_head, chunk_size, chunk_size)
    def forward(self, x):
        wgx = self.wg(x)
        wgx = self.act(wgx)
        y, past = self.y(wgx, self.past)
        self.past = past.detach()
        y = wgx * y
        return self.wo(y)

class FeedForward(nn.Module):
    def __init__(self, n_embed):
        super().__init__() = nn.Sequential(
            nn.Linear(n_embed, 4* n_embed),
            nn.Linear(4 * n_embed, n_embed),

    def forward(self, x):

class Block(nn.Module):
    def __init__(self, n_embed, n_head, block_size):
        self.sa_head= GatedMultiScaleRetention(num_head = n_head, chunk_size = n_embed//n_head, block_size=block_size)
        self.ffw=  FeedForward(n_embed)
        self.ln1 = nn.LayerNorm(n_embed)
        self.ln2 = nn.LayerNorm(n_embed)

    def forward(self, x):
        x = x + self.sa_head(self.ln1(x))
        x = x+self.ffw(self.ln2(x))
        return x
class RetNet(nn.Module):
    def __init__(self, block_size):

        self.token_embedding_table = nn.Embedding(vocab_size, n_embed)
        self.position_embedding_table = nn.Embedding(block_size, n_embed)
        self.blocks = nn.Sequential(*[Block(n_embed, n_head=n_head, block_size=block_size) for _ in range(n_layer)])
        self.lm_head = nn.Linear(n_embed, vocab_size)

    def forward(self, idx, targets=None):
        B, T = idx.shape
        token_emb = self.token_embedding_table(idx)
        pos_emb = self.position_embedding_table(torch.arange(T))
        x = token_emb + pos_emb
        x = self.blocks(x)
        logits = self.lm_head(x)
        if targets == None:
            loss = None
            B, T, C = logits.shape
            logits = logits.view(B*T, C)
            targets = targets.view(B*T)
            loss = F.cross_entropy(logits, targets)
        return logits, loss

    def generate(self, idx, max_new_tokes):
        for _ in range(max_new_tokes):
            b, s = idx.shape
            bk = min(s, block_size)
            idx_cond =, block_size-bk, dtype=int), idx), dim=1)[:, -block_size:]
            logits, loss = self(idx_cond)
            logits = logits[:, -1, :]
            probs = F.softmax(logits, dim = -1)
            idx_next = torch.multinomial(probs, num_samples = 1)
            idx =, idx_next), dim = 1)
        return idx


Various hyperparameters are defined, including batch_size, block_size, max_iters, learning_rate, and more.

# Hyperparameters
batch_size = 16
block_size = 32
max_iters = 5000
eval_interval = 100
learning_rate = 1e-3
device = 'cuda' if torch.cuda.is_available() else 'cpu'
eval_iters = 200
n_head = 4
n_layer = 4
dropout = 0.0
n_embed = 32

Data Loading (Again)

A function get_batch() is defined to generate batches of data for training and validation.

# Data loading function to get input (x) and target (y) batches
def get_batch(split, batch_size):
    data = train_data if split == 'train' else val_data
    ix = torch.randint(len(data) - block_size, (batch_size,))
    x = torch.stack([data[i:i+block_size] for i in ix])
    y = torch.stack([data[i+1:i+block_size+1] for i in ix])
    return x, y

Model Initialization

An instance of the RetNet model is created, along with an optimizer.

# Initialize the RetNet model
model = RetNet(block_size=block_size)
# Get a batch of training data
xb, yb = get_batch('train', batch_size=batch_size)
# Initialize the optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
# Forward pass and loss calculation
logits, loss = model(xb, yb)

# Function to estimate loss on train and validation sets
def estimate_loss():
    out = {}
    for split in ['train', 'val']:
        losses = torch.zeros(eval_iters)
        for k in range(eval_iters):
            X, Y = get_batch(split, batch_size=batch_size)
            logits, loss = model(X, Y)
            losses[k] = loss.item()
        out[split] = losses.mean()
    return out

# Get a batch of training data
xb, yb = get_batch('train', batch_size=batch_size)

# Forward pass and loss calculation
logits, loss = model(xb, yb)

Training Loop

The code enters a training loop where it repeatedly performs the following steps:

  • Estimates the loss for both training and validation data.
  • Samples a batch of training data.
  • Computes the loss and performs backpropagation to update the model's parameters.

# Training loop
for iter in range(max_iters):
    # Every once in a while, evaluate the loss on train and val sets
    if iter % 100 == 0 or iter == max_iters - 1:
        losses = estimate_loss()
        print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")

    # Sample a batch of data
    xb, yb = get_batch('train', batch_size=batch_size)

    # Forward pass, loss calculation, backpropagation, and optimization
    logits, loss = model(xb, yb)

Text Generation

Finally, the code demonstrates text generation using the trained model. It provides a starting context and generates text iteratively.

# Create a context for text generation
context = torch.zeros((1, 1), dtype=torch.long, device=device)
# Generate text using the model
print(decode(model.generate(context, max_new_tokes=200)[0].tolist()))

# Create another context for text generation
context = torch.zeros((1, 1), dtype=torch.long, device=device)
# Generate more text using the model
print(decode(model.generate(context, max_new_tokes=200)[0].tolist()))


You may also perform tokenization using the tiktoken library to tokenize text into tokens for the same guide.

# Install the 'tiktoken' library
!pip install tiktoken

# Import the 'tiktoken' library
import tiktoken
# Get the encoding for a specific model
enc = tiktoken.get_encoding("cl100k_base")

# Assert that encoding and decoding work correctly
assert enc.decode(enc.encode("hello world")) == "hello world"

# To get the tokeniser corresponding to a specific model in the OpenAI API:
enc = tiktoken.encoding_for_model("gpt-4")

# Assert that encoding and decoding work correctly for the new model
assert enc.decode(enc.encode("hello world")) == "hello world"

# Encode "hello world" using the tokeniser
enc.encode("hello world")

# Count the number of tokens in the text
text_tokens = enc.encode(text)

# Get the size of the vocabulary

# Decode the first token in the text

data = torch.tensor(text_tokens, dtype = torch.long)

learning_rate = 3e-4

len(text.split(' '))

chars = sorted(list(set(text.split(' '))))
vocab_size = len(chars)


# Create word-to-index and index-to-word mappings
stoi = {ch: i for i, ch in enumerate(chars)}
itos = {i: ch for i, ch in enumerate(chars)}

# Functions to encode and decode words
encode = lambda s: [stoi[c] for c in s]
decode = lambda l: " ".join([itos[x] for x in l])
# Encode words using the mappings
data = torch.tensor(encode(text.split(' ')), dtype = torch.long)
# Display the first 10 tokens in the data

# Decode the first 10 tokens in the data
decode(encode(text.split(' ')[:10]))

text.split(' ')[:10]

# Split the data into training and validation sets
n = int(0.9 * len(data))
train_data = data[:n]
val_data = data[n:]

# Set hyperparameters for the model
batch_size = 16
block_size = 32

max_iters = 5000
eval_interval = 100
learning_rate = 1e-3
device = 'cuda' if torch.cuda.is_available() else 'cpu'
eval_iters = 200
n_embd = 32
n_embed = 32
n_head = 4
n_layer = 4
dropout = 0.0

# Initialize the RetNet model
model = RetNet(block_size=block_size)
# Get a batch of training data
xb, yb = get_batch('train', batch_size=batch_size)
# Initialize the optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
# Forward pass and loss calculation
logits, loss = model(xb, yb)

# Training loop
for iter in range(max_iters):
    # Every once in a while, evaluate the loss on train and val sets
    if iter % eval_interval == 0 or iter == max_iters - 1:
        losses = estimate_loss()
        print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")

    # Sample a batch of data
    xb, yb = get_batch('train', batch_size=batch_size)

    # Forward pass, loss calculation, backpropagation, and optimization
    logits, loss = model(xb, yb)

# Generate text with different initial contexts
context1 = torch.tensor([encode("thou art kneel before king".split(' '))], dtype=torch.long)
context2 = torch.tensor([encode("Hermione".split(' '))], dtype=torch.long)
context3 = torch.tensor([encode("come".split(' '))], dtype=torch.long)

# Print generated text using different contexts
print(decode(model.generate(context1, max_new_tokes=200)[0].tolist()))
print(decode(model.generate(context2, max_new_tokes=200)[0].tolist()))
print(decode(model.generate(context3, max_new_tokes=200)[0].tolist()))


The provided code is a comprehensive example of training a custom neural network for text generation using PyTorch. It includes components for retention mechanisms and is trained on a given text dataset. It showcases the training process and demonstrates text generation. You can use this code as a basis for training your own custom language models for various text generation tasks.

Looking into the Future

The introduction of RetNet marks a significant milestone in the realm of neural network architecture. Its theoretical foundations, retention mechanism, and unique representations offer a glimpse into the future of deep learning. What are the potential implications and applications of RetNet's innovative features?

  1. Improved Language Models: RetNet's approach to sequence modeling may pave the way for more capable and efficient language models. This could lead to advancements in machine translation, text generation, and chatbots, ultimately enhancing the way we interact with AI-driven systems.
  2. Efficiency Gains: The low inference cost and effective handling of long sequences may revolutionize real-time, resource-efficient natural language processing applications. This is particularly relevant for voice assistants, autonomous vehicles, and automated customer support systems.
  3. Broader Applicability: The theoretical connection between recurrence and attention mechanisms might inspire further research into novel neural network architectures. These findings could extend beyond language modeling and transform other fields, such as computer vision or reinforcement learning.
  4. Hardware Optimization: RetNet's efficient use of GPU resources and reduced memory requirements may drive the development of more efficient hardware tailored for deep learning tasks. This, in turn, can expedite model training and inference in various domains.
  5. Scaling Possibilities: The favorable scaling results of RetNet suggest that building larger and more sophisticated language models may become more accessible. This has the potential to advance natural language understanding and generation, leading to more accurate and context-aware AI systems.



In conclusion, RetNet is not just another neural network architecture; it is a leap forward in how we approach sequence modeling and language understanding. Its theoretical foundations, versatile retention mechanism, and efficient representations offer a tantalizing glimpse into the future of deep learning. As we continue to unlock the potential of RetNet, we may witness the rise of more powerful and efficient AI systems that will redefine our interaction with technology and open up new horizons for AI-driven applications..

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October 18, 2022

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

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This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

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This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

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This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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