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Claude E. Shannon's book, "The Mathematical Theory of Communication", is very accessible. The main points about how entropy is defined and derived along with the "Fundamental Theorem for a Discrete Channel With Noise" is digested below.
Entropy can be defined as the number of bits it takes to describe a system.
Given $n$ symbols, each occurring with probability $p_k$ for $k \in (0, 1, \dots, n1)$, we ask how many configurations are there for a very long message, say of $T$ transmitted symbols.
For the sake of clarity, we assume $T$ large and $T \cdot p_k \cdot n$ is integral.
The number of ways to arrange $T \cdot n$ elements comprised of $n$ symbols each occurring with $T \cdot p_k \cdot n$ frequency is the multinomial:
$$ { T \cdot n \choose (T \cdot p_0 \cdot n), (T \cdot p_1 \cdot n), \dots, (T \cdot p_{n1} \cdot n) } $$ $$ = \frac{(T \cdot n)!}{\prod_{k=0}^{n1} (T \cdot p_k \cdot n)!} $$
If we concern ourselves with the bits it takes to represent the total number of configurations, we find (where $\lg(\cdot) = \log_2(\cdot)$):
$$ \lg( \frac{(T \cdot n)!}{\prod_{k=0}^{n1} (T \cdot p_k \cdot n)!} ) $$ $$ = \lg( (T \cdot n)! )  \sum_{k=0}^{n1} \lg( (T \cdot p_k \cdot n)! ) $$
$$ \approx (T \cdot n) lg( T \cdot n )  (T \cdot n)  \sum_{k=0}^{n1} [ (T \cdot p_k \cdot n) \lg(T \cdot p_k \cdot n)  (T \cdot p_k \cdot n) ] $$
By definition, $\sum_{k=0}^{n1} p_k = 1$, we can reduce to find:
$$ =  T \sum_{k=0}^{n1} p_k \lg(p_k) $$
We define $H$ to be our entropy, the average number of bits needed to represent our system. Since the above is the total number of bits needed, we divide by $T$ to find the average:
$$ H =  \sum_{k=0}^{n1} p_k \lg(p_k) $$
If we transmit $H$ bits per symbol over a noisy line and assume each symbol's error over the line is independent, label the number of bits, whole or partial, that succumb to error as $r$. That is, of the $H$ bits per symbol, $r$ are 'eaten' by noise in the channel.
Call the channel capacity $C = H  r$. This is the number of useful bits that remain after we take away the noise from the number of bits needed to encode symbols.
As above, consider a long message of $T$ transmitted symbols. First allocate some bits for error correction and choose $S$ such that:
$$ S < C = H  r $$
Further
$$ S = C  \eta = H  r  \eta $$
Where the number of error correcting bits is just shy of $T \cdot \eta$. Choose codewords in the source representation so that there are $2^{T \cdot S}$ codewords that sit in $2^{T \cdot H}$ total configurations.
Sent messages will be from the restricted set of codewords and has probability:
$$ \frac{2^{T \cdot S}}{2^{T \cdot H}} = 2^{T \cdot (S  H)} $$
A received message of $T \cdot H$ bits long will have $T \cdot r$ corrupted by error. The number of possible source configurations that could have sent the received message is:
$$ 2^{T \cdot r} $$
The probability that there is another codeword in the $2^{T \cdot r}$ number of theoretical sent messages, aside from the source codeword, is the probability that none of the other codewords are hit:
$$ [ 1  2^{ T \cdot (S  H) } ]^{ 2^{T \cdot r} } $$ $$ = [ 1  \frac{2^{ T \cdot \eta}}{2^{T \cdot r}} ]^{2^{T \cdot r}} $$
As $T$ becomes large:
$$ \approx e^{ 2^{ T \cdot \eta } } $$ $$ = 1  2^{ T \cdot \eta } + O( 2^{2 \cdot T \cdot \eta} ) $$ $$ \approx 1  2^{ T \cdot \eta } $$
Which approaches 0.
So the chance of our transmitted encoded codeword being mistaken for another codeword is vanishingly small. As long as we choose $S$ to be less than the channel capacity $C$ and the message is long enough ($T$ is big enough) we have a low chance of a source codeword colliding after transmission with a channel error rate of $r$.